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The discrimination of the direction of movement of sensory images is critical to the control of many animal behaviors . We propose a parsimonious model of motion processing that generates direction selective responses using short-term synaptic depression and can reproduce salient features of direction selectivity found in a population of neurons in the midbrain of the weakly electric fish Eigenmannia virescens . The model achieves direction selectivity with an elementary Reichardt motion detector: information from spatially separated receptive fields converges onto a neuron via dynamically different pathways . In the model , these differences arise from convergence of information through distinct synapses that either exhibit or do not exhibit short-term synaptic depression—short-term depression produces phase-advances relative to nondepressing synapses . Short-term depression is modeled using two state-variables , a fast process with a time constant on the order of tens to hundreds of milliseconds , and a slow process with a time constant on the order of seconds to tens of seconds . These processes correspond to naturally occurring time constants observed at synapses that exhibit short-term depression . Inclusion of the fast process is sufficient for the generation of temporal disparities that are necessary for direction selectivity in the elementary Reichardt circuit . The addition of the slow process can enhance direction selectivity over time for stimuli that are sustained for periods of seconds or more . Transient ( i . e . , short-duration ) stimuli do not evoke the slow process and therefore do not elicit enhanced direction selectivity . The addition of a sustained global , synchronous oscillation in the gamma frequency range can , however , drive the slow process and enhance direction selectivity to transient stimuli . This enhancement effect does not , however , occur for all combinations of model parameters . The ratio of depressing and nondepressing synapses determines the effects of the addition of the global synchronous oscillation on direction selectivity . These ingredients , short-term depression , spatial convergence , and gamma-band oscillations , are ubiquitous in sensory systems and may be used in Reichardt-style circuits for the generation and enhancement of a variety of biologically relevant spatiotemporal computations .
Control of animal behavior often requires the discrimination of the direction of movement of sensory images . In some behaviors , including tracking [1–5] and postural balance [6–9] , animals must determine the direction of sensory slip to generate appropriate compensatory movements to stabilize the sensory image on the receptor array . For other behaviors , including prey capture [10 , 11] , animals must respond to the direction of motion of prey relative to the sensory background . A neural correlate of these functions was described over 40 years ago: Hubel and Wiesel characterized central neurons in the mammalian visual system that exhibited preferential responses to particular directions of movement of sensory images [12–14] . Following this discovery , direction-selective response properties have been found in a diversity of animal species and across different sensory modalities , from the visual cortex of cats [12] and somatosensory cortex of monkeys [15] to the electrosensory midbrain of weakly electric fish [16 , 17] . Of particular interest are the direction selective responses of midbrain neurons observed in a species of weakly electric fish , Eigenmannia virescens . These neurons exhibit an unexpected enhancement of direction selectivity by the addition of concomitant naturally occurring sensory oscillations in the gamma frequency range [17] . These oscillations strongly induce short-term synaptic depression in these midbrain neurons , and measures of this depression correlate significantly with direction selectivity [17–19] . These correlations suggest that a depression-based mechanism might underlie both the generation of direction selectivity and its enhancement by the addition of gamma-band oscillations . Here we propose a parsimonious model to describe and explore the essential features of this mechanism . The model is based on a conceptual framework for motion processing known as Reichardt detectors: information from two spatially separated channels with asymmetric temporal properties combine via a nonlinear operation on a downstream ( post-synaptic ) neuron to produce direction selective responses [20 , 21] ( Figure 1 ) . When supplied with a stimulus moving in the preferred direction , the temporal shift can compensate for the spatial separation , allowing the inputs from the two channels to interact constructively . The temporal phase shift is typically modeled as a pure delay or a low-pass filter . For direction-selective neurons in V1 , Chance et al . [22] propose that the dynamical differences between synapses that exhibit short-term synaptic depression and those that do not may provide a mechanism for generating both the asymmetrical temporal properties and the nonlinear operation required by an elementary Reichardt circuit ( the electrosensory midbrain of Eigenmannia also exhibit these requisite ingredients for Reichardt-style selectivity based on short-term depression [19 , 23] . ) For a depressing synapse , the magnitude of the response in the post-synaptic cell decreases during repetitive activation [24–27] . Short-term synaptic depression involves two dynamic processes with distinct time constants: the faster process , with a time-constant on the order of tens to hundreds of milliseconds , can be attributed to the depletion of the supply of readily releasable synaptic vesicles , while the slower process , with a time-constant of seconds to tens of seconds , can be attributed to the mechanisms for the replenishment of this supply [25 , 26] . Here we propose a parsimonious model that describes a mechanistic linkage between short-term synaptic depression and direction selectivity , based on a Reichardt-style circuit . We further test the possibility that the enhancement of direction selectivity by concomitant gamma-band oscillations may be mediated by short-term synaptic depression . Global synchronous oscillations in activity may arise endogenously , as occurs in cortical and other circuits [28] , or exogenously , as occurs in weakly electric fish from the interaction of the electric fields of nearby conspecifics [29] and the jamming avoidance response [30] . In the model , these oscillations induce depression , which can lead to an enhancement of direction selectivity to moving objects . We systematically explore the effects of variations of biologically relevant parameters of the model and evaluate the results in relation to electrophysiological data from a population of motion-sensitive electrosensory neurons in the midbrain of weakly electric fish [17] .
We have found that the one-state model , which includes only the fast process of short-term synaptic depression , exhibits direction selectivity ( Figure 2A ) . Since short-term synaptic depression creates a phase advance in the synapse , a moving stimulus that first passes through the nondepressing area leads to a simultaneous arrival ( a constructive combination ) of signals from both synapses ( Figure 2A , blue ) . Movement in the opposite direction leads to asynchronous arrival of information ( Figure 2A , red ) . These results are similar to those reported previously [22] . The response to the sine-wave grating is nearly identical from cycle to cycle over time . In this case , the time constant of depression is fast relative to the period of the stimulation so that the depressing synapse has sufficient time to return to its initial state during the dark phase of each cycle . In the two-state model , which includes both the fast and slow processes associated with short-term synaptic depression , neurons exhibit direction selectivity that enhances from cycle to cycle of a sustained sine wave grating ( Figure 2B ) . In the first cycle , the response is nearly identical to the response of the one-state model . However , in each subsequent cycle there is a total reduction in the probability of firing and in the total number of spikes for both directions of motion . This reduction in probability of firing is asymptotic . This overall reduction in firing nonetheless increases the direction index ( reported in the caption to Figure 2 and defined in Model ) by increasing the relative difference between the responses to the preferred and non-preferred directions of movement . Intracellularly , this enhancement effect will occur as long as the stimulus is maintained even if the depression limits the PSPs so that they do not reach the spiking threshold . In extracellular recordings , however , there is a possibility that the depression could lead to a complete elimination of spiking responses to the moving stimulus . The sine-wave gratings that we used are sustained stimuli—such stimuli that might arise during image-stabilization tasks . In contrast , many behaviors , such as prey capture , involve spatiotemporally localized , or intermittent stimuli . We have examined the performance of the model to this class of stimuli . Our intermittent stimulus consists of the temporal sequence defined by Equation 2 , which is a 1 . 5 cycle sine-wave pulse . Prior to the arrival of the stimulus , we initialized the system with at least 3 seconds of a spatially homogeneous stimulus of intermediate intensity , which we call 50% grey ( see Model ) . At the arrival of the pulse , the model lies in approximately the same state as it does for the first cycle of the sine grating . As a result , the responses to the first cycle of the grating and to the intermittent stimulus are nearly identical ( compare Figures 2B and 3A ) . For the same parameter values , the responses differ only because the stimuli are subtly different: the sine grating stimulus appears in both receptive fields at the same time , but at different phases , whereas the 1 . 5 cycle pulse first appears in one receptive field then moves to the other and disappears . We also tested the model's response to an intermittent stimulus that was initialized not with a uniform background but rather with global synchronous gamma-band oscillations . These sorts of oscillations occur exogenously in groups of weakly electric fish [29] and endogenously in many CNS circuits [28] . In the model , the gamma-band oscillations drive activity simultaneously in both afferents which activates both the fast ( 0 < D ( t ) ≪ 1 ) and slow ( 0 < S ( t ) ≪ 1 ) processes in the depressing synapse ( see Model ) . The response of the model to the moving pulse after 3 seconds of global stimulation compares to its asymptotic response to a persistent sine grating ( compare Figures 2B and 3B ) . The response in this condition is more “sparse” than in the grey-initialized condition—the responses are reduced due to the activation of the slow process associated with short-term synaptic depression . The code is more sparse in that fewer spikes more reliably encode information—the direction of movement . Depending on the values of the parameters , this reduction in spiking can lead to an enhancement of direction selectivity ( Figure 3A versus 3B ) or a reduction of the direction selectivity ( Figure 3C versus 3D ) . We varied the contributions of the depressing and nondepressing synapses in the model and measured the response to the moving pulse in both the grey initialized and gamma-band initialized conditions ( Figure 4A and 4B ) . Both plots show that direction selectivity reaches a maximum along a ray from the origin corresponding to an optimal ratio of depressing to nondepressing synapses . To determine under which conditions the gamma-band initialization will lead to an enhancement of direction selectivity , we subtracted the surfaces in Figure 4A and 4B . The maximum enhancement was found to occur along a ray in which the depressing synapses make a greater contribution than the nondepressing synapses ( Figure 4C , magenta ) . In addition , we found a region in which the combinations of depressing and nondepressing synapses lead to a reduction in direction selectivity with the addition of the gamma-band oscillations ( Figure 4C , green ) . In intracellular recordings of midbrain neurons in Eigenmannia , the addition of an exogenous gamma-band oscillation resulted in an enhancement of direction selectivity to a moving bar stimulus [17] . In many neurons a correlate of the activity of inputs that experience short-term synaptic depression was observed: the amplitude of post-synaptic potentials ( PSPs ) declined on a cycle-by-cycle basis to a sustained gamma-band oscillation [17] ( Figure 5 ) . This “PSP depression” has been shown likely to result from short-term synaptic depression and not other mechanisms [18 , 19] . We tested the model with identical stimuli and made an identical measurement of “PSP depression” [18] . PSP depression is the magnitude of the decline in amplitude of PSPs measured at or near the soma , and is therefore a sum of the synaptic activity , including both depressing and nondepressing synaptic inputs , to the neuron ( Figure 5 ) . In the model , PSP depression was strongest where the ratio of depressing synapses to nondepressing synapses was high , but the total number of synapses was low ( Figure 5 ) . Surprisingly , adding more depressing synapses actually decreased the measure of short-term synaptic depression . This measure consisted of a ratio of the response ( maximum depolarization above resting potential ) to the first cycle of global synchronous stimulation to the average responses to later cycles , after the transient had decayed . As more depressing synapses were added , both the numerator and the denominator of this ratio increased , but they did so in a way such that the value of this ratio decreased . In Eigenmannia , strong correlations were observed between the magnitude of PSP depression measured in each neuron and the magnitude of direction selectivity to the moving bar in both grey and gamma-band initialized conditions ( Figure 6A ) . We tested whether any simple set of parameters in the model could reproduce these relations . We considered four hypothetical distributions , asking if each reproduced the qualitative observations drawn from the sample of neurons within the electrosensory midbrain of Eigenmannia virescens [17] . The qualitative observations were that the measures of PSP depression and direction selectivity were positively correlated in both grey and gamma-band initialized conditions , and that direction selectivity was increased by the addition of the gamma-band oscillation ( Figure 6A ) . We tested hypothesized distributions supported on one-dimensional restrictions of the parameter space in which 14 of the 16 model parameters remained fixed and the other two varied . The two parameters we varied determined the contributions of the depressing and nondepressing synapses . For convenience , we describe our parameter space restrictions in terms of numbers of synapses ( with fixed synaptic weights , see Model ) . Distribution 1 assumes that the total number of synapses remains constant ( 80 ) , but the ratio of depressing to nondepressing synapses varies . Distribution 2 assumes the number of depressing synapses remains constant ( 80 ) but the number of nondepressing synapses varies . Distribution 3 assumes the ratio of depressing to nondepressing synapses remains constant ( 5/3 ) but the total number varies . Finally , distribution 4 assumes the number of nondepressing synapses remains constant ( 12 ) but the number of depressing synapses varies . The restrictions of parameter space associated with these distributions are plotted in Figure 6B . The resulting relationships between the measures of PSP depression and direction selectivity for each distribution are plotted in Figure 6C–6F . Distributions 6E and 6F match the three qualitative features seen in the population of neurons observed in the midbrain of Eigenmannia .
In our model , the activation of the slower process of short-term depression is quantified by the state variable S ( t ) that depends upon the stimulus history . If there has been little recent stimulation ( recent with respect to the time constant , which in this model is set to 3 seconds ) , then the neuron resides in a state in which it responds vigorously to stimuli that are moving in any direction . On the other hand , if there has been recent stimulation , the depressing synapses will be depressed , and as a result the neuron will respond less vigorously . As shown in Figure 4C , the ratio of the contributions of depressing and non-depressing synapses determines whether the neuron will be more or less directionally selective in the depressed state that results from recent stimulation . In a population of midbrain neurons in Eigenmannia , recent stimulation leads to an enhancement of direction selectivity [17] . Recent stimulation shifts depressing synapses from a highly responsive state to a more depressed state . The difference between these two states may correspond to vigilance and focus ( at least in Eigenmannia ) and can be seen as a form of attention . In this way , the current value of the slow variable S ( t ) corresponds to an attentional state associated with the neuron . Indeed , the state of the slow process could be critical to specific computations . Although we only explored the responses to moving stimuli , these results could be applied more broadly to any computation in the brain that involves the temporal comparison of information that converges from independent pathways . Indeed , we have shown that any stimulus that activates the slow process can lead to a shift in the computational properties of elementary Reichardt circuits . As a result , any change in the activity patterns , whether they be stimulus-driven or endogenous , could affect neural computations through similar mechanisms . This feature may provide an opportunity for animals to use behavior to modulate computations in the brain . Animals may modulate the state of their synapses and hence the degree of direction selectivity in central neurons using behavior . This form of behavioral modulation requires that: 1 ) the behavior generates patterns of activity that elicit short-term depression in downstream neurons and 2 ) that these patterns of activity do not interfere with the motion processing . Evidence for the behavioral modulation of direction-selectivity is seen in the Jamming Avoidance Response ( JAR ) of weakly electric fish . In the JAR , the electric fields of fish that are within about a meter of each other interact to produce oscillations in electroreceptor activity across the entire body [30] . In this way , the stimulation leads to global , synchronous activity across the receptor array . In the wild , the frequencies of these oscillations are most commonly in the gamma frequency band , from 20 to 80 Hz [29] . Laboratory experiments have shown that lower frequency oscillations , below approximately 8 Hz , impair the perception of moving objects [31–33] . These gamma oscillations are encoded by electroreceptors and propagate through the ascending electrosensory system . In the midbrain , these oscillations match the stimulation frequencies that best elicit short-term depression [18 , 19] . As a result , the ongoing oscillations that occur in social situations dramatically modulate short-term depression , leading to an enhancement of direction selectivity to intermittent stimuli [17] . Another more general example of behavioral modulation of temporal processing in plasticity-based elementary Reichardt circuits could include movement-induced self-stimulation of sensory receptors . For example , were an electric fish to remain motionless in a tube , the slow process will be in its initial state for midbrain electrosensory neurons , whereas if the fish were to move back and forth within the tube , the slow process may be activated . The neurons would be more responsive and less selective while the animal remained motionless and would be less responsive but more selective when the animal was moving relative to nearby objects . If behavior can be used to generate patterns of brain activity that change the state of depressing synapses to alter spatiotemporal computations in the brain , then intrinsic activity in brain circuits could possibly have the same effect . The intrinsic activity would have to have two properties: 1 ) the patterns of activity must elicit short-term depression in downstream neurons and 2 ) these patterns of activity must not interfere with the spatiotemporal computation . There are numerous examples of endogenous oscillations that occur at all levels of the CNS [28] . If the output of these endogenous synchronous oscillations converge on neurons that perform spatiotemporal computations , such as motion processing , through synapses that experience short-term synaptic depression , then the endogenous oscillations could have the same effects on computation that have been reported in Eigenmannia . The correlation between attentional processes and the emergence of gamma-band oscillations in cortical and other circuits may support this idea . Perhaps the gamma-band synchronous activity shifts elementary Reichardt circuits from a more responsive but less selective state to a less responsive but more selective state . The population of neurons observed in the midbrain of Eigenmannia showed a positive correlation between a measure of short-term synaptic depression ( PSP depression ) and direction selectivity . We tested the hypothesis that this relation follows from the proposed elementary Reichardt circuit that uses short-term synaptic depression . The model , however , clearly demonstrated that this relation is but one possible outcome . Indeed , without assumptions constraining the distribution of parameter values , the model does not make any specific prediction about the relationship between PSP depression and direction selectivity in populations of neurons . Thus , the relationship previously observed in the midbrain of weakly electric fish [17] is likely associated with functional constraints beyond the elementary Reichardt circuit . These functional constraints may be related to the control of specific behaviors . For example , in tracking behavior [4 , 5 , 34] , fish make compensatory movements to stabilize an image on the sensory array . Future studies will determine , via neural system identification , how a population of direction-selective neurons may encode the sensorimotor transfer function inferred from behavioral performance [5] . A key feature of the model is the convergence of information from spatially separated locations on the receptor array . The receptive field of a neuron that uses the mechanisms associated with this model should be composed of regions that differ in relation to short-term synaptic plasticity: the response of the neuron to stationary , highly localized stimuli at different locations should show differences in measures of short-term depression . In the simplest case , the receptive field has two regions , e . g . , caudal and rostral . If the neuron exhibits little or no short-term depression when the stimulus is in the caudal region and strong depression to local stimulation in the rostral region , then the model predicts that the neuron will respond more strongly to caudal-to-rostral movement than to rostral-to-caudal movement [23] . Preliminary evidence obtained from midbrain neurons in weakly electric fish are consistent with this hypothesis: gamma-band stimulation in subregions of the receptive fields of midbrain neurons elicit different levels of short-term synaptic depression ( personal observations ) . Nevertheless , one could envision far more complicated receptive field structure leading to direction selectivity using similar mechanisms . By posing a parsimonious model describing the transformation of the spatiotemporal stimulus into the neural response , we have taken the first step in a rigorous identification of the underlying neural system . The steps remaining include ( for each neuron ) validating or falsifying the model , estimating its parameters and comparing our model to alternative models . The field of system identification offers systematic and rigorous approaches to these remaining problems . For example , the stochastic model that includes action potential timing can be used to determine the likelihood that an experimentally observed train of action potentials was generated using mechanisms captured by the model . Systematic exploration of the parameters can be made to achieve the maximum likelihood estimates [35 , 36] . This procedure can be repeated for each neuron studied in the population to get more rigorous estimates of the distribution of parameter values .
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Short-term synaptic plasticity is ubiquitous in brain circuits , but its function in sensorimotor processing remains unclear . We propose a parsimonious model of motion processing using short-term depression to produce directionally selective responses . In the model circuit , information from two spatially separated receptive fields is combined after being asymmetrically processed by synapses that either exhibit short-term synaptic depression or do not . Motion in a preferred direction leads to a constructive interaction between the two channels; motion in the opposite direction does not . The model represents short-term synaptic depression as two processes with distinct time constants . The faster process alone suffices to generate direction selectivity in the circuit . The slow process , in contrast , can enhance direction selectivity to sustained stimuli . Therefore , the slow process mediates a form of attentional shift from alert , where the neuron responds more vigorously , to discriminating , where the neuron responds more selectively with fewer spikes . This explains a previously observed enhancement of direction selectivity in weakly electric fish in the presence of global synchronous gamma-band oscillations . These findings suggest a mechanistic connection between gamma-band oscillations and attention .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion"
] |
[
"vertebrates",
"teleost",
"fishes",
"neuroscience",
"computational",
"biology"
] |
2008
|
Synaptic Plasticity Can Produce and Enhance Direction Selectivity
|
Deletion of tumor suppressor genes in stromal fibroblasts induces epithelial cancer development , suggesting an important role of stroma in epithelial homoeostasis . However , the underlying mechanisms remain to be elucidated . Here we report that deletion of the gene encoding TGFβ receptor 2 ( Tgfbr2 ) in the stromal fibroblasts ( Tgfbr2fspKO ) induces inflammation and significant DNA damage in the neighboring epithelia of the forestomach . This results in loss or down-regulation of cyclin-dependent kinase inhibitors p15 , p16 , and p21 , which contribute to the development of invasive squamous cell carcinoma ( SCC ) . Anti-inflammation treatment restored p21 expression , delayed tumorigenesis , and increased survival of Tgfbr2fspKO mice . Our data demonstrate for the first time that inflammation is a critical player in the epigenetic silencing of p21 in tumor progression . Examination of human esophageal SCC showed a down-regulation of TGFβ receptor 2 ( TβRII ) in the stromal fibroblasts , as well as increased inflammation , DNA damage , and loss or decreased p15/p16 expression . Our study suggests anti-inflammation may be a new therapeutic option in treating human SCCs with down-regulation of TβRII in the stroma .
Mounting evidence supports a cross talk between epithelial and stromal cells in cancer progression mediated by paracrine signals and extracellular matrix components [1] , [2] . For example , in a prostate cancer model , epithelial tumor progression induces loss of p53 function in stromal fibroblasts [3] . Conversely , in a breast tumor model , deletion of Pten in stromal fibroblasts promoted tumor progression associated with massive extracellular matrix ( ECM ) remodeling , immune cell infiltration , increased angiogenesis , and increased recruitment of the Ets2 transcription factor to targeted gene regulation [4] . Likewise , conditional expression of a mutant allele of APC gene in murine uterine stromal cells resulted in endometrial gland hyperplasia progressing to endometrial carcinoma in situ and invasive endometrial adenocarcinoma [5] . Furthermore , deletion of Notch1 in epidermal keratinocytes has significant impact on the stromal microenvironment , promoting skin carcinogenesis [6] . This crosstalk has been witnessed in human sporadic breast cancers: somatic TP53 mutations in stroma but not epithelia were associated with regional nodal metastases [7] . These studies suggest that epithelial and stromal cell signaling influence one another and may co-evolve during the course of tumor progression [2] . The autocrine and paracrine actions of transforming growth factor-β ( TGF-β ) have been well documented in stromal and tumor cell interaction [8] , [9] . Deletion of Tgfbr2 in a variety of epithelial cells results in a more aggressive tumor phenotype in mammary , pancreatic , colon , intestinal , head and neck , anal and genital tumors ( reviewed by Yang [9] ) . The mechanism underlying this observation involves increased infiltration of immune cells in the tumor microenvironment [8]–[11] . Interestingly , conditional knockout of the Tgfbr2 gene in a subset of stromal fibroblasts ( FSP1+ cells ) contributes to the transformation of epithelia and results in invasive squamous cell carcinoma ( SCC ) in mouse forestomach [1] . The deletion of Smad4 , a downstream mediator of TGF-β signaling in T cells , results in spontaneous epithelial cancers throughout the gastrointestinal tract in mice [12] . Unexpectedly , epithelial-specific deletion of Smad4 did not result in the same tumor phenotype [12] . The impact of stromal TGF-β on epithelial cancers was also demonstrated in a tissue recombination model wherein loss of TβRII function in 50% of immortalized human prostate fibroblasts resulted in malignant transformation of the nontumorigenic human prostate epithelial cells [13] . These studies suggest that stromal loss of TGF-β signaling induces epithelial transformation . One of the mechanisms delineated in these studies involves hepatocyte growth factor ( HGF ) overproduction by Tgfbr2fspKO stroma and activation of c-MET signaling on adjacent epithelia through paracrine signaling , resulting in epithelial hyperproliferation [1] , [14] . However , it is unclear whether changes in stromal cells induce genetic and epigenetic alterations in the epithelial compartment , and if so , what are the underlying molecular mechanisms ? Here we report that stromal deletion of Tgfbr2 induced inflammation resulted in DNA damage , loss of p15 and p16 , promoter methylation of p21 , and increased epithelial proliferation , i . e . the development of SCC . We showed for the first time that down-regulation of TGF-β signaling in the stroma has significant impact on the genetic and epigenetic components of the adjacent epithelial compartment through inflammation mediated mechanisms . Therefore , therapeutic targeting of inflammation may be a useful strategy in treating human SCCs with down-regulation of TβRII in the stroma .
Stromal cells and their signaling pathways have significant impact on epithelial tumor progression [4] , [12] , [13] . Specific deletion of Tgfbr2 in FSP1+ fibroblasts ( Tgfbr2fspKO ) induced development of SCC in forestomach with 100% penetrance [1] ( Figure 1A , left panel ) . These mice die by 7 weeks with a median survival of 38 days ( Log rank p<0 . 001 ) ( Figure 1A , right panel ) . Examination of Tgfbr2fspKO forestomach between embryonic day 16 ( E16 ) and 5 weeks of age suggested that hyperplasia began during week 3 and was followed by dysplasia , carcinoma in situ , and invasive SCC ( Figure S1A ) . Here we investigated the molecular mechanisms that are responsible for the development of SCC due to loss of Tgfbr2 in the stromal compartment . We first confirmed the specific deletion of Tgfbr2 in stromal fibroblasts using TβRII immunofluorescence ( Figure S1B ) and β-galactosidase IHC in FSP1-Cre/Rosa26 reporter mouse tissue ( Figure S1C ) . The absence of p-smad2 nuclear localization in stroma was used as an indicator for the absence of TGF-β signaling ( Figure S1D ) [1] . Tgfbr2fspKO SCC tumors showed substantial infiltration of CD45+ leukocytes between weeks 3 and 5 compared to Tgfbr2flox/flox littermates ( Figure 1B ) , indicating an inflammatory reaction due to loss of Tgfbr2 in stromal fibroblasts . Inflammation is a critical player in carcinogenesis and is known to cause DNA damage as well as histone modification in cancer [15] , [16] . We thus examined DNA damage in forestomach sections of Tgfbr2fspKO and Tgfbr2flox/flox mice using immunofluorescence staining of 8-oxo-2′-deoxyguanosine ( 8-oxo-dG ) , a major product of DNA oxidation indicative of DNA damage . Interestingly , DNA damage was initially detected in mice at 3 weeks of age and became progressively worse by 5 weeks ( Figure 1C ) concomitant with infiltration of CD45+ leukocytes . The expression of γ-H2AX ( Figure 1D ) , a histone molecule associated with DNA double strand breaks , was also increased in Tgfbr2fspKO mice . The 8-oxo-dG and γ-H2AX were not seen in the forestomach of Tgfbr2flox/flox control mice ( Figure 1D , left and middle panels ) . Our data suggest that loss of Tgfbr2 in FSP1+ stromal cells induced inflammation and DNA damage . DNA damage often results in chromosomal aneuploidy [17] and alteration of epigenetic marks including acetylation , methylation , and ubiquitylation [18] . We evaluated genetic alterations using array-CGH and genomic DNA PCR . We first analyzed epithelial cells isolated from forestomach tumors of Tgfbr2fspKO mice . We found a loss of band C4 of chromosome 4 ( Figure S2A ) , which contains CDK inhibitors Cdkn2b/p15INK4B ( p15 ) , Cdkn2a/p16Ink4A ( p16 ) , and Cdkn2a/p19Arf . Loss of p15 and p16 tumor suppressor genes is a frequent event in human and mouse cancers [19] , [20] . We confirmed loss of p15 and p16 using array-CGH and genomic PCR of epithelial cells from tumor tissue sections of 5 week old Tgfbr2fspKO mice using laser capture microdissection technology ( Figure S2B and S2C , Figure 1E ) . Cdkn2a/p19Arf is an alternative reading frame in the same locus that harbors p16 , it is presumably deleted along with p16 . These genetic alterations were not detected in the stromal compartment of the forestomach sections from Tgfbr2fspKO mice ( Figure S2C and Figure 1E ) . Western analysis of forestomach tumor tissues from Tgfbr2fspKO mice showed loss of or decreased expression of p15 and p16 proteins ( Figure 1F ) , which was detected from 4 week of age ( Figure 1G ) , one week after the inflammation onset ( Figure 1B ) . p15 and p16 are critical mediators in cell cycle control and are important in suppressing tumor development [19] . Our data suggest that deletion of Tgfbr2 in FSP1+ stromal cells induced a loss of p15 and p16 in epithelial cells . Our data suggest a dysregulation of the G1 cell cycle checkpoint in epithelial cells due to loss of Tgfbr2 in FSP1+ stromal cells . We next examined several critical molecules in cell cycle control . The expression of Cyclin D1 was increased in the forestomach of 5 week old Tgfbr2fspKO compared to Tgfbr2flox/flox mice ( Figure 2A ) . Likewise , expression of phospho-p53 ( p-p53 ) was increased ( Figure 2A ) , likely in response to DNA damage ( Figure 1C ) . No mutation was found in p53 ( data not shown ) . Surprisingly , expression of Cdkn1a/p21 ( p21 ) , the downstream mediator of p53 , was reduced in the forestomach of Tgfbr2fspKO mice at 3 weeks of age but with more profound reduction at 4 weeks ( Figure 2A and 2B ) . Suspecting epigenetic regulation of p21 expression in tumor cells , we performed pyrosequencing and found an increased methylation of CpG in the p21 promoter in forestomach tumor samples of Tgfbr2fspKO ( 77% ) compared to that of Tgfbr2flox/flox mice ( 29% ) ( Figure 2C , left panel ) . We then treated forestomach tumor epithelial cells ( 1096 , isolated from SCC of FSP-Cre/RII knock out mouse , and kept in low passages ) with the DNA methyltransferase inhibitor 5-aza 2′ deoxycytidine ( Decitabine ) at 5 uM concentration for 48 hours . We observed increased expression of p21 at both the mRNA and protein level , and decreased cell proliferation compared to untreated cells ( Figure 2C , right panel , and Figure S3A ) . Our data suggest that methylation of the p21 promoter likely prevented p53-mediated p21 transcription , resulting in decreased expression of p21 in Tgfbr2fspKO mice . Together , these data support a loss of cell cycle mediators in epithelial cells due to a loss of Tgfbr2 in stromal compartment in Tgfbr2fspKO mice . We next evaluated cell proliferation in the forestomach of Tgfbr2fspKO and control mice as downregulation of p15 , p16 and p21 may lead to increased proliferation . Forestomach tissue from 3 , 4 , and 5-week old mice , was collected and stained with FSP1 ( stroma ) and cytokeratin-14 ( K14 ) ( epithelium ) . Using immunofluorescence , we observed hyperplasia at 4 weeks and dysplasia/carcinoma in situ at 5 weeks in the epithelial compartment ( Figure 2D ) , and increased FSP1+ cells in Tgfbr2fspKO mice ( Figure 2E ) . The latter was likely due to deletion of Tgfbr2 and loss of growth inhibition . Co-staining of Ki-67 , a marker of cellular proliferation , with K14 or FSP1 showed the extent of proliferation was more pronounced in the epithelial compartment compared to the stromal compartment ( Figure 2F ) . Interestingly , the epithelial compartment had very few apoptotic cells compared to the stromal compartment ( Figure S3B ) . Together , these data suggest that absence of TGF-β signaling in stromal fibroblasts likely induced uncontrolled proliferation and decreased apoptosis in the epithelial compartment . Inflammation has been shown to play an important role in carcinogenesis . To characterize the role of inflammation in Tgfbr2fspKO mice , we collected forestomach tissue from 4 week old Tgfbr2fspKO and Tgfbr2flox/flox control mice for Western analysis . We found an increased expression of inducible nitrogen oxide synthatase ( NOS2 ) , cyclooxygenase 2 ( COX2 ) , and nuclear factor ( NF ) κB subunit ( p65 ) in forestomach epithelail layer samples of 4 week old Tgfbr2fspKO mice compared to control mice ( Figure 3A , left and right panels ) . The increased expression of NOS2 , COX2 , and p65 was found in both the stromal and epithelial compartments ( Figure 3B ) . IFN-γ and TNF-α were significantly increased in the tumor tissues ( Figure 3C ) , suggesting a type I inflammatory response associated with the development of SCC frequently observed in gastrointestinal cancers [21] . Inflammation promotes migration and infiltration of leukocytes , certain types of which are known to have significant impact on tumor microenvironment and tumor progression . We further characterized the subsets of infiltrating immune cells described earlier ( see figure 1 ) . We found a significant increase in CD45+ cells in the forestomach of 5-week-old Tgfbr2fspKO compared to control Tgfbr2flox/flox mice ( 29 . 36% vs 8 . 34% ) ( Figure 3D ) . The increased immune cells included Gr-1+ CD11b+ cells ( 20 . 52% vs 9 . 59% ) , also known as myeloid derived suppressor cells ( MDSCs ) . These MDSCs are mostly monocytic subset Ly6C+CD11b+ ( 13 . 81% vs 1 . 10% ) but not Ly6G+CD11b+ cells ( Figure 3E ) . MDSCs have significant impact on tumor microenvironment [8] , [21] , [22] and are well known for their role in cancer associated immune suppression [23] . In addition , there was a significant presence of TH17 cells that stained for CD4+IL17A+ by immunofluorescence staining in 5-week-old Tgfbr2fspKO compared to control Tgfbr2flox/flox mice ( Figure 3F ) . These findings reveal considerable alteration in the cellular and molecular properties of the tumor microenvironment in Tgfbr2fspKO compared to Tgfbr2flox/flox mice , suggesting that multiple inflammatory mediators interacting with an altered microenvironment are implicated in the progression of SCC following deletion of TGF-β signaling in stromal fibroblasts . To investigate the role of inflammation in SCC development , Tgfbr2fspKO mice were treated with the COX2 inhibitor ( Celecoxib ) . Celecoxib treatment significantly improved body size and weight , decreased tumor burden , and increased lifespan of Tgfbr2fspKO mice from 28 to 49 days ( Figure 4A , left and right panels and Figure S4A and S4B ) . The median survival of Celecoxib treated Tgfbr2fspKO mice was 53 days compared to 38 days in untreated mice ( Log rank p<0 . 001 ) ( Figure 4A , left and right panels and Figure S4A and S4B ) . This was accompanied by reduced infiltration of CD45+ leukocytes and decreased hyperplasia ( Figure 4B ) . Surprisingly , treatment with L-NAME , an inhibitor of NOS2 , did not significantly affect body weight and tumor burden or lifespan despite decreased serum levels of nitric oxide to normal baseline ( Figure S4A and S4B , Figure S5A and S5B ) . Alterations in microbial communities , particularly in gastrointestinal ( GI ) tract , are associated with inflammation and cancer development [24] . To investigate the role of microbiome in the progression of SCC in the Tgfbr2fspKO model , we re-derived the mouse line using super-ovulation and artificial insemination to obtain pups free of Helicobacter ( Table S1 ) . The uninfected Tgfbr2fspKO mice displayed significantly improved body size and weight with a median survival time of 47 days compared to 38 days in mice housed under standard conditions ( Log rank p<0 . 001 ) ( Figure 4A , left and right panels and Figure 4B ) . The tumors in the uninfected mice were characterized by decreased CD45 infiltration , decreased production of COX2 and P-p65 ( Figure 4B and Figure S5C ) ; delayed hyperplasia and dysplasia compared to the control mice ( Figure 4B ) . These data suggest an involvement of microflora in the inflammation and SCC development in Tgfbr2fspKO mice . Indeed , alterations in the microflora are associated with inflammation and intestinal metaplasia of the distal esophagus [25] . In addition to a decrease in p53 expression ( Figure 4C ) , Celecoxib treatment decreased COX-2 , NOS2 and p65 expression to levels similar to control samples ( Figure 4C and Figure S5C ) . L-NAME treatment decreased the expression of COX-2 , NOS2 and p65 . However , it did not affect p53 and γ-H2AX production ( Figure 4C ) . Celecoxib treatment and Helicobacter free environment significantly reduced IFN-γ and TNF-α levels ( Figure 4D ) , and decreased 8-oxo-dG production in the forestomach of Tgfbr2fspKO mice ( Figure 4E ) . These data suggest that COX-2 and Helicobacter infection are important mediators in inflammation and SCC progression . We showed earlier that the downregulation of p21 was likely mediated by methylation of p21 promoter . We next investigated whether anti-inflammatory treatment would decrease this methylation and increase p21 expression . Celecoxib treated Tgfbr2fspKO mice were evaluated for p21 promoter methylation by pyrosequencing of the epithelial layer from the forestomach tissue . Interestingly , methylation was significantly decreased ( p<0 . 05 ) at CpG 16 ( Figure 5A ) . Consistent with this finding , the p21 mRNA expression was observed in the laser captured epithelia from Celecoxib treated Tgfbr2fspKO mice ( Figure 5B ) . The p21 mRNA in the stroma was not different from that of the Tgfbr2flox/flox control mice , and was not changed by Celecoxib treatment ( Figure 5B ) . p21 protein expression was also increased upon Celecoxib treatment ( Figure 5C ) . In contrast , p15 expression was not observed ( Figure 5D ) . Together with genomic PCR showing a loss of p15 ( Figure 1E ) , our data indicate that p15 might be genetically deleted . Surprisingly , Celecoxib treatment also resulted in increased expression of p16 protein ( data not shown ) , suggesting a possible methylation of p16 promoter as a result of inflammation , similar to that of p21 . Together , our data support that inflammation induced DNA damage , genetic and epigenetic alterations of cell cycle mediators play a critical role in SCC progression in Tgfbr2fspKO mice . The SCC in the forestomach of the Tgfbr2fspKO mice shows similarity to that of human ESCC by way of similar histology and functional behavior . Additionally , downregulation of TGF-β receptors has previously been reported at the invasive front and stroma in human ESCC [26] and prostate cancer [13] , [27] . Due to these histological and molecular similarities , we measured the TβRII expression level in FSP1+ stromal cells of eight human ESCC specimens . Adjacent normal tissues from these patients served as a control ( Figure S6A ) . We observed an increased number of FSP1+ cells in the stromal compartment of the ESCC tumors compared to the adjacent normal esophagus ( 32 . 2% vs 6 . 6% , p<0 . 001 ) ( Figure 6A , upper panels ) . This data was consistent with the expansion of FSP1+ cells in Tgfbr2fspKO mice ( Figure 2E ) . In these FSP1+ cells , TβRII expression was decreased in tumor esophagus compared to adjacent normal esophagus ( 39 . 8% vs 93 . 2% , p<0 . 001 ) ( Figure 6A , lower panels ) . Down-regulation of TβRII was also observed in tumor-associated stroma compared to the adjacent normal in a dataset of breast carcinoma ( Ma 4 Breast ) ( Figure 6B ) . The expression of p65 and NOS2 was elevated , 8-oxo-dG was increased in both stromal and epithelial compartments ( Figure 6C ) , consistent with findings in the animal model . In order to investigate biomarkers of DNA damage and genetic aberrations in human ESCC , we interrogated the Oncomine database ( www . oncomine . com ) . Expression of H2AX mRNA was significantly upregulated in ESCC ( p<0 . 0001 ) ( Figure 6D ) . Additionally , p15 and p16 were co-deleted in human ESCC ( Figure 6E ) and [28]–[30] . These data suggest an association of reduced expression of TβRII in stromal cells with increased inflammation , DNA damage , and genetic alterations in human ESCC , which is consistent with our observations in Tgfbr2fspKO mice .
We used FSP1-cre transgenic mice to mediate Tgfbr2 deletion specifically in the stromal fibroblasts . However , no alteration in epithelial TβRII was noticed . We used several approaches including TβRII immunofluorescence , p-smad2 nuclear localization in stroma , and an FSP1-Cre/Rosa26 reporter mouse to verify specificity . FSP1/S100A4 was identified as a specific marker for fibroblasts [31] . The FSP1-cre mediated gene deletion has been widely used in a number of mouse models for fibroblast specific gene deletion [1] , [4] , [32] . However , several studies reported that a specific subset of inflammatory macrophages co-express FSP1 and F4/80+ under a number of pathological conditions [33] , [34] . We did not observe an overlap between FSP1+ cells with F4/80 positive cells ( Figure S6B ) . This is supported by an extensive study regarding comparison overlap between FSP1+ fibroblasts with the macrophage markers , in which they authors demonstrated that F4/80 antibodies can be used to distinguish macrophages from FSP1+ fibroblasts [35] . We thus believe that the FSP1-cre mediated deletion is highly specific in a subset of stromal fibroblasts , with a small possibility in other host cells , and certainly not in tumor cells . TGF-β signaling regulates inflammation , as deletion of Tgfbr2 increased the transcription factor NF-κB [36] . In TGF-β1 deficient mice , inflammation causes precancerous lesions to progress to colon cancer [37] . Inflammation is known to induce DNA damage , genetic alterations and histone posttranslational modifications in mice and human cancers [15] , [16] , [20] , [38] . We found a significant production of 8-Oxo-dG and γ-H2AX , indicating severe DNA damage from 3 weeks of age in the Tgfbr2fspKO mice . This is likely due to production of reactive oxygen and nitrogen species such as nitrogen oxide ( NO ) . DNA damage often results in chromosomal segregation errors and structural alterations including mutations , deletions , amplifications , and balanced/unbalanced chromosomal translocations [17] , [20] , [39]–[41] . Indeed , we found a loss of p15 and p16 located in mouse chromosome 4 band C4 locus , which is orthologous to chromosome band 9p21 in humans . The loss of these chromosome bands is frequently observed in murine and human cancers [20] , [42] , [43] . Inflammatory mediated genetic alterations were previously observed in the head and neck tumor mice model , in which SMAD4−/− deletion in head and neck epithelia resulted in genetic aberrations and deletion of chromosome 4q in SMAD−/− mice [20] . Conditional deletion of p120 in the esophagus , oral cavity , and forestomach increased the production of proinflammatory cytokine TNF-α [21] . TNF-α , and IFN-γ , are known to induce epithelial dysfunctions and SCC of intestine [44] , [45] . Our data and these published reports may provide molecular insight for a previous study that showed inactivation of Smad4 and PTEN in K5+ epithelia induced forestomach SCC development and downregulation of CDK inhibitors [46] . Deletion of Tgfbr2 induced inflammation was also responsible for the decreased p21 expression in the Tgfbr2fspKO mice . Very interestingly , increased p53 expression in response to DNA damage did not result in elevated expression of p21 . Our data demonstrated that the inflammation induced methylation of p21 promoter region . This methylation inhibited the expression of p21 , the critical mediator of p53 function . The inhibition of p21 was reversed by treatment with the COX2 inhibitor Celecoxib and 5-aza 2′ deoxycytidine treatment . We believe that the loss of p15 and p16 , combined with decreased expression of p21 is critical in dysregulation of cell cycle control . This explains a massive proliferation of epithelia in the forestomach of Tgfbr2fspKO mice . Our data add novel mechanistic insight to the SCC development in addition to the finding of HGF as critical mediator [1] ( Figure S7 ) . Our data are supported by studies in which decreased levels or total loss of TGF-β signaling via defects of TGF-β receptors or Smads resulted in inflammation and uncontrolled proliferation of epithelial cells while promoting tumor development [26] , [36] , [47] . Our results in Tgfbr2fspKO mice are clearly corroborated by human ESCC . Our studies showed significantly decreased expression of TβRII in FSP1+ stromal cells in human ESCC ( Figure 6A ) . Interestingly , no alteration of TβRII was found in tumor epithelial cells compared to that of adjacent normal tissue [48] , [49] . Similar to our mouse model data , elevated expression of inflammatory mediators such as COX2 and CCL2 as well as production of DNA damaging mediators 8-Oxo-dG and γ-H2AX were associated with the development of human ESCC ( Figure 6C and 6D ) [28] , [48] , [50] . Methylation of p21 gene promoter was also observed in 56% ESCC [28] . In addition , genetic deletion , loss of heterozygosity , and promoter methylation of p15 and p16 genes was associated with the development of human ESCC [30] , [51] , [52] . Co-deletion of p15 and p16 has also been found in human ESCC ( Figure 6E ) and [29] , [49] , [51] . Notably , approximately , 60% of human SCC including skin , head and neck , esophagus , bronchi , and uterine cervix are associated with the alterations in TGF-β signaling pathway molecules [53] . Indeed , stromal cell signaling has an impact on epithelial carcinogenesis and prediction of clinical outcome of cancer [54] . Furthermore , down-regulation of TβRII in tumor-associated stroma is correlated with poor prognosis in the clinic [55] . The squamous mucosal lining of mouse forestomach is similar to that of human esophagus at both histopathological and molecular levels [56] . Therefore , targeting inflammation may be a strategy to counteract the stromal-epithelial cross-talk in ESCC development .
Cre-Tgfbr2flox/flox female and Cre+Tgfbr2flox/wt male mice were kindly provided by Dr . Harold Moses , Vanderbilt Cancer center , Nashville , TN . Mice were bred to yield Tgfbr2fspKO mice . Cre+Tgfbr2flox/wt male mice were crossed with Rosa26 reporter female mice to validate the Cre specificity . All mice were housed at the National Cancer Institute ( NCI ) animal facility Animal studies were performed under NCI- IACUC approved protocol . Forestomach samples were collected from Tgfbr2fspKO and Tgfbr2flox/flox mice , which then fixed , sectioned and stained using H&E . For immunofluorescence studies , the sections were incubated overnight at 4°C with primary antibodies directed against TβRII ( 1∶50 , Santa Cruz ) , S100A4 ( 1∶100 , Abcam ) , K14 ( 1∶100 , Covance ) , F4/80 ( 1∶100 , BD Transduction Laboratories ) , NOS2 ( 1∶100 , BD Transduction Laboratories ) , Cox2 ( 1∶100 , Cell Signaling Technology ) , p65 ( 1∶100 , Cell Signaling Technology ) , γH2AX ( 1∶100 , Trevigen ) , CD4 and IL17A ( 1∶100 , Biolegend ) . Fluorescence-tagged secondary antibodies were used for visualization ( anti-rabbit , 1∶1000 , Invitrogen or; anti-mouse , Vector lab , respectively ) . Slides were examined using fluorescence microscopy ( Olympus ) . For immunohistochemistry , slides were incubated with primary antibodies against CD45 ( 1∶100 , BD Pharmigen ) , Psmad2 ( 1∶100 , Cell Signaling Technology ) , and β-galactosidase ( 1∶100 , Abcam ) . Signals were visualized using Vectastain ( Vector Lab ) and examined under a light microscope ( Carl Zeiss ) . Quantitative data was measured by counting total number of cells expressing the marker out of all the cells in one field by Image J software . Three different fields were evaluated; percentage was calculated from total number of cells counted and averaged for three independent fields . Apoptosis was evaluated by TUNEL ( Terminal deoxynucleotidyl transferase dUTP nick end labeling ) method using “In Situ cell death detection kit , Fluorescein” ( Roche Applied Science , Indianapolis , IN , USA ) . Briefly , tissue sections were pretreated with xylene and ethanol ( 100% , 95% , 90% , 80% , 70% ) , washed with 1× PBS , then treated with proteinase K ( 20 ug/ml in 10 mM Tris-HCL ( pH 7 . 4 ) and 50 ul tunel mixture for 60 min . Data was acquired using fluorescence microscopy ( Olympus ) . Quantitative data was obtained using Image J software by counting the number of cells expressing the marker out of total cells for each field . Three different fields were evaluated; the data was presented as percentage of stained cells in total cells and averaged for three independent fields . Single cell suspensions were made using fresh forestomach tissue from Tgfbr2fspKO and Tgfbr2flox/flox mice through incubation with Liberase TL ( 200 U/mL ) ( Roche Applied Science , Indianapolis , IN ) at 37°C for 30 minutes . Forestomach tissue was then crushed and filtered through a 70 µm cell strainer . Cells were labeled with fluorescence-conjugated antibodies against CD45-PE , CD11b-FITC , Gr-1-APC , Ly6G-APC , Ly6C-PE , or 7-AAD ( BD Pharmingen ) . Isotype-matched IgG was used as a control ( BD Pharmingen ) . The flow data was acquired on BD FACS Calibur flow cytometer ( BD Biosciences , San Jose , CA ) and analyzed using FlowJO . The forestomachs were dissected and treated with 0 . 05% trypsin overnight at 4°C . The forestomach tumor tissues from Tgfbr2fspKO or equivalent normal tissues from Tgfbr2flox/flox mice were separated by peeling them from the stromal and muscle layers . Protein was extracted , and then separated by gel electrophoresis . Membranes were incubated with primary antibodies against NOS2 ( 1∶100 , BD Transduction Laboratories ) , γH2AX ( 1∶1000 , Trevigen ) , HGF ( 1∶1000 , Santa Cruz Biotechnology ) , Cox2 , P-p65 , P-p53 , Cyclin D1 , p21 , p15 , and p16 ( all at 1∶1000 , Cell Signaling Technology ) or β-actin ( 1∶5000 , Sigma ) , and horseradish peroxidase-conjugated secondary antibody ( 1∶5000 , Biorad ) . The blots were developed using a SuperSignal West Pico Chemiluminescent substrate kit ( Pierce ) . Images were scanned in a G: Box ( Syngene ) . DNA was isolated from epithelial layers of forestomach as described in “Western Blotting . ” QIAGEN Genomic-tip 20/G ( Qiagen , CA , USA ) and modified with Epitech Bisulfite kit ( Qiagen , CA , USA ) were used in pyrosequencing . PCR templates for pyrosequencing analysis were amplified from 10 ng gDNA using Hotstart Taq Mastermix ( Qiagen , CA , USA ) and 5 pmol of each primer in a total reaction volume of 25 µl . In all , 1 µl of each PCR reaction was analysed on an Agilent 2100 Bioanalyzer ( Santa Clara , CA ) using a DNA 1000 kit . Pyrosequencing was carried out on 0 . 15–0 . 5 pmol of each PCR product using the PyroMark MD System ( Qiagen , CA , USA ) following the manufacturer's instructions with sequencing primers and assay parameters specific to each methylation site . Resulting pyrograms were analysed using the PyroMark MD 1 . 0 software in ‘AQ mode’ . For each assay , duplicate pyrosequencing analysis was performed , and the average of these was taken to represent the identified percentage methylation of the methylated allele . Laser capture microdissection of Tgfbr2flox/flox , Tgfbr2fspKO and Celecoxib treated Tgfbr2fspKO mouse tissue was performed using an Arcturus XT ( Life Technologies , CA , USA ) . Frozen tissue sections on PEN membrane frame slides ( Applied Biosystems ) were H&E stained followed by dehydration using the standard protocol to improve visualization of the cells at the microscope . The epithelia and stroma were identified by morphology , captured using a low-power infrared laser pulse , and transferred onto a cap ( Capsure™ Macro LCM Caps , Life Technologies ) . The DNA was extracted using a QIAamp DNA micro kit ( Qiagen , CA , USA ) . Primary epithelial cells ( 1096 , isolated from SCC of FSP-Cre/RII knock out mouse , and kept in low passages ) were cultured in 6 well plate with seeding density 0 . 3×106 per well for overnight in DME/F12 medium containing 10% FBS and 1× antibiotics ( GIBCO , Life Technologies , CA , USA ) . DNA methyltransferase inhibitor , 5-aza 2′ deoxycytidine ( Decitabine ) , was added to the culture at 5 uM concentration for 48 hours . Cells were harvested using 0 . 25% trypsin-EDTA ( GIBCO , Life Technologies , CA , USA ) and subjected to total RNA extraction by RNeasy mini kit ( Qiagen , CA , USA ) and protein extraction by standard method . Total RNA was isolated from laser captured micro-dissected tissue samples by Arcturus Picopure RNA isolation Kit ( Applied Biosystems ) . Reverse transcription was performed using oligo dT primers and Superscript II ( Invitrogen ) . The primers for qPCR were mCDKN1A ( p21 ) F: 5′-ACAGGAGCAAAGTGTGCCGTTGT-3′; mCDKN1A ( p21 ) R: 5′- GCTCAGACACCA GAGTGCAAGACA 3′; mGAPDH F: 5′-ATGACCACAGTCCATGCCATCACT-3′; mGAPDH R: 5′-TGTTGAAGTCGCAGGAGACAACCT-3′ . PCR reactions were performed using fast real-time 7500 PCR system ( Applied Biosystems ) . All samples were tested in triplicate . The comparative CT method was used for quantification of gene expression . Gapdh was used as an endogenous reference . Statistical analysis was performed using SDS v2 . 1 software ( Applied Biosystems ) according to the manufacturer's instructions . Protein extraction was obtained from forestomach samples of Tgfbr2fspKO and Tgfbr2flox/flox mice , and analyzed for IFN-γ and TNF-α expression , as per manufacturer's instruction . Data was acquired and analyzed using Bio-Plex Manager version 4 . 0 software ( Bio-Rad ) . Genomic DNA was isolated from primary tumor cells ( 1096 ) from Tgfbr2fspKO mice and primary normal epithelial cells isolated from forestomach epithelial cell layer with a QIAamp DNA Mini Kit according to manufacturer protocol ( Qiagen , Valencia , CA ) . Array-CGH was performed using test DNA from laser captured epithelia and stroma , 1096 primary tumor cell culture , and reference DNA . DNA was labeled with Cy3 or Cy5 fluorescent dyes ( Pharmacia , Piscataway , NJ ) according to the BioPrime array CGH genomic labeling protocol ( Invitrogen , Carlsbad , CA ) and cleaned using Microcon YM-30 filters ( Millipore , Billerica , MA ) . Hybridization was carried out using Mouse Genome CGH Microarray 4×44 K from Agilent Technologies ( Santa Clara , CA ) according to CGH Procedures for Genomic DNA Analysis ( Agilent Technologies ) . Slides were hybridized for 20 hours , washed , and scanned with an Agilent microarray scanner . Data was analyzed using Feature Extraction® and CGH Analytics® software packages ( Agilent Technologies ) . The array-based CGH data is available , GEO accession number: GSE42773 . Genomic DNA from laser captured epithelia and stroma described above was used for genomic PCR using the 2× Taq master mix ( Gene Script , NJ , USA ) , 50 ng genomic DNA and Exon 1 specific primers of mouse p15 and p16 genes . The primer sequences were p15: forward- 5′- GTT GGG CGG CAG CAG TGA C-3′ and reverse- 5′-CCT CCC GAA GCG GTT CAG-3′ , p16: forward- 5′-ACT GGT CAC ACG ACT GGG CGA TTG -3′ and reverse- 5′-AAT CGG GGT ACG ACC GAA AGA G-3′ . Actin: forward-5′-TCA TCA GGT AGT CAG TGA GGT CGC-3′ and reverse-5′-CAC CAC ACC TTC TAC AAT GAG CTG-3′ . The PCR conditions included initial denaturation at 95°C for 5 min , denaturation at 95°C for 1 min , annealing at 60°C for 1 min and extension at 72°C for 1 min for 40 cycles and final extension at 72°C for 7 min . Agarose gel electrophoresis ( 2% ) was used to detect the PCR products and data was recorded using G: Box ( syngene ) . C57BL/6NCr wild type female mice were super-ovulated and crossed with the Fsp-Cre male mice . Four or eight cell embryos were transferred to pathogen free females . After birth , pups were examined for infection and genotypes using ELISA and PCR . Male mice with Cre+ Tgfbr2flox/wt and female mice with Cre- Tgfbr2flox/wt were identified and crossed to obtain pathogen free Tgfbr2fspKO mice . The mice re-derivation was performed in the mice re-derivation core facility located at NCI-Frederick , MD . Mice were transferred and housed in Helicobacter free facility at NIH Bethesda , MD . Tgfbr2flox/flox and Tgfbr2fspKO mice were treated with diet pellets containing Celecoxib at 1500 ppm with ( D04090202 ) or without ( AIN 76A D1000 ) active compound , or Nω-Nitro-L-arginine methyl ester hydrochloride ( L-NAME , Sigma ) at a dose of 50 mg/kg/day . The pups received the Celecoxib starting the second week after birth , together with the nursing mother mouse in the same cage , due to early inflammation onset ( Figure 1B ) . The treatment continued after weaning . The synergistic effect of L-NAME and Celecoxib was also examined . In addition , Mice housed in the Helicobacter free conditions were treated with Celecoxib to evaluate the cooperative effect in survival and phenotype . For gel diet treatment , after weaning on day 21 , Tgfbr2fspKO and Tgfbr2flox/flox pups were fed with gel diet ( AIN76A , Clear H2O ) . All mice were monitored daily and sacrificed with signs of poor health including small size , hunched body , slow movements , and weakness in comparison to healthy littermates . Human ESCC ( n = 8 ) and adjacent normal FFPE tissue slides ( n = 8 ) were previously described [57] ( PMID: 19789312 ) , and were stained to detect TβRII expression in FSP1+ stromal cells . p65 , NOS2 , and 8-Oxo-dG adducts were also examined for inflammation and DNA damage . The immunofluorescence staining procedures are as described above . Oncomine database ( www . oncomine . com ) was utilized to evaluate γ-H2AX expression , p15 , and p16 loss in human ESCC , specifically mRNA expression datasets , Hu Esophagus ( 34 samples ) and Su Esophagus 2 ( 106 samples ) , and DNA copy number datasets , Hu Esophagus 2 ( blood vs LCM tumor sample ) and Bass Esophagus ( cell lines and tissue specimens ) . Dot plots of H2AX mRNA expression are presented as log2 median-centered intensity . The dataset ( PMID: 19789312 ) from Oncomine was analyzed using GraphPad Prism 5 . 0 and two-tailed , paired t-test . Heat map of copy number loss of p15 and p16 in ESCC was obtained from the same dataset that measure DNA copy number on a SNP microarray platform . The expression of TβRII in tumor-associated stroma vs adjacent normal was also analyzed using Oncomine mRNA dataset Ma 4 Breast Carcinoma . Data was analyzed using the Student t-test , and was expressed as mean ± SE . Differences were considered statistically significant at p<0 . 05 . Mouse survival data was examined using SPSS 16 software , and is presented as Kaplan Meier curve . A log rank test was used to calculate statistical differences in survival and median survival of the different groups .
|
Cancer is no longer regarded as a problem of solely cancer cells . The development and metastasis of cancers clearly involves many aspects of the host . We sought to identify the molecular mechanisms underlying epithelial cancer development due to alterations in stromal cells . Using an animal model in which TGF-β signaling is deleted in stromal fibroblasts , we found that inflammation and DNA damage are induced in the epithelial compartment and are responsible for the loss of cell cycle–dependent kinase inhibitors , leading to the compromise of epithelial cell cycle control . These results are important in understanding the stromal-tumor cross talk which has been an important focus in cancer biology in recent years . Our findings suggest that careful examination of the stromal compartment is important and that anti-inflammation therapy may be a new chemoprevention option for epithelial cancer development .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"cancer",
"genetics",
"immune",
"cells",
"mechanisms",
"of",
"signal",
"transduction",
"gene",
"regulation",
"immunology",
"epigenetics",
"molecular",
"genetics",
"gene",
"expression",
"biology",
"dna",
"modification",
"immune",
"response",
"signal",
"transduction",
"monocytes",
"genetics",
"molecular",
"cell",
"biology",
"genetics",
"and",
"genomics"
] |
2013
|
Inflammation-Mediated Genetic and Epigenetic Alterations Drive Cancer Development in the Neighboring Epithelium upon Stromal Abrogation of TGF-β Signaling
|
Dengue virus infections are a major cause of febrile illness that significantly affects individual and societal productivity and drives up health care costs principally in the developing world . Two dengue vaccine candidates are in advanced clinical efficacy trials in Latin America and Asia , and another has been licensed in more than fifteen countries but its uptake has been limited . Despite these advances , standardized metrics for comparability of protective efficacy between dengue vaccines remain poorly defined . The Dengue Illness Index ( DII ) is a tool that we developed thru refinement of previous similar iterations in an attempt to improve and standardize the measurement of vaccine and drug efficacy in reducing moderate dengue illness . The tool is designed to capture an individual’s overall disease experience based on how the totality of their symptoms impacts their general wellness and daily functionality . We applied the DII to a diary card , the Dengue Illness Card ( DIC ) , which was examined and further developed by a working group . The card was then refined with feedback garnered from a Delphi methodology-based query that addressed the adequacy and applicability of the tool in clinical dengue research . There was overall agreement that the tool would generate useful data and provide an alternative perspective to the assessment of drug or vaccine candidates , which in the case of vaccines , are assessed by their reduction in any virologically confirmed dengue of any severity with a focus on the more severe . The DIC needs to be evaluated in the field in the context of vaccine or drug trials , prospective cohort studies , or during experimental human infection studies . Here , we present the final DIC resulting from the Delphi process and offer its further development or use to the dengue research community .
Interventional efficacy trials need to prospectively define criteria for what constitutes a “case” of dengue . Defining what will and will not be considered a “case” is not a simple task . It encompasses a number of considerations and nuances . The definition must be objective and easily measurable . It must not require prohibitively expensive or complex technology to complete the measurements . The definition must be applicable across countries and cultures where health care seeking behavior and the standard approach to medical care delivery may vary , even in the context of a highly regulated and controlled clinical trial . Ideally , the case definition should represent the public health burden the intervention is seeking to relieve . “Cases” should also occur with sufficient frequency within a proposed study population , such that the conduct of an efficacy trial is financially and logistically feasible within a reasonable timeframe . For dengue vaccine trials , developers/sponsors have used some variation of fever plus a positive reverse transcriptase-polymerase chain reaction ( RT-PCR ) and/or an anti-dengue non-structural protein 1 ( NS1 ) serological assay as the basis for identifying a “case . ” The prevention of fever plus RT-PCR or anti-NS1 positivity in the vaccine group compared to the control/placebo group defines the primary efficacy endpoint . Secondary endpoints in these trials typically include severe dengue phenotypes or hospitalized disease ( ClinicalTrials . gov Identifier: NCT02747927 , NCT02406729 ) . We hypothesize there is a spectrum of clinically relevant dengue falling between the endpoints of more mild dengue ( most consistent with the definition of a “case” ( via supra ) and more severe dengue cases ( defined in trials as “severe” or hospitalized cases ) . By failing to measure clinical disease events falling between these endpoints , vaccine or drug efficacy may be underestimated . Specifically , a vaccine may not be highly efficacious at preventing milder forms of dengue disease , which the currently used case definitions capture ( fever + RT-PCR positivity ) , but they may be highly efficacious at preventing or treating slightly more severe disease , defined here as “moderate . ” We question at what point along the dengue disease spectrum do you begin to identify illness episodes which are clinically relevant and represent individual and public health burden ? Is the current “case” definition ( fever + PCR positivity ) accurately capturing this point ? Alternatively , have developers/sponsors and regulatory authorities chosen an endpoint which raises the vaccine efficacy “bar” to focus on prevention of severe and hospitalized cases and does not establish a candidate’s true potential public health benefit by measuring prevention of less severe cases ? Because of these questions , we proposed to develop a tool that could measure outcomes of DENV infections to better help elucidate the variable benefits of different vaccine or drug candidates . Importantly , the tool should complement , not replace , the currently used primary efficacy endpoints of severe and hospitalized dengue . It is important to understand the DII is not designed to categorize dengue disease severity in relation to its pathophysiology or prognosis . The DII is also not designed to replace methods of capturing and characterizing dengue cases currently being employed in interventional trials using World Health Organization 1997 or 2009 guidelines . The DII tool is designed to capture the overall subjective disease experience for an individual based on their identification of having individual symptoms and how the totality of these symptoms impacts the individual’s overall feeling of wellness and ultimately , on daily functional activities , such as those that involve work , school , recreation , and sleep . The DII is meant to complement and supplement current methodologies as stated above . The DII is intended to capture and quantify the subjective dengue illness experience and supplement the traditional endpoints dengue vaccine and drug developers are using to support their studies . The DII classification defines ( 1 ) mild illness as feeling somewhat ill but not enough to effect normal activity and no medication is required for treatment , ( 2 ) moderate illness as having some impact on daily activities , and/or non-prescription medication is required to treat signs or symptoms ( such as acetaminophen for headache ) , and ( 3 ) severe illness as preventing most or all daily activities , and/or prescription medications are required to treat symptoms , and/or a visit to health care provider is required . Severe illness may or may not result in hospitalization . We assume mild and severe illness experiences will be captured by the standard dengue surveillance systems being utilized in clinical trials . This makes the case for the value of the DII in identifying moderate illness as that which interferes with daily activities short of hospitalization or organ damage .
A scientific working group for the DII was one of three working groups to convene at a workshop attended by 56 participants representing 16 countries , sponsored by the National Institute of Allergy and Infectious Diseases ( NIAID ) and the Partnership for Dengue Control . This manuscript describes the working group discussions about the DII , its implementation on a diary card referred to as the Dengue Illness Card ( DIC ) , and the results of a Delphi method inquiry that yielded an evolved version of the card proposed for validation and/or piloting . The DII working group was comprised of 10 dengue experts who , along with the members of the other two working groups and non-working group attendees , were identified via referral and selected to achieve a balanced representation of dengue expertise from all sectors and from various global endemic regions . The DII working group members originated from the U . S . , Belgium , Brazil , Nicaragua , and Sri Lanka , and represented , alone or in combination , one or more of the following sectors: the government ( 90% ) , non-government ( 10% ) , academic ( 60% ) , pharmaceutical/vaccine development ( 30% ) , clinical ( 40% ) , and public health ( 30% ) . The first round of comments on the DIC were collected after the first meeting on April 27 , 2015 , and in preparation for the second meeting on October 29 , 2015 , which was largely comprised of the same attendees as the first meeting . A number of comments were received from stakeholders in attendance and helped to shape the final product being presented in this manuscript . Some specific comments which lend insight into the varied perspectives include: The above comments and others were incorporated into modification of the DII; the modified product was then assessed by a Delphi methodology-based query . The Delphi methodology-based query was conducted electronically using Mesydel software and commenced with a panel questionnaire that addressed the Overall Approach ( OA ) of defining and validating the clinical endpoints of moderate and severe dengue in clinical research trials , the overarching topic of the first of the three working groups . Attendees of the first and second meetings , in addition to several non-attendee dengue experts , were invited to participate in the Delphi query and totaled 64 persons , of which 39 accepted an invitation to the initial OA panel . Following this OA panel , participants were given the option of participating in a panel to define the Clinical Endpoints generated from the second working group discussions , or a panel on the third DII working group , or both panels . A total of 23 panelists self-selected to participate in the DII online Delphi query , of which there were 19 active respondents in round 1 , 18 active respondents in round 2 , and 10 active respondents in round 3 who commented on the satisfaction of the process . Participants were provided a graphic of the DIC ( initially referred to as the Dengue Severity Index ( DSI ) card ) and asked to assess its intuitiveness , visual clarity , practicality , and adequacy . They were further asked if the illness metrics on the card were relevant and sufficient for clinical practice and/or research contexts , and if they would use the DIC . In addition , they were asked specific questions about how and under what conditions the card could be used ( e . g . , type of study , study population , etc . ) and if they could recommend improvements to the card . The majority of questions were open-ended , and participants were given the opportunity to explain or comment further on their answer . Responses in each round were collected over a 2 to 3-week duration . Responses from round 1 were analyzed and used to modify the card in accordance with participants’ feedback . In round 2 , the modified card was presented along with clarifications of its features and intended use , and participants were then asked to assess the quality of the revised card similarly to that done in round 1 . The Delphi query concluded with a third round to gauge if respondents felt their comments were taken into consideration , if all relevant issues were addressed , and if they had additional comments .
In round 1 of the Delphi process , over half of the respondents ( 53% ) felt the card was intuitive , while the other 47% felt it was only relatively intuitive or some combination of not intuitive and not appropriate . When asked to assess the clarity and practicality of the tool , 63% of respondents felt it was clear and practical ( or relatively so ) , while 21% felt it was clear but not practical , and 16% felt it lacked both clarity and practicality . The majority of respondents ( 79% ) felt the card was inadequate and cited reasons that overlapped with those that accounted for the tool’s perceived lack of intuitiveness and clarity/practicality . Specifically , respondents felt the card lacked basic instructions regarding how it should be filled out , how the first day of illness is defined , how symptom severity is rated , and how the index value is calculated . Some respondents questioned the grouping of the signs and symptoms on the card , while others questioned the usefulness of the information captured . There was also concern that card-users would erroneously record the severity of individual symptoms as opposed to the severity of their overall daily illness experience as is intended , and lastly , some respondents felt the card would benefit from the addition of operational guidelines regarding how and when temperature should be measured . When asked to comment on the sufficiency of the diary card’s metrics for clinical practice and/or research studies , 47% felt it was sufficient , while 37% felt it was insufficient due to a lack of higher grades of severity of the signs and symptoms , as well as inadequate consideration of symptoms such as neurological signs and bleeding . It was also felt that the metrics were not specific enough because the signs and symptoms were not weighted to account for things like frequency of a fever or intensity of headaches . Several respondents commented that the metrics did not aid the assessment of disease progression or were not predictive of disease outcome . One respondent thought that overall illness severity should also be depicted as one’s inability to administer self-care and conduct regular interpersonal relationships , and there were several reiterations that signs and symptoms should not be grouped , but rather , individualized on the card . Participants were asked in what context the tool would be useful ( what phase , for whom and for clinical practice or research ) . The most cited answer was clinical trials ( 32% ) . Other contexts included research ( 18% ) ; disease burden ( 14% ) ; before/after medical visit ( 14% ) ; clinical practice ( 7% ) ; morbidity ( 7% ) ; human challenge studies ( 4% ) and surveillance ( 4% ) . When asked if they would use the diary card tool , 79% stated they would use it , and when asked for what type of study it should be used , the most frequent answers consisted of vaccine ( 28% ) or clinical trials ( 23% ) . Lesser cited studies included clinical studies ( 9% ) and epidemiological studies; fever of unknown origin; dengue research; natural history studies; household contact studies; febrile illness studies and challenge studies , each at 5% . The other 21% of respondents who indicated they would not use the tool stated that it was not specific , clear , practical , intuitive or precise enough and that the card failed to achieve meaningfulness as a stand-alone tool because it would need to be complemented by information collected at a medical visit , or because it did not assess progression towards severe disease . Suggestions for improvement to the diary card included the addition of more signs and symptoms , as well as simple instructions on how to fill it out and calculate the index value . Some suggested that the format be adjusted to improve ease of use or include a tutorial that informs on the need for urgent medical advice , while other respondents desired more severity detail for the individual signs and symptoms . Several respondents suggested that the card be validated in a prospective study so that grouping of symptoms could be substantiated in an evidence-based manner . One respondent emphasized the need for the tool to be able to predict severe disease outcome , and another respondent suggested eliminating the word “severity” from the name of the card because it tended to be suggestive of the card’s ability to assess the immediate or potential severity of disease as it relates to life-threatening sequelae . In round 2 , a new version of the card renamed “Dengue Illness Card” was presented , which featured formatting suggestions gained from round 1 , along with a thorough introduction that emphasized the card’s intended use to better help inform participants’ answer choices in the subsequent round . All 18 of the active respondents felt the card was intuitive , though one participant still felt it was complex and needed to be simplified to better accommodate users with a basic education level . When asked if the card was clear and practical , 83% felt it was , while the 17% who disagreed felt the format and instructions still required improvement such that “first day of illness” is clearly defined , signs and symptoms be adapted for children , and simple , comprehensive instructions be included for the clinical staff administrator and the user . One participant emphasized the need for a strong teacher-student relationship between the clinical administrator and the user to ensure the card is filled out properly , and another respondent desired for the severity scale be simplified . There was marked improvement in the perceived adequacy of the card as 67% of respondents agreed it was adequate . Reasons cited by the 28% of respondents who did not feel the card was adequate were focused on the calculation of the illness index , stating that severity of illness experience could not be captured in the index calculation without weighting the signs and symptoms . Almost all the participants ( 94% ) felt the card could achieve its intended purpose . The one participant who disagreed was adamant that the card did not have any utility if it could not guide clinical management or “explore disease severity . ” The contexts in which the card was deemed relevant in round 1 were presented in round 2 and respondents were asked to select the top three contexts from the list in which they felt the card is relevant . Eighty-three percent of respondents selected clinical trials , followed by research ( 72% ) ; disease burden ( 56% ) ; human challenge studies ( 44% ) ; before/after medical visit ( 17% ) ; clinical practice ( 17% ) ; morbidity ( 11% ) and surveillance ( 11% ) . One respondent who felt the tool was appropriate for research and human challenge studies stated that it would be useful for vaccine trials in instances where users were familiar with its format and were in possession of the card before the onset of illness , further expressing concern for the need for febrile individuals to fill the card out retrospectively in clinical practice or clinical trials . Another respondent who felt the card was appropriate for clinical practice and before/after medical visits , commented that the card was not specific enough for clinical trials , research and disease burden . The large majority of participants ( 89% ) stated they would use the DIC . The resulting list of studies from round 1 for which respondents said the card should be used was presented in round 2 and respondents were asked to select their top three preferred types of studies . The top studies were vaccine trials ( 68% ) , clinical trials ( 68% ) , and epidemiological studies ( 37% ) . Other study types , which some respondents cited were overlapping with one another , included clinical studies ( 32% ) ; drug trials ( 32% ) ; natural history studies ( 32% ) ; challenge studies ( 21% ) ; dengue research ( 21% ) ; fever of unknown origin studies ( 16% ) ; household contact studies ( 16% ) and febrile illness studies ( 11% ) . The 11% of participants who stated they would not use the card felt it was not applicable to their clinical-oriented work , did not serve as a prognostic tool to assess disease severity progression , remained non-specific as it was not clear how it was applicable to dengue versus other febrile illnesses , or did not include enough severe endpoints . When asked if the card was in suitable condition for testing in a real-life setting , 78% felt it was and 22% felt it was not , stating that the format needed to be refined and the instructions needed to be simplified . Additional suggestions for improvements to the card included pilot testing in different populations to account for different ethnic and cultural norms associated with illness , as well as different educational levels . In round 3 , participants were presented with the results of round 2 along with a graphic image of the final DIC ( Fig 1 and Fig 2 ) . When asked about their overall satisfaction with the DIC Delphi method , >88% felt their input was taken into consideration , felt they received sufficient feedback throughout the Delphi method , and felt that all relevant issues were addressed . Questions addressing the demographic profile of the respondents revealed that they self-identified with the following sectors: academic ( 44% ) ; clinical ( 30% ) ; government ( 30% ) ; pharmaceutical/vaccine development ( 30% ) ; public health ( 26% ) and non-government ( 9% ) . Their work was relevant to the United States ( 44% ) ; Central and South America ( 44% ) ; Southeast Asia ( 22% ) ; Europe ( 13% ) or globally ( 13% ) . The majority of respondents originated from the United States ( 57% ) , while others originated from Europe ( 13% ) ; Southeast Asia ( 13% ) and Central and South America ( 9% ) . All but one respondent worked in a dengue-relevant capacity .
The Dengue Illness Card will advance in two parallel paths . The first path will be immediate application in its current form to ongoing field investigations or investigations where execution is imminent . We communicated with the researchers and their teams that the tool would be made available for their use . The tool was designed to not be cumbersome and to fit into , not replace , the current processes researchers use to collect data during their studies . Once the tool is utilized , data collected , and analysis complete the research teams can decide if it added value to their research . If it did , they could move forward with the tool . If it did not , they could reject further use; either outcome would be informative to the tool developers . The second path would be to allow researchers to take the tool and manipulate it to meet their specific objectives and needs . For example , a pharmaceutical company developing a dengue drug or vaccine may want to use the tool as a foundation to develop a more advanced tool which captures more data or could withstand a more rigorous regulatory assessment . The tool could also be simplified . For example , the 24-hour functional level of illness score ( Fig 1 ) can be calculated and used as stand-alone value to compare the functional burden of an illness episode in trials of different vaccines or drugs . Once reworked , the tool could be prospectively deployed into vaccine or drug trials , used alongside the standard methods of data capture , and its usefulness evaluated and “validated . ” In conclusion , we contend there is merit to developing and deploying a data capture tool which aims to identify the spectrum of dengue disease which is relevant by many metrics but not always captured using the current and broadly applied data capture methods . It is our hope through meetings sponsored by government , industry and foundations and through this publication , researchers will be willing to include this tool into their research plans and to explore the tool’s performance and value , or lack thereof , in investigations of countermeasures to reduce dengue’s global burden .
|
The Dengue Illness Index ( DII ) is a tool that was developed to improve and standardize the measurement of vaccine and drug efficacy in reducing moderate dengue illness by capturing the overall subjective disease experience of an individual based on how the totality of their symptoms impacts their wellness and daily functionality . The DII was applied to a diary card , the Dengue Illness Card ( DIC ) , which was examined and further developed by a working group . The resulting DIC was then refined with feedback garnered from a Delphi methodology-based query that addressed the adequacy and applicability of the card in dengue research . Here , we report on the Delphi results and present the final DIC .
|
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2018
|
Dengue illness index—A tool to characterize the subjective dengue illness experience
|
In multicellular organisms , growth and proliferation is adjusted to nutritional conditions by a complex signaling network . The Insulin receptor/target of rapamycin ( InR/TOR ) signaling cascade plays a pivotal role in nutrient dependent growth regulation in Drosophila and mammals alike . Here we identify Cyclin G ( CycG ) as a regulator of growth and metabolism in Drosophila . CycG mutants have a reduced body size and weight and show signs of starvation accompanied by a disturbed fat metabolism . InR/TOR signaling activity is impaired in cycG mutants , combined with a reduced phosphorylation status of the kinase Akt1 and the downstream factors S6-kinase and eukaryotic translation initiation factor 4E binding protein ( 4E-BP ) . Moreover , the expression and accumulation of Drosophila insulin like peptides ( dILPs ) is disturbed in cycG mutant brains . Using a reporter assay , we show that the activity of one of the first effectors of InR signaling , Phosphoinositide 3-kinase ( PI3K92E ) , is unaffected in cycG mutants . However , the metabolic defects and weight loss in cycG mutants were rescued by overexpression of Akt1 specifically in the fat body and by mutants in widerborst ( wdb ) , the B'-subunit of the phosphatase PP2A , known to downregulate Akt1 by dephosphorylation . Together , our data suggest that CycG acts at the level of Akt1 to regulate growth and metabolism via PP2A in Drosophila .
The growth of an organism is a highly coordinated process regulated by a wide range of different inputs . Members of the Insulin receptor ( InR ) and Target of rapamycin ( TOR ) signaling pathways are well established key players in the control of cell growth in higher eumetazoa . Studies in different organisms support the idea that this signaling network modulates cellular growth in response to nutrient availability , growth factor signaling , energy status as well as to diverse cellular stressors ( for review [1] , [2] ) . Drosophila has proven to be a powerful system for investigating the InR/TOR signaling network . Signaling through the InR pathway is triggered through the binding of Drosophila Insulin-like peptides ( dILPs ) to the single InR . Four of the eight known dILPs ( dILP1 , 2 , 3 and 5 ) are expressed in neurosecretory cells of the brain , the so-called Insulin producing cells ( IPCs ) , from which they are released to act systemically via haemolymph transport [3]-[7] . Activation of the InR triggers a phosphorylation cascade mediated by a relay of kinases . As one of the first steps , the lipid kinase Phosphoinositide 3-kinase ( PI3K92E ) is activated leading to the activation of the kinase Akt1 that in turn phosphorylates the small GTPase Rheb ( Ras homologue enriched in brain ) , an activator of TOR ( for review [2] , [8] ) . The TSC1/2 ( tuberous sclerosis complex ) tumor suppressor complex inhibits the activity of the TOR kinase by negatively regulating Rheb [9]-[13] . In addition , phosphatases like PTEN and PP2A were identified as negative regulators of the InR/TOR signaling cascade [14]-[18] . Starvation , especially amino acid withdrawal , interferes with dILP secretion in larval brains: dILPs accumulate in IPCs , and larval growth is impaired [4] . Nutrient availability is sensed by the TOR network which serves as the central coordinator of cellular and organismal growth , aging and fertility [19]-[21] . The TOR kinase , central to the TOR pathway , exists in two distinct conserved complexes , TORC1 and TORC2 . Like in mammals , TORC1 is the crucial regulator of cell size and organismal growth in Drosophila ( for review [2] , [22] ) . The best studied substrates for TORC1 are S6 kinase ( S6K ) and the eukaryotic translation initiation factor 4E binding protein ( 4E-BP ) , both serving the regulation of translation ( for review [22] ) . Phosphorylation of either protein enhances translation efficiency , either by relief of translational repression as in the case of 4E-BP , or by enhancement of ribosome recruitment as in the case of S6K ( for review [1] , [23] ) . This spectrum of phenotypes conforms to the pivotal role of TOR signaling in the control of growth and maintenance of cellular homeostasis in synchrony with the actual nutrient conditions . In Drosophila the larval fat body , a functional equivalent of the vertebrate liver and white adipose tissue , acts as a nutrient sensor controlling dILP release in the brain [4] , [21] . In accordance , reducing TOR signaling specifically in the fat body has a negative impact on the overall growth of the animal comparable to the effects observed in underfed larvae [4] . Here we identify Cyclin G ( CycG ) as a new regulator of InR/TOR signaling activity in Drosophila . Homozygous cycG mutant flies are viable , however females are sterile . Mutant eggs display dorso-ventral patterning defects in the eggshell due to an impaired EGFR-signaling activity . This phenotype was shown to be a consequence of compromised double strand break repair , assigning CycG a role in meiotic checkpoint control during oogenesis [24] , [25] . Moreover , CycG was proposed to act as a negative regulator of cell growth and cell cycle progression based on the misregulation of CycG activity [26] . Here we report that the phenotypes of cycG mutants recapitulate defects in InR/TORC1 signaling . Our genetic and molecular data indicate that CycG acts at the level of Akt1 presumably via a regulation of PP2A-Akt1 binding . Altogether our genetic and molecular observations provide evidence that CycG is required for InR/TORC1 pathway members to tap their full potential in mediating growth and metabolism in Drosophila .
Homozygous cycGHR7 null mutants are viable but female sterile [24] . They are , however , developmentally delayed and underrepresented with regard to their siblings ( Fig 1A ) . In addition , cycGHR7 mutant animals are smaller and slimmer than the controls ( Fig 1B ) , and have a reduced body weight ( Fig 1C ) . This finding was unexpected as CycG was reported to function as a negative regulator of growth and proliferation based on overexpression studies [26] . We also observed that the ubiquitous overexpression of CycG ( da::CycG ) resulted in a weight loss , however , was able to ameliorate the weight deficit of the cycGHR7 homozygotes ( Fig 1C ) . Because of its ability to bind to several cyclin dependent kinases [26]-[28] , a strong CycG overexpression is likely to interfere with cell cycle regulation , which may explain these observations . In accordance , the subtle induction of a heat shock CycG construct ( hs-CycG ) at ambient temperature was sufficient to robustly rescue the observed growth and weight deficits in cycGHR7 mutant animals ( Fig 1B and 1D ) . The size defect of the cycGHR7 mutant animals was further studied in the wing . Here the reduced size was associated with a reduction in cell size and cell number ( quantified via trichome density ) ( Fig 1E ) , pointing to a defect in InR/TORC1 signaling [29] . Next we induced mutant clones in the developing imaginal tissue by Flp/FRT mediated mitotic recombination [30] . By 72 hours of larval development , the majority of cycGHR7 mutant cell clones was smaller than their wild type twin spots in eye-antennal as well as in wing discs ( Fig 1F and 1F' ) , indicating a cell autonomous requirement of CycG for normal growth . In order to exclude second site defects in the cycGHR7 allele , a second independent cycG allele was generated by ‘ends out’ homologous recombination [31]: in the resultant allele cycGeoC , nearly all of the coding region is deleted ( S1A Fig ) . Like cycGHR7 , the cycGeoC mutant as well as the transheterozygous cycGHR7/cycGeoC combinations behave also as protein null on western blots ( S1B Fig ) and display the same phenotypic characteristics as cycGHR7 , i . e . female sterility with defective egg patterning , developmental delay and a reduced body size and weight ( S1C and S1D' Fig ) . Taken together , our results support a role for CycG as a positive effector of growth/weight control in the fly . The distinct weight reduction of cycG mutant flies suggested a defect in metabolic homeostasis . Weight is a parameter that is directly correlated with food intake and metabolism . However , the ingestion of cycG mutant animals appeared normal as judged by the intake of colored yeast paste ( Fig 2A ) . When exposed to starvation stress , cycGHR7 mutant flies had a reduced life span compared to the wild type control ( Fig 2A' and 2A'' ) . Moreover , cycGHR7 mutant larval fat bodies displayed an aggregation of lipid droplets similar to starved controls ( Fig 2B–2B'' ) . This phenotype has been described before as a result of amino acid withdrawal and likewise loss of TOR and can be taken as an early evidence of fat mobilization for energy consumption [20] . We therefore determined the proportion of lipid and protein in the cycG mutants compared to control larvae of the same developmental stage . We noted a significant shift in favor of the triacylglycerol ( TAG ) level in cycG mutant larvae , suggesting an elevated level of stored fat ( Fig 2C and 2C' and S2A Fig ) . Fat accumulation was further confirmed with a buoyancy-based assay [32] . Whereas wild type larvae sink in a 10% sucrose solution , cycG mutant larvae float due to their higher fat content ( Fig 2D and 2D' and S2B and S2B' Fig ) . The metabolic defect in the cycGHR7 mutant larvae was rescued by low level expression of the hs-CycG construct , which on its own was indistinguishable from the control , emphasizing the specific requirement of CycG for a normal fat metabolism ( Fig 2C and 2D' ) . During poor nutritional conditions TAG is mobilized from the larval fat body and the free fatty acids are delivered to the larval oenocytes [33]-[35] . Oenocytes are hepatocyte-like cells that are clustered underneath the lateral epidermis of the larva [33] . To test lipid release in cycG mutants , we examined lipid accumulation in the oenocytes under conditions of feeding and starvation ( i . e . amino acid deprivation ) [33] . Well fed control larvae show little lipid accumulation in the oenocytes ( Fig 2E and 2H ) , yet an aggregation of lipid droplets is observed after a 14 hours fasting period ( Fig 2E' ) . Oenocytes of cycG mutant larvae accumulated numerous lipid droplets already under normal feeding conditions ( Fig 2F and S2C–S2F Fig ) , which was rescued by the hs-CycG background at ambient temperature ( Fig 2G ) , but which was hardly increased under conditions of starvation ( Fig 2F' ) . The observed differences were highly significant ( S3 Fig ) . In summary , cycG mutants display a starvation phenotype even under normal feeding conditions suggesting a defect in the regulation of lipid mobilization from the fat body . The influence of TOR activity on lipid metabolism is well established [35] . For example , a downregulation of the TORC1 signal by overexpression of the negative regulator TSC1/2 in the fat body has been shown to provoke a marked lipid droplet accumulation in the oenocytes of well fed larvae [33] ( Fig 2I and 2J' ) . We noted that the loss of CycG had a very similar effect as the overexpression of TSC1/2 ( compare Fig 2F and 2J ) , further supporting a link between CycG and the InR/TOR signaling pathway . The primary downstream targets of TORC1 are S6 kinase ( S6K ) and elF-4E binding protein ( 4E-BP ) ( for review [22] , [23] ) . The growth defects of the cycG mutants prompted us to analyze the phosphorylation level of S6K and 4E-BP in protein extracts from cycGHR7 and wild type control flies ( Fig 3 ) : as expected for a positive role of CycG in TORC1 signaling , the level of the phosphorylated isoform was each decreased in the mutant ( Fig 3 ) . We further addressed the phosphorylation status of the kinase Akt1 , which is at the point of intersection between InR and TOR signaling cascades , activating the latter ( for review [2] , [8] ) . Interestingly , phosphorylation levels of Akt1 were also reduced in the cycG mutants ( Fig 3 and S4A Fig ) , and rescued to normal in the hs-CycG background at ambient temperature , which displayed normal levels on its own ( S4A Fig ) . Due to its impact on energy homeostasis and cell growth , loss of TOR in Drosophila affects multiple tissues and organs . For example , TOR mutants display characteristic defects in endoreplication , e . g . of cells in the salivary glands , accompanied by a reduction of Cyclin E levels that regulate G1/S phase entry in mitotic and endoreplicative tissues [20] . In fact , the salivary glands and their polytene nuclei were smaller in cycGHR7 mutant larvae compared to control , indicative of a reduced ploidy ( S4B–S4D Fig ) . In addition , we also observed a reduced level of Cyclin E protein in the cycGHR7 mutant compared to wild type larvae of the same developmental stage ( S4E Fig ) . In summary , cycG mutants phenocopy a reduced TORC1 activity , which can be explained at the molecular level by a requirement of CycG for full Akt1 activity , and hence , TOR activation . TORC1 activity in the larval fat body is required for the release of Drosophila Insulin-like peptides ( dILPs ) from specialized neurosecretory cells in the brain called insulin-producing cells ( IPCs ) [4] . Four of the eight known dILP genes ( dILP1 , 2 , 3 and 5 ) are specifically expressed in IPCs [3] , [5] . Interestingly , only IPC-derived transcription of dILP3 and dILP5 is sensitive to food deprivation , whereas dILP2 transcription remains unchanged [36] . Expression of dILP2 and dILP5 was monitored in the brains of cycGHR7 mutant third instar larvae under fed conditions . In contrast to dILP2 ( Fig 4A–4C ) , dILP5 expression was reduced in the cycGHR7 mutant comparable to the level observed in starved control animals ( Fig 4A'-4C' ) . Moreover , it was reported that the production and secretion of dILP2 and dILP5 peptides are also controlled post-transcriptionally by nutritional inputs [4] . Under normal fed conditions , both proteins are evenly distributed within the IPC cell body and its axons . Upon starvation , a strong accumulation of dILP protein is observed in the IPC cell body and the axonal termini [4] ( see also Fig 4D and 4E for controls ) . Although this analysis does not allow discriminating between insulin production and secretion , it does reveal changes in insulin dynamics [37] . In accordance with the starvation phenotype of the cycG mutants , we detected a strong increase in dILP2 protein labeling similar to that of the IPCs in starved wild type larval brains ( compare Fig 4E and 4F ) . Compared to well-fed larvae , the signal intensity was nearly doubled in starved wild type larvae as well as in well-fed cycGHR7 mutant larvae ( Fig 4G ) . Ablation of dILP producing cells is correlated with a reduced egg production , linking dILP activity directly to fertility [38] . As expected by the perturbed dILP2 abundance in IPCs , cycGHR7 mutant females laid significantly less eggs per day , reaching only two thirds of control females ( S4F Fig ) . This phenotype was normalized in the hs-CycG background at ambient temperature ( S4F Fig ) . Together , these observations indicate that CycG is required for the endocrine control of dILP production or secretion that is regulated by TOR activity . As the growth and metabolic deficits in cycG mutants reveal an impairment of InR/TORC1 signaling activity , we tested the potential role of CycG in this network . One of the first effectors of the activated InR is PI3K92E whose activity was monitored with the tGPH reporter in cycG larval tissues [39] . This reporter expresses GFP fused to a pleckstrin-homology domain ( GPH ) that is recruited to the plasma membrane upon PI3K activation [39] . We compared fat body cells from early third instar wild type larvae under fed and starved conditions with those of fed cycGHR7 mutant larvae ( Fig 5A ) . In agreement with the published data , tGPH accumulated little along the fat body cell membranes in starved larvae ( Fig 5A ) [39] . In the cycGHR7 mutants , the tGPH reporter highlighted the membranes as strongly as in the well-fed wild type , indicating a normal threshold of PI3K92E activity in the absence of CycG ( Fig 5A ) . The next factor downstream of PI3K , Akt1 shows a reduced phosphorylation level in cycGHR7 mutants ( Fig 3 ) . We hence studied the ability of Akt1 to rescue the growth defects of the cycG mutant by genetic epistasis experiments . To this end , Akt1 was specifically induced in the larval fat body of cycGHR7 mutant larvae , resulting in normal weight animals ( Fig 5B ) . Furthermore , lipid droplet accumulation in oenocytes of cycGHR7 mutant larvae was considerably improved by the fat body specific expression of Akt1 , commuting to a more wild type level ( Fig 5C and S3 Fig ) . These data show that activation of InR/TORC1 signaling at the level of Akt1 is sufficient to counteract the starvation phenotype of cycG mutants . Drosophila Widerborst ( Wdb ) is a B' subunit of the protein phosphatase 2A ( PP2A ) ( for review: [40] ) . Wdb acts as a negative regulator in the InR/TOR network by targeting PP2A to dephosphorylate Akt1 [18] . Both mammalian CycG homologues , CycG1 and CycG2 , interact directly with several B' subunits of PP2A , acting as specificity factors [41] , [42] . A likewise direct molecular interaction of CycG and Wdb has been predicted in Drosophila [27] , [28] , which we confirmed in a yeast two-hybrid assay , showing that it involves the conserved cyclin domains ( Fig 6A ) . Moreover , Wdb and CycG were co-precipitated from embryonic extracts , indicating in vivo complexes including the two proteins ( Fig 6B and S5A Fig ) . To determine whether the growth and metabolic defects observed in cycG mutants might be due to a deregulated PP2A activity , we first assayed the size and weight of wdb cycG double mutant larvae and adults . To this end , the two wdb alleles wdb14 and wdbdw were used , which are lethal in homozygosis but viable in the trans combination [43] . Each allele was recombined with the cycGHR7 allele to generate the double mutant heteroallelic combination . We found that wdb cycG double mutant larvae and adults showed a nearly wild type size and weight ( Fig 6C and 6D and S5B and S5B' Fig ) . Accordingly , lipid storage defects of the wdb cycG double mutant larvae were likewise normalized , i . e . TAG-levels , specific weight and lipid droplet accumulation in the oenocytes were similar to wild type ( Fig 6E and 6F and S5C and S5C' Fig ) . Finally , the abundance of phosphorylated Akt1 in the wdb cycG double mutants was similar to the control and no longer diminished compared to the cycGHR7 homozygotes ( Fig 6G ) . The remarkably diverse and cell type specific functions of Akt1 in the context of InR/TOR signaling have recently been attributed to the existence of different subcellular pools of activated Akt1 kinase that control different cellular processes [44] . For example , whereas activated Akt1 is predominantly found at the apical membrane of Drosophila eye tissue , it is mostly cytoplasmic in the Drosophila ovary , where it regulates the lipid metabolism in nurse and follicle cells [18] , [44] , [45] . This specific ovarian function of Akt1 is under the control of Wdb , selectively modulating the levels of cytoplasmic phosphorylated Akt1 and thereby lipid droplet size in ovarian cells [18] . Accordingly , Wdb and Akt1 physically interact in the ovary , whereas no interaction was observed in larval tissue [18] . Because both larval and adult phenotypes of cycG mutants depend on Wdb activity ( Fig 6 and S5 Fig ) , we asked whether CycG may influence the physical interaction of Wdb and Akt1 . Indeed we observed a robust interaction of Akt1 and Wdb in head extracts from cycGHR7 mutant animals in contrast to control animals ( Fig 7A ) , implying an involvement of CycG in the regulation of Akt1/Wdb binding . Both protein species of Wdb co-precipitated with Akt1 ( Fig 7A ) , indicating that the binding of Akt1 is not restricted to the higher molecular weight species of Wdb as previously reported [18] . Accordingly , no influence of CycG on the relative abundance of the two protein species of Wdb was detected comparing wild type and cycGHR7 protein extracts ( Fig 7B ) . Overall our data point to a causal link between CycG and PP2A activity in the regulation of growth and metabolism in Drosophila at the level of Akt1 ( Fig 8 ) : the presence of CycG may disfavor binding of PP2A-B’ to Akt1 , which is facilitated in its absence , resulting in a decrease of phosphorylated , i . e . activated Akt1 .
Drosophila CycG appears to have extraordinarily diverse roles . It has been involved in epigenetic regulation of homeotic gene activity , in cell cycle regulation , developmental stability and in DNA repair [24] , [26] , [46] , [47] , and now also in metabolic homeostasis . Our current work confirmed molecular interactions between CycG and Wdb proteins in vivo that had been predicted from genome-wide proteome analyses in vitro [27] , [28] . Interestingly , similar molecular interactions have been described before for mammalian CycG1 and CycG2: both proteins interact with several B' subunits , thereby mediating the recruitment of PP2A to its different substrates [41] , [42] . In contrast to mammals , the genetic relationship between CycG and PP2A is antagonistic in Drosophila as a reduction of PP2A activity ameliorates the consequences of CycG loss . The cycG mutation could be formally explained by a gain of PP2A activity . It is tempting to speculate that the diversity of CycG functions results from a regulation of PP2A by CycG . PP2A affects a plethora of developmental and cellular processes , hence , pleiotropy is expected in case of its misregulation ( for review [40] , [48] ) . Most likely , this hypothesis is too simplified . For example , loss of cycG in the female germ line results in an increase of phosphorylated H2Av ( gamma-H2Av ) [24] , a known target of PP2A activity [49] . One might have expected a reduced amount of gamma-H2Av if loss of CycG equated with a gain in PP2A activity . Instead , we have shown that CycG is found in a protein complex together with Rad9 and BRCA2 that primarily acts in the sensing of DNA double strand breaks [24] . The importance of Drosophila CycG in DNA double strand break repair is reminiscent of functions described for mammalian CycG proteins: albeit CycG1 and CycG2 mutant mice are viable and healthy , they are both sensitive to DNA damaging reagents [50] , [51] . Moreover , upregulation of CycG2 was involved in the activation of Chk2 and in damage induced G2/M cell cycle arrest , i . e . in DNA damage response in mammals as well [51] . Whether the other phenotypes and interactions reported for Drosophila CycG are linked to the regulation of PP2A remains to be addressed in more detail . The cycG mutants display several phenotypic characteristics of a diminished TORC1 signaling activity [20] , [33] , including weight reduction , a reduced egg laying rate , impaired endoreplication and a general increase in lipid mobilization . Moreover , CycG activity promotes phosphorylation of the primary TORC1 targets , i . e . S6K and 4E-BP . In contrast to TOR mutants , however , cycG mutants are viable , implying that CycG facilitates InR/TOR signaling rather than being an essential factor . Overall , cycG mutant flies show typical signs of nutritional starvation distress even under normal food conditions , suggesting a problem in their capacity to take up food and/or to sense and utilize the food . This defect is not due to a general inability of the animal to grasp the feed , but instead reflects a defect in coordinating the energy status with the regulation of systemic growth . As dILP accumulation in the brain is altered in cycG mutants , we know that the signals transmitted from the nutritional sensor fat body must be disturbed . The fact , that we can strongly ameliorate the growth defects of cycG mutants by an induction of Akt1 specifically in the fat body rules out a function of CycG in the endocrine signal emanating from the fat body . Instead , all of our data indicate that CycG acts genetically at the level of Akt1 , thereby controlling TOR signaling activity ( Fig 8 ) . Akt1 is negatively regulated by PP2A ( for review [48] ) , supporting a model whereby CycG exerts its positive input on Akt1 via an inhibition of PP2A . In accordance , mutations in wdb efficiently rescue the growth and metabolic defects observed in cycG mutants . Likewise a downregulation of Wdb ameliorates the weight deficits resulting from a loss of Akt1 activity [18] . In Drosophila , Wdb acts as a tissue-specific negative regulator of Akt1: it modulates lipid metabolism in the ovary as a result of a direct interaction with Akt1 , whereas no such influence was seen in eye tissue [18] . We have shown that Wdb-Akt1 binding in the adult head is favored in the absence of CycG , i . e . CycG is able to influence the interaction between Wdb and Akt1 presumably by its direct binding to Wdb . A consequence of CycG loss may be the enhanced binding of PP2A to Akt1 and an enforced dephosphorylation of Akt1 , resulting in the inhibition of downstream TOR signaling activity and affecting lipid metabolism and growth ( Fig 8 ) . Moreover , the second B'-subunit of Drosophila PP2A ( also called Well rounded , Wrd ) is involved in the negative regulation of the S6K [15] . Assuming a molecular interaction of Wrd and CycG , a likewise regulatory input of CycG on PP2A containing the Wrd B'-subunit is conceivable . In this case , CycG might influence S6K activity as well , having a regulatory input on InR/TOR signaling also downstream of TORC1 . This scenario is complicated by the negative feed back regulation of InR signaling by S6K and of Akt1 by TORC1 [52] . Circular regulation of InR/TOR signaling has been described at several levels , implementing a tight control of dietary signals and growth but complicating genetic analyses [52] ( for review [53] ) . In conclusion , the identification of CycG as a novel regulator of InR/TOR signaling in Drosophila highlights the importance of studying the regulatory network at the Akt1—PP2A nexus . Based on the high conservation of the InR/TOR signaling pathway and its regulation by PP2A , mammalian fat homeostasis is likely to involve similar regulatory control mechanisms to those we have uncovered in Drosophila . Our work raises the possibility of an involvement of CycG in InR/TOR-associated diseases that might be modulated by PP2A . A better understanding of the underlying mechanisms could therefore open up avenues for new strategies to fight InR/TOR-associated disorders in the future .
The cycGHR7 null allele and the pUASp-cycG transgene have been previously described [24] . The generation and verification of the cycGeoC allele is described in the supplementary experimental procedures . To generate the hs-CycG construct , cycG cDNA was cloned as 2 . 4 kb EcoRI/KpnI fragment into the pCasper-hsRX vector [54] and several independent lines were established by P-element mediated germline transformation [55] . For rescue assays an insertion on the second chromosome was used and combined with the cycGHR7 mutant . Fat metabolism and growth defects were analyzed with the following fly strains ( BL strains were from Bloomington stock center ) : Adh-Gal4 ( gift from R . Kühnlein ) [39] ) , da-Gal4 ( BL8641 ) , Lsp2-Gal4 ( BL6357 ) ; UAS-Akt1 ( BL8191 ) , UAS-GFP ( BL4776 ) , UAS-lacZ ( BL8529 ) , UAS-TSC1 UAS-TSC2 [56] , the tGPH-reporter ( gift from B . Edgar ) [39] and the wdb mutant alleles wdb14/TM6B and wdbdw/TM6B ( both obtained from C . Wilson ) [18] , [43] . Oregon-R was used as wild type control . Flies were raised at 25°C under non-crowded conditions on standard cornmeal/molasses/agar medium . For amino acid deprivation , third instar larvae were kept on a sugar only diet [20% sucrose , 1% agar in PBS] for 14 hours . The Flp/FRT system was used to generate cycGHR7 mutant clones by mitotic recombination [30] . To this end cycGHR7 was recombined with the FRT82B bearing chromosome ( BL2050 ) . Females of the genotype yw hsFlp; FRT82B cycG/TM6B were mated with FRT82B arm-lacZ/TM6C ( BL7369 ) males . 24–36 hours after egg laying , the animals were subjected to a 30 minute heat shock at 37°C to induce recombination with low frequency . Control clones were generated in parallel with only the FRT82B bearing chromosome . Mutant cell clones are characterized by the loss of lacZ and were analyzed in imaginal discs of third instar larvae . A total number of 30 discs was assayed and compared with control clones . Adult males , three to five days old , were used for analysis . Dehydrated wings were mounted in Euparal ( Roth; Karlsruhe , Germany ) . Wing area was measured using Image J software ( oval selection for total wing size in two measurements that were sampled ) . Cell number was determined by counting the individual trichoma on the wing blade in three defined 10 . 000 μm2 squares localized in the L4/L5 field next to the posterior cross vein . Subsequently , the total cell number for the determined wing area was calculated , as was cell size . Pictures were assembled using Corel Draw and Corel Photo Paint . Flies and wings were photographed with an ES120 camera ( Optronics; Goleta CA , USA ) using Pixera Viewfinder software , version 2 . 0 . To investigate developmental timing , offspring of six parallel inter se crosses with the genotype w1118; cycGHR7/+ was counted at days 9 to 20 . Since the cycGHR7 mutant carries a mini-white+ gene [24] , the homozygous cycGHR7 and the heterozygous cycGHR7/+ flies could be distinguished by red and orange eye color , respectively , from the white-eyed control siblings . As an assay for ingestion , blue-colored yeast paste was offered to larvae as a food source . Food uptake in the gut was visualized by illuminating larvae from the side and taking pictures with a Pixera camera coupled to a Leica stereo-microscope . For the starvation assay , triplicate batches of 15 three days old males of each genotype were transferred to vials containing 1% agarose in PBS only ( wet starvation ) . Mortality rate was determined by counting the number of dead flies every two hours . Third instar larvae were likewise amino acid deprived for two days before dissection . Newly hatched male flies were transferred to fresh food vials and maintained at 25°C for three days before measurement . Body weight of 100 flies of each genotype was measured individually with a precision scale . Organismal triacylglycerol ( TAG ) and protein content was quantified using the Pierce BCA Protein determination Kit ( Thermo Fisher Scientific; Rockford IL , USA ) and the protocol procedure B of the Triglycerid-Assay Kit ( Sigma-Aldrich; St . Louis MO , USA ) . For each genotype , batches of five third instar larvae were homogenized in 150 μl 0 . 01% Tween in PBS . After incubation at 70°C for 5 minutes the samples were centrifuged for 1 minute at 5000 rpm . The supernatant was transferred to a fresh microtube and after additional centrifugation for 3 minutes at 14000 rpm the cleared lysate was applied in the appropriate assay . A minimum of three independent experiments was performed for each genotype and the results were sampled . A simplified version of the buoyancy-based screen protocol was used [32]: 10 larvae of each genotype were placed in 3 ml of 10% sucrose solution . After gentle mixing and five minutes without agitation , the number of larvae floating at the surface was counted and documented . Statistical significance of probes was determined according to Student's T-test ( http://www . physics . csbsju . edu/stats/t-test . html ) and p-value was scaled accordingly: p>0 . 05 ( not significant , n . s . ) ; p<0 . 05 ( weakly significant; * ) ; p<0 . 01 ( significant; ** ) ; p<0 . 001 ( highly significant; *** ) . Larval brains were dissected in PBS and fixed for 20 minutes in 4% paraformaldehyde . After several washes with PBS plus 0 . 3% Triton X100 followed by a preincubation step with 4% normal goat serum , rabbit anti-dILP2 antibody ( 1:800; gift from P . Léopold ) [4] was added and incubated over night at 4°C . Imaginal discs were likewise treated , and stained with anti beta-Galactosidase antibodies ( 1:50; DSHB; Iowa , USA ) . Secondary antibodies coupled to DTAF or Cy3 were purchased from Jackson ImmunoResearch ( Dianova; Hamburg , Germany ) . The Oil Red O staining of oenocytes was performed exactly as described previously [33] . Larval fat body was dissected from well fed or amino acid deprived third instar larvae in PBS , fixed in 4% paraformaldehyde for 15 min and stained with Nile Red ( Sigma-Aldrich; St . Louis MO , USA ) at a concentration of 10 μg/ml . As a measurement of PI3K92E activity in vivo , the tGPH reporter [39] was used and the degree of membrane tGPH localization analyzed in early third larval tissues by confocal microscopy . Fluorescently labeled tissues were mounted in Vectashield . Larval brains were analyzed with a Zeiss-ApoTome Axio Imager using AxioVision Software . Imaginal discs and fat bodies were documented with a Bio-Rad MRC1024 confocal microscope coupled to a Zeiss Axiophot using Laser Sharp 2000 software ( Carl Zeiss AG; Oberkochen , Germany ) . Pictures of fat bodies were inversed for better visibility . In situ hybridization on larval brains was performed with digoxygenin labeled DNA probes of dILP2 and dILP5 according to standard protocols [57] . The templates for the probes were generated by PCR using the following primer sets: UP dILP2: GAT CGT AAA GCA ACC TAA GCA GTA A; LP dILP2: ATT CGT AAA GAG TAA CAT GCA ACA A; UP dILP5: GAT CCC AGT TCT CCT GTT CCT GAT C; LP dILP5: TTT CAA GTT TCA AAG CCG TGC ATA T . For each genotype 100 adult heads were homogenized in RIPA I buffer [50 mM Tris-HCl pH 7 . 5 , 150 mM NaCl , 1% Triton X-100 , 0 . 1% SDS , protease inhibitor cocktail ( Roche Diagnostic; Basel , Switzerland ) ] on ice . After centrifugation , loading buffer was added to the supernatant and the probes were loaded on a SDS-PAGE followed by Western blotting . The extracts were probed with guinea pig anti-CycG ( 1:400 ) [46] . As loading controls we used anti-beta-Tubulin ( 1:50 ) ( E7 DSHB; developed by M . Klymkowsky ) and anti-Erk1/2 antibodies ( 1:1000 ) ( Cell Signaling Technology; Danvers MA , USA ) , as tubulin levels might change when InR signaling is influenced [15] . The amount of total and phosphorylated protein was determined with rabbit anti-4E-BP ( 1:100 , gift from G . Tettweiler ) [58] , rabbit anti-Akt1 ( 1:250 ) , rabbit anti-p70 S6 kinase ( 1:100 ) , rabbit anti-Phospho-Akt ( 1:250 ) , rabbit anti-Phospho-p70 S6 kinase ( 1:100 ) and rabbit anti-Phospho-4E-BP ( 1:100 ) ( all from Cell Signaling Technology; Danvers MA , USA ) . Full length wdb was PCR amplified from cDNA ( LD34343 , obtained from DGRC , Bloomington IN , USA ) and cloned as BglII/NotI fragment into pEG and VP16 ( BamHI/NotI ) vectors . pJG-CycG ( 1–566 ) , GST-CycG ( 1–215 ) and GST-CycG ( 215–566 ) DNA was a gift from F . Peronnet , France [46] . The CycG subdivision constructs were PCR-amplified using the pJG/GST-CycG constructs as template and further subcloned as EcoRI/XhoI fragments in either pEG , pJG or VP16 vectors [59] , [60] . Protein-protein interaction assays were done according to standard protocols using the Brent two-hybrid system [59] . Protein expression in yeast cells ( EGY40: Mata , ura3 , his3 , trp1 , leu2 , GAL ) was verified either with mouse anti-HA ( 1:1000; St . Louis MO , USA ) , mouse anti-VP16 ( 1:100; Santa Cruz Biotechnology , Dallas , USA ) or rabbit anti-LexA antibodies ( 1:1000; Bio Acadamia , Osaka , Japan ) . CycG or Wdb protein was immuno-precipitated from about 500 embryos ( 0–24h ) using either anti-CycG antibodies or anti-Wdb antibodies ( see supporting materials and methods ) as described before [46] . Akt1 and Wdb complexes were co-immunoprecipitated from 150 heads each of either wild type or cycGHR7 homozygous mutant animals using rabbit anti-Akt1 ( 1:50; Cell Signaling Technology; Danvers MA , USA ) and detected with rabbit anti-Akt1 or rat anti-Wdb ( see supporting materials and methods ) .
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Size and growth of an organism are adjusted to nutritional conditions by a complex regulatory network involving the Insulin receptor and TOR signaling cascades . Drosophila melanogaster has been used in the past as a genetically tractable model to unravel the complex circuitry by genetic means . We have identified CycG as an important player in the regulation of TOR signaling . CycG mutants are underweight in the midst of food and show typical signs of TOR defects . We provide evidence that CycG acts at the level of Akt1 kinase that links the Insulin receptor and TOR signaling cascades . Molecular and genetic data point to an interplay of CycG and phosphatase PP2A , a well established negative regulator of Akt1 activity . Moreover , CycG may influence PP2A-Akt1 binding . We propose that CycG , by impeding PP2A-Akt1 interaction , acts as a positive regulator of growth in Drosophila .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
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Cyclin G Functions as a Positive Regulator of Growth and Metabolism in Drosophila
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In response to misaligned sister chromatids during mitosis , the spindle checkpoint protein Mad2 inhibits the anaphase-promoting complex or cyclosome ( APC/C ) through binding to its mitotic activator Cdc20 , thus delaying anaphase onset . Mad1 , an upstream regulator of Mad2 , forms a tight core complex with Mad2 and facilitates Mad2 binding to Cdc20 . In the absence of its binding proteins , free Mad2 has two natively folded conformers , termed N1-Mad2/open-Mad2 ( O-Mad2 ) and N2-Mad2/closed Mad2 ( C-Mad2 ) , with C-Mad2 being more active in APC/CCdc20 inhibition . Here , we show that whereas O-Mad2 is monomeric , C-Mad2 forms either symmetric C-Mad2–C-Mad2 ( C–C ) or asymmetric O-Mad2–C-Mad2 ( O–C ) dimers . We also report the crystal structure of the symmetric C–C Mad2 dimer , revealing the basis for the ability of unliganded C-Mad2 , but not O-Mad2 or liganded C-Mad2 , to form symmetric dimers . A Mad2 mutant that predominantly forms the C–C dimer is functional in vitro and in living cells . Finally , the Mad1–Mad2 core complex facilitates the conversion of O-Mad2 to C-Mad2 in vitro . Collectively , our results establish the existence of a symmetric Mad2 dimer and provide insights into Mad1-assisted conformational activation of Mad2 in the spindle checkpoint .
At the metaphase–anaphase transition , a multisubunit ubiquitin ligase called the anaphase-promoting complex or cyclosome ( APC/C ) in complex with its mitosis-specific activator Cdc20 mediates the ubiquitination of securin and cyclin B [1 , 2] . Degradation of securin and cyclin B activates separase , which cleaves the Scc1 subunit of cohesin and triggers sister-chromatid separation [1 , 2] . Premature sister-chromatid separation leads to aneuploidy , which contributes to cancer progression [3 , 4] . In response to the existence of sister chromatids that lack attachment of spindle microtubules at their kinetochores , a cell-cycle surveillance system called the spindle checkpoint inhibits APC/CCdc20 through multiple mechanisms , stabilizes securin and cyclin B , and delays the onset of anaphase [2 , 3 , 5] . The spindle checkpoint protein Mad2 binds directly to Cdc20 in mitosis and is essential for checkpoint-dependent inhibition of APC/C [6–8] . Binding of Mad2 to Cdc20 requires Mad1 , an upstream regulator of Mad2 that binds to Mad2 throughout the cell cycle [9–11] . Both Mad1 and Cdc20 contain similar short peptide motifs that mediate Mad2 binding [11] . Either inactivation or hyperactivation of Mad2 promotes tumorigenesis in mice [12 , 13] , highlighting the importance of proper Mad2 regulation in vivo . A series of biochemical , cell biological , and structural studies has established that Mad2 is a highly unusual two-state protein and that the Mad1-assisted conformational switch between these two states is central to Mad2 regulation [5 , 14] . In an early study , Fang , et al . [8] showed that recombinant purified Mad2 has two natively folded conformers , a monomer and a dimer , in the absence of ligand binding or covalent modification . The Mad2 dimer can form tetramers at high concentrations . The Mad2 dimer , but not the monomer , is active in APC/C inhibition in Xenopus egg extracts . Furthermore , the Mad2 monomer blocks the function of the Mad2 dimer in a dominant-negative manner . Structural studies were subsequently carried out to explain this striking two-state behavior of Mad2 . The structures of the Mad2 monomer and Mad2 in complex with either Mad1 or an unnatural peptide ligand called Mad2-binding peptide 1 ( MBP1 ) that mimics the Mad2-binding motifs of Mad1 or Cdc20 were determined [11 , 15 , 16] . These structures revealed that the Mad2 monomer has a globular domain and a flexible C-terminal tail . A Mad2 mutant with its C-terminal tail deleted ( Mad2ΔC ) is an open Mad2 ( O-Mad2 ) monomer , is incapable of binding to Cdc20 , and inhibits the activity of wild-type Mad2 in a dominant-negative manner . Mad2 undergoes a dramatic conformational change upon ligand binding . The peptide ligands are trapped by the C-terminal region of Mad2 in a manner similar to the way that passengers are restrained by the seat belts in automobiles . The Mad2 point mutant , Mad2R133A , has two distinct monomeric conformers in the absence of ligands , which allowed us to determine the structure of both natively folded conformers of Mad2R133A , termed N1-Mad2/open Mad2 ( hereafter referred to as O-Mad2 ) and N2-Mad2/closed Mad2 ( C-Mad2 ) , by nuclear magnetic resonance ( NMR ) spectroscopy [17] . ( We initially named these two conformers N1-Mad2 and N2-Mad2 . To avoid confusion , however , we have decided to adopt the nomenclature of De Antoni et al . [18] . ) The structure of unliganded C-Mad2 closely resembles that of Mad1- or Cdc20-bound C-Mad2 except that the ligand-binding site is vacant . O-Mad2 can spontaneously convert to C-Mad2 with slow kinetics ( t1/2 = 9 h at 30 °C ) [17] . Furthermore , cytosolic Mad2 in human cells is an O-Mad2 monomer [17] . Monomeric C-Mad2R133A , but not O-Mad2R133A , is active in APC/CCdc20 inhibition . In addition , O-Mad2 and C-Mad2 can form an asymmetric O-Mad2–C-Mad2 ( O–C ) dimer that is less active in APC/CCdc20 inhibition [17] , explaining why Mad2ΔC ( which only adopts the O-Mad2 conformation ) can block the activity of wild-type Mad2 in a dominant-negative manner . Finally , Mad1 facilitates the conversion of O-Mad2 to C-Mad2 in vitro [17] . Mad2 is targeted to unattached kinetochores by Mad1 and turns over rapidly at the kinetochores as revealed by fluorescence recovery after photobleaching ( FRAP ) studies [9 , 19–21] . These studies suggest that Mad1 activates Mad2 at kinetochores by facilitating the structural conversion of O-Mad2 to C-Mad2 . More recent FRAP studies revealed that only about 50% of kinetochore-bound Mad2 undergoes fast exchange with its cytosolic pool [22] , suggesting that there is a stably bound pool of Mad2 at the kinetochores . Musacchio and coworkers then showed that this stably kinetochore-bound pool of Mad2 forms a tight complex with Mad1 and adopts the C-Mad2 conformation [16 , 18] . The Mad1–Mad2 core complex recruits cytosolic O-Mad2 to kinetochores through asymmetric O–C Mad2 dimerization . All available data thus support the following main framework to explain the mechanism by which Mad1 assists the binding of Mad2 to Cdc20 ( Figure 1 ) [14 , 17 , 18 , 23–26] . In this model , Mad2 has two distinct conformations of roughly equal free energy: a latent O-Mad2 and an active C-Mad2 . The Mad1–Mad2 core complex recruits another copy of cytosolic O-Mad2 to kinetochore through O–C Mad2 dimerization . O-Mad2 bound to the Mad1–Mad2 core complex undergoes a conformational change to adopt a short-lived , high-energy intermediate conformation ( I-Mad2 ) . ( I-Mad2 was previously referred to as O*-Mad2 . To avoid confusion , we will use the unified nomenclature described in [24 , 25] . ) I-Mad2 can be directly passed onto Cdc20 from the Mad1–Mad2 core complex . Alternatively , at least a fraction of I-Mad2 converts to unliganded C-Mad2 , which dissociates from Mad1 . Because Mad1 is a homodimer , two C-Mad2 molecules dissociated from Mad1 are expected to form a symmetric C-Mad2–C-Mad2 ( C–C ) Mad2 dimer . These unliganded C-Mad2 species are more active for Cdc20 binding and APC/C inhibition . Chemical shift perturbation experiments had initially suggested that , upon binding to C-Mad2 , O-Mad2 undergoes a large conformational change to become I-Mad2 [23] . The structure of the asymmetric O-Mad2–C-Mad2 dimer has , however , revealed that O-Mad2 bound to C-Mad2 has virtually the same conformation as does free O-Mad2 [25] . Thus , I-Mad2 is not the stable conformation of O-Mad2 bound to C-Mad2 , but rather a high-energy state with a finite lifetime . The existence and nature of I-Mad2 remain to be established . In this study , we performed systematic mutagenesis studies of human Mad2 and obtained Mad2 mutants that preferably adopt the closed conformation . We determined the crystal structure of one such mutant , Mad2L13A , demonstrating unequivocally that C-Mad2 can form a symmetric C–C dimer in vitro . Using NMR spectroscopy , we showed that the wild-type Mad2 can form both an asymmetric O–C dimer and a symmetric C–C dimer . Mad2L13A , which predominantly exists as the symmetric C–C Mad2 dimer , is functional in cells and is active in APC/CCdc20 inhibition in vitro . Finally , the Mad1–Mad2 core complex enhances the conversion of O-Mad2 to C-Mad2 . These findings provide further mechanistic insights into the conformational activation of Mad2 by Mad1 in the spindle checkpoint .
We have previously shown that Mad2R133A forms monomeric O-Mad2 and C-Mad2 conformers that interconvert with slow kinetics [17] . The monomeric open and closed conformers of Mad2R133A can be separated by anion exchange chromatography at 4 °C . O-Mad2 elutes at 150 mM salt , whereas C-Mad2 elutes at 260 mM salt . Inspection of their surface electrostatic potentials reveals that C-Mad2 contains a contiguous , negatively charged patch centered around β6 that is absent in O-Mad2 because β6 is largely buried by β7 and β8 ( Figure S1 ) . The presence of this negatively charged patch provides a possible explanation for the tighter association of C-Mad2 with the positively charged resin of the anion exchange column . We performed systematic structure-based mutagenesis to identify Mad2 mutants that preferably adopt either the open or closed conformation in the background of the R133A mutation . We used the elution profiles of anion exchange chromatography and NMR spectroscopy to determine the conformational state of the Mad2 mutants and to measure of the O–C conversion rates of mutants that can form both conformers . The binding affinities of these Mad2 mutants toward the Mad2-binding motif of Cdc20 were determined by isothermal titration calorimetry ( ITC ) . The results from these studies are summarized in Table 1 . Previous studies showed that a Mad2 mutant with its C-terminal ten residues deleted ( Mad2ΔC ) exclusively adopts the open conformation and can no longer interact with Cdc20 [8 , 15 , 17] . The majority of Mad2 mutants formed both O-Mad2 and C-Mad2 conformers that interconverted with rates similar to that of Mad2R133A . However , several Mad2 mutants behaved similarly to Mad2ΔC and only adopted the open conformation , including F186A , T188A , H191A , V197A , and Y199A ( Figure 2 ) . None of these mutants had detectable binding toward Cdc20 ( Table 1 ) . In addition , we identified several Mad2 mutations that selectively destabilized the open conformation of Mad2 , such as L13A , W75A , L153A , and Y156A . These mutants preferably adopted the closed conformation ( Figure 2 ) . Among these C-Mad2-specific mutants , Mad2L13A , Mad2L153A , and Mad2Y156A retained their ability to bind to Cdc20 ( Table 1 ) , consistent with C-Mad2 being the more active species of Mad2 for Cdc20 binding . Because W75 is located in the ligand-binding site of Mad2 , Mad2W75A does not bind to Cdc20 ( Figure 2 ) . Because the Mad2L13A , R133A double mutant exclusively adopts the monomeric C-Mad2 conformation , we next introduced the L13A mutation into the wild-type Mad2 ( Mad2WT ) to obtain a symmetric C–C Mad2 dimer . C79 and C106 of Mad2 are located in close proximity and tend to form an intramolecular disulfide bond , causing conformational heterogeneity . To facilitate crystallization , we created a Mad2L13A , C79S , C106S triple mutant , which retained its abilities to bind to Cdc20 and inhibit APC/C in vitro ( see below ) . For simplicity , we will hereafter refer to this triple mutant as Mad2L13A . We next fractionated both Mad2WT and Mad2L13A on an anion exchange column ( Figure S2A ) . Similar to Mad2R133A , Mad2WT eluted in two well-resolved peaks ( Q1 and Q2 ) , which were further fractionated on a gel filtration column . Mad2WT in the low-salt peak ( Q1 ) was monomeric , whereas Mad2 in the high-salt peak ( Q2 ) eluted on the gel filtration column with an apparent molecular mass of about 50 kDa , consistent with it being a dimer ( Figure S2B ) . NMR studies further confirmed that the Mad2WT monomer had the O-Mad2 conformation , and at least one copy of Mad2 in the dimer had the C-Mad2 conformation [17] . In contrast to Mad2WT , Mad2L13A eluted as a single high-salt peak on an anion-exchange column ( Figure S2A ) . Mad2L13A in this peak eluted as a dimer from a gel filtration column ( Figure S2B ) . We next used 2D 1H-15N transverse-relaxation optimized heteronuclear single quantum coherence spectroscopy ( TROSY-HSQC ) to further characterize the conformational state of Mad2L13A . The peaks in the HSQC spectrum of Mad2L13A largely overlap with those in the HSQC spectrum of C-Mad2R133A , indicating that Mad2L13A has the C-Mad2 conformation ( unpublished data ) . The HSQC spectrum of the 205-residue Mad2L13A protein has only about 190 backbone peaks , consistent with each backbone amide group of Mad2L13A having a single peak . Thus , the column fractionation profiles and the TROSY-HSQC spectrum of Mad2L13A suggest that Mad2L13A forms a symmetric C–C dimer . We next used equilibrium sedimentation to determine the native molecular mass of Mad2L13A and to measure its self-association affinity ( Figure S2C ) . After fitting the data to a single ideal species , we obtained a molecular mass of 43 . 5 kDa , which was about twice the predicted molecular mass of Mad2L13A ( 23 . 5 kDa ) . Fitting the data to a monomer-dimer equilibrium model yielded a dissociation constant ( Kd ) of 0 . 25 μM for the Mad2L13A dimer . Thus , Mad2L13A forms a stable symmetric dimer with relatively high affinity . Our extensive efforts to crystallize the Mad2WT dimer failed , likely due to its conformational heterogeneity . However , we obtained crystals of Mad2L13A that diffracted X-rays to a minimum Bragg spacing of 1 . 95 Å and determined its structure using molecular replacement . Data collection and refinement statistics are listed in Table 2 . Both monomers in the Mad2L13A dimer adopt the C-Mad2 conformation and are related by noncrystallographic , two-fold symmetry ( Figures 3A–3C and S3 ) . The two monomers mainly interact through the C-terminal halves of their αC helices . The high resolution of our structure of Mad2L13A allows clear visualization of side chains as well as several well-ordered water molecules at the dimer interface ( Figure 3D ) . The dimerization interface of Mad2L13A is symmetric and consists of residues from the C-terminal half of αC , R184 from β8′ , and Q34 at the C-terminal end of αA ( Figure 4 ) . These residues form hydrophobic interactions and extensive networks of water-mediated hydrogen bonds . For example , F141 forms intermolecular interactions with A137 , T138 , Q134 , and F141 ( Figure 4A ) . Bridged by two tightly bound water molecules , R133 from one monomer forms a network of hydrogen bonds with both the backbone and side-chain carbonyl groups of Q34 and the backbone carbonyl of T136 from the neighboring monomer ( Figure 4B ) . The interactions between the two Mad2 monomers observed in our structure are consistent with previous mutagenesis results [23] . Mutations of residues directly located at the dimer interface , including R133 , Q134 , T140 , and F141 , have been shown to disrupt Mad2 dimerization . Residues from β8′ in C-Mad2 do not form intermolecular interactions in the C–C Mad2 dimer ( Figure 4C ) . Residues in β1 in O-Mad2 do not interfere with the interactions at the dimer interface mainly involving the C-terminal end of αC . Why does O-Mad2 not form a symmetric O–O dimer using the same interface as that of the C–C dimer ? As discussed above , Q134 is a critical residue at the dimer interface . Its side chain forms an intermolecular hydrogen bond with the backbone carbonyl of T140 . The orientation of the Q134 side chain is determined by its packing with F141 from the neighboring monomer and , more importantly , by an intramolecular hydrogen bond with the backbone amide of R184 ( Figure 4D ) . In C-Mad2 , R184 is located in a β bulge and forms an electrostatic interaction with E127 on αC , thus presenting its backbone amide for hydrogen bonding with the side chain of Q134 . In O-Mad2 , R184 is located at the opposite side of the molecule . The side chain of Q134 packs against W100 and is not available for intermolecular hydrogen bonding . Thus , R184 of β8′ indirectly contributes to Mad2 dimerization by forming an intramolecular hydrogen bond with the side chain of Q134 , explaining the inability of O-Mad2 to form symmetric dimers . V197 in O-Mad2 is located in the flexible C-terminal tail , whereas it resides in β8′′ and packs against W100 in C-Mad2 [17] . As a consequence , the γ2 methyl group of V197 ( V197γ2 ) has a high-field 1H chemical shift at −0 . 34 parts per million ( ppm ) only in C-Mad2 . Hence the −0 . 34 ppm V197γ2 peak is unique to C-Mad2 . Consistent with the essential role of R184 in symmetric C–C Mad2 dimerization , Mad2R184E ( a point mutant of Mad2 with R184 mutated to glutamate in wild-type Mad2 ) adopts the monomeric C-Mad2 conformation as evidenced by its apparent molecular weight from the gel filtration chromatography and the existence of the unique V197γ2 peak at −0 . 34 ppm in the 1D NMR spectrum ( Figure 4E and 4F ) . Our previous biochemical and NMR studies have shown that the Mad2WT dimer contains at least one copy of C-Mad2 [17] . However , it is unclear whether the Mad2WT dimer is a symmetric C–C dimer , an asymmetric O–C dimer , or a mixture of both . To characterize the nature of the Mad2WT dimer , we compared its 2D 1H-13C HSQC spectrum with those of the symmetric C–C Mad2L13A dimer and an asymmetric O–C Mad2 dimer ( Figure 5 ) . As discussed above , the −0 . 34 ppm V197γ2 peak is unique to C-Mad2 . The symmetric C–C and asymmetric O–C dimers each contain a single V197γ2 peak at −0 . 34 ppm . However , the V197γ2 peak in the C–C Mad2 dimer has a higher-field 13C chemical shift as compared to that in the O–C Mad2 dimer . The Mad2WT dimer has two peaks for V197γ2 , with an intensity ratio of about 1:3 ( Figure 5A ) . The stronger peak overlays well with the V197γ2 peak in the O–C Mad2 dimer , whereas the weaker peak corresponds to the V197γ2 peak in the C–C Mad2 dimer ( Figure 5D ) . Both methyl groups of I128 in the Mad2WT dimer also have two sets of peaks that overlay well with those of the C–C and O–C dimers ( Figure 5 ) . Thus , the Mad2WT dimer contains a mixture of symmetric C–C and asymmetric O–C dimers with a molar ratio of about 1:3 . We next compared the APC/CCdc20-inhibitory activities of Mad2L13A , untagged dimeric Mad2WT , and His6-tagged dimeric Mad2WT using an in vitro reconstituted APC/C ubiquitination assay ( Figures 6A and S4 ) . Addition of Mad2 to the preformed APC/CCdc20 complex failed to inhibit its activity ( unpublished data ) . Thus , to observe the APC/CCdc20-inhibitory activity of Mad2 , we needed to preincubate Mad2 and Cdc20 before the addition of APC/C . When Mad2 and Cdc20 were preincubated for 2 h prior to APC/C addition , Mad2WT and Mad2L13A inhibited APC/CCdc20 with similar potency , with Mad2L13A being slightly more active ( Figure S4 ) . Both dimeric untagged and His6-tagged Mad2WT behaved similarly in this assay . As a control , Mad2ΔC , which lost its ability to bind to Cdc20 , had no effect on the activity of APC/CCdc20 ( Figure S4 ) . In contrast , when Mad2 and Cdc20 were preincubated for only 30 min prior to their addition to APC/C , Mad2L13A inhibited APC/CCdc20 about 3-fold more potently than did Mad2WT ( Figure 6 ) . Therefore , at equilibrium , Mad2WT and Mad2L13A are equally efficient inhibitors of APC/CCdc20 . The fact that Mad2L13A inhibits APC/CCdc20 more efficiently than Mad2WT with a shorter preincubation suggests that Mad2L13A has a faster on-rate in Cdc20 binding . Because the majority of dimeric Mad2WT forms the asymmetric O–C dimer , whereas Mad2L13A predominantly forms the symmetric C–C dimer , this finding further suggests that C-Mad2 is more active in APC/CCdc20 inhibition in vitro . Overexpression of Mad2 causes mitotic arrest in human cells [17] . We next transfected HeLa cells with a control vector or plasmids encoding untagged Mad2WT or Mad2L13A . Despite being expressed at slightly lower levels ( Figure 6B ) , Mad2L13A consistently caused a higher percentage of cells to arrest in mitosis than did Mad2WT ( Figure 6C ) . Therefore , as compared to Mad2WT , Mad2L13A is more efficient in eliciting mitotic arrest in living cells . Mad2L13A is thus a gain-of-function mutant , suggesting that C-Mad2 is more active than O-Mad2 in APC/CCdc20 inhibition . The ability of Mad2L13A to more effectively titrate p31comet might also contribute to its higher activity in living cells [27] . Vink et al . [28] have recently shown that the in vitro turnover of O-Mad2 bound to purified Mad1–Mad2 core complex has kinetics similar to that of Mad2 turnover at unattached kinetochores in vivo . Thus , the Mad1–Mad2 core complex is the minimal component required for Mad2 turnover and activation at kinetochores . Furthermore , addition of Cdc20 does not appreciably alter the rate of Mad2 turnover on the Mad1–Mad2 core complex , suggesting that Cdc20 binding is not required for the release of Mad2 from the Mad1–Mad2 core complex [28] . However , the conformational state of Mad2 released from the Mad1–Mad2 core complex is unknown . To address this question , we reconstituted Mad2 activation by the Mad1–Mad2 core complex using purified recombinant proteins in solution . We assembled the Mad1–Mad2 core complex by mixing His6-Mad2 and the C-terminal fragment of Mad1 ( residues 495–718 ) . As a control , we also assembled a Mad1–Mad2 core complex that contained the His6-Mad2R133E , Q134A mutant incapable of forming O–C Mad2 dimers . We then incubated untagged 13C-labeled O-Mad2 with the Mad1–His6-Mad2 or Mad1–His6-Mad2R133E , Q134A core complexes at a molar ratio of 4:1 for 30 min at 37 °C . The use of both His6-tagged and untagged Mad2 allowed us to distinguish , using SDS-PAGE , the Mad2 molecule in the Mad1–Mad2 core complexes from the free O-Mad2 that turned over on the Mad1–Mad2 core complex . The reaction mixtures were then fractionated by gel filtration chromatography at 4 °C , and the fractions were analyzed using Coomassie blue–stained SDS-PAGE ( Figures 7 and S5 ) . In the absence of the Mad1–Mad2 core complex , about 60% of O-Mad2 remained as monomer while 40% of Mad2 formed dimers ( Figure 7A ) . 1H-13C HSQC spectra confirmed that the Mad2 monomer adopted the O-Mad2 conformation and that the Mad2 dimer contained a mixture of O–C and C–C Mad2 dimers at a molar ratio of 3:1 , as described above ( unpublished data ) . Thus , about 25% of O-Mad2 molecules spontaneously converted to C-Mad2 during the course of the experiment . In the presence of the Mad1–His6-Mad2 core complex , about 10% of Mad2 remained bound to the Mad1–Mad2 core complex , while virtually all free Mad2 formed dimers ( Figure 7B ) . Consistent with previous findings , we did not observe substantial dissociation of His6-Mad2 from the Mad1–Mad2 core complex . The Mad2 dimer again contained a mixture of O–C and C–C Mad2 dimers at a 3:1 ratio based on 1H-13C HSQC spectra , indicating that about 60% of O-Mad2 converted to C-Mad2 in the presence of the Mad1–Mad2 core complex . In contrast , addition of the Mad1–His6-Mad2R133E , Q134A complex that lost its ability to recruit another copy of O-Mad2 did not appreciably change the rate of conversion from O-Mad2 to C-Mad2 ( Figure 7C ) . Thus , the Mad1–Mad2 core complex promotes the conversion of O-Mad2 to C-Mad2 through O–C Mad2 dimerization . A substantial fraction of Mad2 dissociated from the Mad1–Mad2 core complex adopts the C-Mad2 conformation . We note that because of the absence of Cdc20 in our assays , unliganded C-Mad2 accumulated to high concentrations and dimerized with a pool of O-Mad2 , preventing this pool of O-Mad2 from interacting with the Mad1–Mad2 core complex . In cells , unliganded C-Mad2 is expected to bind to Cdc20 and is unlikely to accumulate to high enough concentrations to compete with the Mad1–Mad2 core complex for O-Mad2 . Nevertheless , our results indicate that in the absence of Cdc20 , O-Mad2 bound to the Mad1–Mad2 core complex can complete the open-to-closed rearrangement and dissociate from the Mad1–Mad2 core complex as unliganded C-Mad2 . The Mad1–Mad2 core complex recruits O-Mad2 and converts it to C-Mad2 . How is C-Mad2 released from the Mad1–Mad2 core complex after the conversion ? Mapelli et al . [25] recently determined the crystal structure of the asymmetric O-Mad2–C-Mad2 dimer . We thus superposed C-Mad2 onto O-Mad2 in the O-Mad2–C-Mad2 dimer ( Figure 8A ) . As described above , a major difference between the fold of O-Mad2 and C-Mad2 is the translocation of the C-terminal region from one side of the molecule to the other , forming the β8′/8′′ hairpin that pairs with β5 in C-Mad2 . To accommodate this β hairpin and avoid steric clashes , αC in C-Mad2 needs to rotate slightly , which in turn causes a rotation of the β2/3 hairpin . Consequently , in our structural model , αC of C-Mad2 superposed with O-Mad2 develops steric clashes with β8′ and αA of the original C-Mad2 molecule in the O-Mad2–C-Mad2 dimer ( Figure 8A ) . Thus , C-Mad2 cannot bind to another copy of C-Mad2 using the asymmetric O-Mad2–C-Mad2 dimerization interface . Conversion of O-Mad2 to C-Mad2 on the Mad1–Mad2 core complex introduces steric clashes between αC of the newly formed C-Mad2 and parts of the C-Mad2 molecule in the Mad1–Mad2 core complex , enabling the release of the newly converted C-Mad2 . On the other hand , excluding the ligand-binding site , the structures of unliganded C-Mad2 and Mad1-bound C-Mad2 are highly similar , with a backbone root mean square deviation ( RMSD ) of 1 . 1 Å . Furthermore , the ligand-binding site and the symmetric dimerization interface of Mad2 are located on opposite sides of the protein . Why then is C-Mad2 incapable of rebinding to the Mad1–Mad2 core using the symmetric C-Mad2–C-Mad2 interface ? A superposition of unliganded C-Mad2 and Mad1-bound C-Mad2 reveals a structural difference in the C-terminal end of their αC helices ( Figure 8B ) . Residues 135–141 in αC adopt an irregular helical conformation in unliganded C-Mad2 , whereas they adopt a 310-helical conformation in Mad1-bound C-Mad2 . Because of this important difference and a difference in the rotamer conformation of F141 , the side chain of F141 points into different directions in the two C-Mad2 structures ( Figure 8B ) . In unliganded C-Mad2 , F141 points outward and engages in numerous interactions at the dimerization interface ( see Figure 4A ) . In contrast , F141 in liganded C-Mad2 points inward , forms intramolecular hydrophobic interactions with V181 and Y199 , and is unavailable to mediate dimerization ( Figure 8B ) . Mutation of F141 disrupts Mad2 dimerization [23] , confirming the essential role of this residue . Thus , ligand binding at one side of Mad2 might trigger structural changes of F141 at the other side , thereby preventing unliganded C-Mad2 from binding to liganded C-Mad2 , although we cannot rule out the possibility that the structural differences involving F141 are caused by crystal packing . We have shown that unliganded C-Mad2 is more active than O-Mad2 in APC/CCdc20 inhibition in vitro . Because O-Mad2 and unliganded C-Mad2 form the same C-Mad2–Cdc20 complex , the difference in their APC/CCdc20-inhibitory activity is likely caused by different on-rates during their binding to Cdc20 . Binding of Cdc20 to O-Mad2 is a complicated process and can be conceptually separated into four steps , not necessarily in the stated order ( Figure S6 ) . First , β8 dissociates from β6; the C-terminal region of Mad2 either retains the β7/8 hairpin or possibly rearranges into the β8′/8′′ hairpin as in C-Mad2 . Second , β1 dissociates from β5 , traverses through the β5-αC loop , and forms an additional turn in αA . Third , the Mad2-binding motif of Cdc20 forms a β strand that pairs with β6 and extends the main β sheet of Mad2 . Fourth , the β8′/8′′ hairpin wraps around Cdc20 and translocates to pair with β5 , thus trapping Cdc20 in the closed seatbelt conformation . O-Mad2 is thus an autoinhibited conformation in which β8 blocks the accessibility of β6 and , hence , ligand-binding through an intramolecular interaction . Consistent with this notion , a Mad2 deletion mutant ( Mad21–160 ) that lacks β7 , β8 , and the C-terminal tail still folds , exhibits cooperative unfolding with a melting temperature of 47 °C , and retains weak binding to MBP1 ( Figure S7 ) , possibly through the formation of edge-on interactions between β6 and MBP1 . In contrast , Mad2ΔC lacks only the C-terminal tail , but retains β7 and β8 . This mutant fails to bind to MBP1 because of the blockage of β6 by β8 ( unpublished data ) . We propose two nonexclusive models to explain why unliganded C-Mad2 is more active in APC/CCdc20 inhibition than O-Mad2 ( Figure S6 ) . In the first model ( pathway a ) , dissociation of β1 and its subsequent traversing through the β5-αC loop are rate-limiting steps in the conversion of O-Mad2 to I-Mad2 . These structural changes involving β1 have already occurred in C-Mad2 . The energetic barrier between C-Mad2 and I-Mad2 may be lower than that between O-Mad2 and I-Mad2 ( Figure 1 ) . Thus , C-Mad2 can reach the I-Mad2 conformation more easily than O-Mad2 , explaining why C-Mad2 is more active in APC/CCdc20 inhibition . In the second model ( pathway b ) , because β6 is exposed in C-Mad2 , but blocked in O-Mad2 , the Mad2-binding motif of Cdc20 more readily forms an edge-on interaction with β6 of C-Mad2 . Binding of Cdc20 on one side of Mad2 allosterically triggers the dissociation of the β8′/8′′ hairpin from β5 on the other side of Mad2 . This hairpin then wraps around Cdc20 and completes the binding event . Although only C-Mad2 can form symmetric dimers , the β8′/8′′ hairpin of C-Mad2 does not directly participate in this symmetric dimerization . Formation of symmetric C-Mad2–C-Mad2 dimers does not impede the dissociation of β8′/8′′ from β5 and the binding of C-Mad2 to Cdc20 . In contrast , the β8′/8′′ hairpin of C-Mad2 is a major structural element that mediates the binding of O-Mad2 . Formation of the asymmetric O-Mad2–C-Mad2 impedes the dissociation of β8′/8′′ from β5 and , hence , the binding of C-Mad2 to Cdc20 , explaining the dominant-negative effects of O-Mad2 on C-Mad2 . Furthermore , O-Mad2 in the O-Mad2–C-Mad2 dimer is less active in APC/CCdc20 inhibition than C-Mad2 , suggesting that O-Mad2 cannot be activated by unliganded C-Mad2 to become I-Mad2 , unlike O-Mad2 bound to the Mad1–Mad2 core complex . The two-state behavior of Mad2 was discovered nearly a decade ago [8] . It was shown that dimeric Mad2 was active in APC/CCdc20 inhibition . Monomeric Mad2 not only was inactive in APC/CCdc20 inhibition , but also blocked the ability of dimeric Mad2 to inhibit APC/CCdc20 in a dominant-negative manner . We have now determined the crystal structure of an active dimeric Mad2 species , and show that the active Mad2 dimer is a symmetric C-Mad2–C-Mad2 dimer . O-Mad2 forms an asymmetric O-Mad2–C-Mad2 dimer and blocks the ability of C-Mad2 to inhibit APC/CCdc20 in a dominant-negative manner . The Mad1–Mad2 core complex catalyzes the conversion of O-Mad2 to unliganded C-Mad2 in the absence of Cdc20 . Our results further support the following conformational activation model for Mad2-dependent spindle checkpoint signaling ( Figure 1 ) . In this model , cytosolic O-Mad2 is autoinhibited and has a high kinetic barrier for binding to Cdc20 . Upon checkpoint activation , O-Mad2 is recruited to kinetochore-bound Mad1–Mad2 core complex through asymmetric O-Mad2–C-Mad2 dimerization . The Mad1–Mad2 core complex converts O-Mad2 to a short-lived intermediate Mad2 ( I-Mad2 ) . I-Mad2 is kinetically more favorable for Cdc20 binding and can bind directly to Cdc20 to form C-Mad2 . Alternatively , I-Mad2 can convert to unliganded C-Mad2 on its own and , upon release from the Mad1–Mad2 core complex , can form symmetric C-Mad2–C-Mad2 dimers . Both monomeric C-Mad2 and symmetric C-Mad2–C-Mad2 dimer are active in APC/CCdc20 inhibition .
The coding region of human Mad2 was amplified by polymerase chain reaction ( PCR ) and cloned into either a pGEX-KT or pQE30 ( Qiagen ) vector , each of which also included a tobacco etch virus ( TEV ) protease cleavage site . Mad2 mutants were generated with the QuikChange mutagenesis kit ( Stratagene ) . The pQE30-Mad2 plasmids were transformed into the bacteria strain M15[pREP4] to produce various His6-tagged Mad2 proteins . These proteins were purified with Ni2+-NTA agarose resin ( Qiagen ) and cleaved with TEV protease to remove the His6-tag . The proteins were further purified by anion exchange chromatography followed by gel filtration chromatography . Expression of pGEX-Mad2L13A , C79S , C106S ( referred to as Mad2L13A for simplicity ) in the bacterial strain BL21 produced a GST-Mad2 fusion protein . The fusion protein was isolated with glutathione-Sepharose beads ( GE Healthcare ) and cleaved with TEV protease to remove GST . The Mad2L13A protein was further purified by anion exchange and gel filtration chromatography . The purified Mad2L13A dimer was concentrated to 3 mg/ml in a buffer containing 20 mM Tris ( pH 8 . 0 ) , 50 mM NaCl , and 2 mM TCEP . To prepare the asymmetric Mad2ΔN10 O–C dimer , we first expressed and purified the His6-Mad2ΔN10 monomer in the O-Mad2 conformation . We had previously shown that O-Mad2 was stable at 4 °C , whereas it underwent slow spontaneous conversion to C-Mad2 at 30 °C [17] . Incubation of O-Mad2ΔN10 with TEV overnight at 4 °C did not result in the cleavage of the His6-tag from His6-O-Mad2ΔN10 , whereas TEV efficiently cleaved other unrelated His6-tag proteins under the same conditions . This result suggested that the TEV cleavage site in His6-O-Mad2ΔN10 was not accessible . We thus incubated the mixture of His6-O-Mad2ΔN10 and TEV overnight at 30 °C , which resulted in the cleavage of about 50% of the His6-O-Mad2ΔN10 molecules . This mixture of His6-tagged and untagged Mad2ΔN10 fractionated as a single high-salt peak on an anion exchange column and as a 1:1 heterodimer on a gel filtration column . Moreover , the 1H-13C HSQC spectrum of the Mad2ΔN10 dimer was virtually identical to that of O-Mad2ΔC–C-Mad2WT dimer , indicating that Mad2ΔN10 indeed formed an O-Mad2–C-Mad2 dimer . We reasoned that as His6-O-Mad2ΔN10 spontaneously converted to His6-C-Mad2ΔN10 or an intermediate Mad2 state , its TEV cleavage site became accessible , resulting in the cleavage of the His6-tag in this population of Mad2ΔN10 . The formation of the His6-O-Mad2ΔN10–untagged C-Mad2ΔN10 dimer prevented the further conversion of His6-O-Mad2ΔN10 to His6-C-Mad2ΔN10 , and thus prevented further cleavage of the His6-tag in the rest of the Mad2ΔN10 molecules by TEV . The Mad2 L13A dimer was crystallized at 20 °C using the sitting-drop vapor-diffusion method . Drops were formed by mixing 1 μl of protein and 1 μl of reservoir solution that contained 19% ( w/v ) PEG 2000 , 16% ( v/v ) glycerol , 100 mM Tris ( pH 8 . 0 ) , and 0 . 3 M MgCl2 . Larger crystals were obtained by seeding using the same conditions . The crystals were cryoprotected with reservoir solution and then flash-cooled in liquid propane . Crystals diffracted to a minimum Bragg spacing ( dmin ) of about 1 . 9 Å . At lower resolution , the diffraction data are compatible with an orthorhombic crystal symmetry . However , at higher resolution , the crystals exhibited the symmetry of space group C2 with cell dimensions of a = 109 Å , b = 191 Å , c = 154 Å and β = 90 . 02° with 12 molecules per asymmetric unit . Diffraction data were collected at beamline 19-ID ( SBC-CAT ) at the Advanced Photon Source ( Argonne National Laboratory , Argonne , Illinois , United States ) and processed with HKL2000 [29] . The Mad2L13A dimer structure was determined by the molecular replacement method with the program Phaser [30] using the Mad2 core ( residues 12–36 , 58–158 , and 177–205 ) from the structure of Mad2–MBP1 as the search model . Refinement was performed with REFMAC5 [31] from the CCP4 package [32] using diffraction data to a resolution of 1 . 95 Å , interspersed with manual rebuilding using the program Coot [33] . The 12 molecules in the asymmetric unit are arranged in two sets of six molecules related by almost perfect translational symmetry . No noncrystallographic symmetry restraints were used during refinement . Between one and four residues per Mad2 molecule were disordered and were not included in the model . The final model ( Rwork = 21 . 2% and Rfree = 24 . 7% ) contains 2 , 464 residues , 1 , 342 water molecules , eight magnesium ions , 32 chloride ions , as well as ten short PEG molecules . All but two residues are in the favored region of the Ramachandran plot . The two residues in the disallowed region are located at surface loops and are associated with weak electron density . Data collection and structure refinement statistics are summarized in Table 2 . Sedimentation equilibrium experiments were performed at 4 °C with a Beckman Optima XL-I analytical ultracentrifuge using a four-position An60Ti rotor with six-channel equilibrium centerpieces ( optical path length = 1 . 2 cm ) and an absorbance optical detection system ( Beckman Instruments ) . Sample channels were filled with 100 μl of protein at three different concentrations ( 0 . 23 , 0 . 36 , and 0 . 50 mg/ml ) in a buffer containing 20 mM Tris ( pH 8 . 0 ) , 50 mM NaCl , 0 . 2 mM TCEP . The reference channels were filled with 110 μl of buffer . The absorbance at 280 nm was monitored for each cell in 0 . 002-cm steps . Samples were centrifuged at 13 , 000 rpm , 17 , 500 rpm , and 25 , 000 rpm until equilibrium had been reached , followed by overspeed runs at 42 , 000 rpm to obtain baseline values of absorbance . The partial specific volume ( 0 . 7451 ml/g ) and the solvent density ( 1 . 0054 g/ml ) were calculated using the program SEDNTERP ( http://rd . plos . org/pbio . 0060050 ) . Sedimentation equilibrium datasets were fitted to the self-association model using Beckman Optima XL-A/Xl-I data analysis software ( Origin 6 . 03 ) . A global analysis was carried out for datasets obtained at different concentrations and rotor speeds . Isothermal titration calorimetry was performed as described [27] . ll NMR spectra were acquired at 30 °C on a Varian Unity Inova 800 MHz spectrometer using H2O/D2O 95:5 ( v/v ) as the solvent . Samples typically contained 0 . 1 mM protein in a buffer consisting of 50 mM phosphate ( pH 6 . 8 ) , 300 mM KCl , and 1 mM DTT . HeLa Tet-on ( Invitrogen ) cells were cultured in DMEM medium supplemented with 10% fetal bovine serum . The cells were transfected with pCS2-Mad2 vectors using Effectene ( Qiagen ) . After 36 h , the cells were stained with Hoechst 33342 ( Molecular Probes ) and examined using an inverted fluorescence microscope ( Zeiss ) . Lysates of the transfected cells were blotted using the appropriate antibodies . APC/C assays were performed as described [34 , 35] .
The atomic coordinates and structure factors for the symmetric C–C Mad2L13A dimer have been deposited in the Protein Data Bank ( http://www . rcsb . org/pdb/home/home . do ) with accession number PDB ID 2VFX . The Protein Data Bank accession numbers for other proteins discussed in this paper are as follows: Mad1-bound C-Mad2 ( PDB ID 1GO4 ) , Mad2–MBP1 ( PDB ID 1KLQ ) , and O–C Mad2 dimer ( PDB ID 2V64 ) .
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Chromosome missegregation during mitosis results in the gain or loss of chromosomes in the next generation of cells and can contribute to birth defects or cancer . A cellular surveillance system called the spindle checkpoint ensures that accurate chromosome segregation occurs by inhibiting the activity of the anaphase-promoting complex or cyclosome ( APC/C ) until all sister chromatids have achieved proper attachment to the mitotic spindle . The spindle checkpoint protein Mad2 binds to Cdc20 , an activator of APC/C , and inhibits the complex . The Mad2 protein can adopt either an open or closed conformation . The conformational switch in Mad2 is critical for Cdc20 binding and APC/C inhibition , and is regulated by the protein Mad1 . We report the crystal structure of the symmetric Mad2 dimer , which is made up of two closed monomers , and is active in APC/C-Cdc20 inhibition . Mad1 seems to facilitate the open–closed conformational switch of Mad2 , and we present a unified model to explain Mad1-assisted Mad2 activation in the spindle checkpoint .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
] |
[
"biochemistry",
"cell",
"biology",
"biophysics"
] |
2008
|
Insights into Mad2 Regulation in the Spindle Checkpoint Revealed by the Crystal Structure of the Symmetric Mad2 Dimer
|
Previously , we proposed a rare autosomal recessive inherited enteropathy characterized by persistent blood and protein loss from the small intestine as chronic nonspecific multiple ulcers of the small intestine ( CNSU ) . By whole-exome sequencing in five Japanese patients with CNSU and one unaffected individual , we found four candidate mutations in the SLCO2A1 gene , encoding a prostaglandin transporter . The pathogenicity of the mutations was supported by segregation analysis and genotyping data in controls . By Sanger sequencing of the coding regions , 11 of 12 other CNSU patients and 2 of 603 patients with a diagnosis of Crohn’s disease were found to have homozygous or compound heterozygous SLCO2A1 mutations . In total , we identified recessive SLCO2A1 mutations located at seven sites . Using RT-PCR , we demonstrated that the identified splice-site mutations altered the RNA splicing , and introduced a premature stop codon . Tracer prostaglandin E2 uptake analysis showed that the mutant SLCO2A1 protein for each mutation exhibited impaired prostaglandin transport . Immunohistochemistry and immunofluorescence analyses revealed that SLCO2A1 protein was expressed on the cellular membrane of vascular endothelial cells in the small intestinal mucosa in control subjects , but was not detected in affected individuals . These findings indicate that loss-of-function mutations in the SLCO2A1 gene encoding a prostaglandin transporter cause the hereditary enteropathy CNSU . We suggest a more appropriate nomenclature of “chronic enteropathy associated with SLCO2A1 gene” ( CEAS ) .
The use of capsule endoscopy and balloon endoscopy has provided a better understanding of the features of small bowel ulcers among various gastrointestinal disorders , such as Crohn’s disease ( CD ) , intestinal tuberculosis , and nonsteroidal anti-inflammatory drug ( NSAID ) –induced enteropathy [1 , 2] . Previously , we proposed a rare clinicopathologic entity characterized by multiple small intestinal ulcers of nonspecific histology and chronic persistent gastrointestinal bleeding as chronic nonspecific multiple ulcers of the small intestine ( CNSU ) [3 , 4] . The macroscopic findings of CNSU are characterized by multiple thin ulcers in a linear or circumferential configuration and concentric stenosis , and apparently mimic those of NSAID-induced enteropathy [4–7] . CNSU predominantly occurs in females and the symptoms , such as general fatigue , edema , and abdominal pain , appear during adolescence . The clinical course of the disease is chronic and intractable with reduced effects of immunosuppressive treatment including prednisolone and azathioprine . Although CNSU predominantly occurs in females , it also appears to be an autosomal recessive inherited disorder because of frequent parental consanguinity [8] . To identify the causative gene for this disorder , we performed whole-exome sequencing and identified recessive mutations in the SLCO2A1 gene , encoding a prostaglandin transporter , as causative variants . Furthermore , we replicated our findings in other patients with CNSU and established a genetic cause for this inherited disease .
We performed whole-exome sequencing in five affected females with CNSU ( A-V–2 , B-IV–3 , C-IV–3 , D-II–4 , and D-II–5 ) and one unaffected individual ( A-V–3 ) ( Figs 1 and 2 ) . Parental consanguinity was noted in families A , B , and C . The average depth of sequence coverage in the whole-exome sequencing data was 68 . 9× ( S1 Table ) . We identified a total of 368 , 403 variants , of which 20 , 271 were non-synonymous or splice-site mutations . By filtering the data with dbSNP135 , we found 2 , 406 variants located in 1 , 578 genes . Based on the parental consanguinity of the patients , we focused on the shared genes with homozygous variants among three affected individuals ( A-V–2 , B-IV–3 , and C-IV–3 ) and found nine candidate genes , PCSK9 , ASPM , DAG1 , SLCO2A1 , MCPH1 , EFEMP2 , DDHD1 , PKD1L3 , and SYNGR1 . After consideration of the results for the unaffected individual ( A-V–3 ) and another two sibling patients ( D-II–4 and D-II–5 ) , only SLCO2A1 remained as a candidate gene . The three patients with parental consanguinity ( A-V–2 , B-IV–3 , and C-IV–3 ) had a homozygous splice-site mutation in the SLCO2A1 gene , c . 1461+1G>C or c . 940+1G>A ( Fig 1 and Table 1 ) . The two sibling patients had compound heterozygous mutations , c . 664G>A ( p . Gly222Arg ) and c . 1807C>T ( p . Arg603X ) . All four mutations were predicted to be loss-of-function or damaging mutations by SIFT and PolyPhen–2 . The four identified SLCO2A1 mutations were confirmed to be present in five affected individuals ( A-V–2 , B-IV–3 , C-IV–3 , D-II–4 , and D-II–5 ) by Sanger sequencing ( Fig 1 ) . Segregation analysis of patient A-V–2 revealed that her unaffected parents , sister , brother , daughter , and son carried the heterozygous c . 1461+1G>C mutation ( Fig 1A ) . To compensate for bias in our analysis , such as the possibility of ethnic-specific variants , we genotyped the four candidate variants in 747 unaffected Japanese subjects from our previous genome-wide association study [9] . All mutations except for the c . 940+1G>A mutation were absent in controls ( Table 2 ) . The c . 940+1G>A mutation was found in the heterozygous state in 3 of 747 controls , showing a similar allele frequency of 0 . 0022 to the public exome database for the Japanese population ( HGVD database ) . Subsequently , we screened all 14 coding exons and intron-exon boundaries using Sanger sequencing in 12 other CNSU patients , and identified two novel mutations , c . 421G>T ( p . Glu141X ) and c . 1372G>T ( p . Val458Phe ) ( Table 1 ) . Eleven of the 12 patients were found to have homozygous ( nine patients ) or compound heterozygous ( two patients ) SLCO2A1 mutations that were rare and predicted to be deleterious by SIFT , PolyPhen–2 , and PROVEAN ( Table 1 ) . The remaining patient ( 66-year-old female ) , who did not have an SLCO2A1 mutation , was diagnosed as CNSU because of multiple ulcerations in the duodenum and jejunum . Although anti-tumor necrosis factor-α antibody therapy was ineffective , clinical improvement was achieved by enteral nutrition . Because CNSU can be misdiagnosed as CD in some cases , we searched for the six identified mutations of SLCO2A1 in CD patients to identify concealed CNSU patients . Among 603 patients previously diagnosed as CD [10] , two individuals ( patients 17 and 18 in Table 1 ) were found to carry a pair of compound heterozygous SLCO2A1 mutations , c . 940+1G>A/c . 547G>A and c . 940+1G>A/c . 421G>T , respectively . The c . 547G>A mutation ( p . Gly183Arg ) , was a novel mutation at a highly conserved site and predicted to be deleterious by in silico analysis . The clinical information for the two individuals was reviewed retrospectively , and the diagnosis of CNSU was confirmed . In total , we identified seven deleterious SLCO2A1 mutations in 18 patients ( Table 2 ) . In total , we found 18 patients with CNSU confirmed by genetic analysis ( Table 1 ) ; 14 of them were female . In all patients , the ulcers occurred in the ileum ( Fig 2A–2D ) . The stomach and duodenum were affected in five ( 27 . 8% ) and eight ( 44 . 4% ) patients , respectively . Because mutations in the SLCO2A1 gene , encoding a prostaglandin transporter , have been reported to be the pathogenic cause of primary hypertrophic osteoarthropathy ( PHO; OMIM 614441 ) [11–13] , we investigated whether CNSU patients had clinical manifestations of PHO . Although no patients had any clinical manifestations of PHO requiring treatment , mild digital clubbing or periostosis was present in seven of 18 patients ( S2 Table ) . Moreover , three male patients ( patients 12 , 16 , and 17 ) fulfilled the major clinical criteria for PHO , having digital clubbing , periostosis , and pachydermia . There were no female patients who fulfilled the major clinical criteria ( Fig 2E and 2F ) . Among the identified SLCO2A1 mutations , a splice-site mutation of intron 7 ( c . 940+1G>A ) was the most frequent , and nine of the 18 patients were homozygous for this mutation . There were no obvious correlations between the types of mutations and the clinical phenotypes . Because the SLCO2A1 gene encodes a prostaglandin transporter that mediates the uptake and clearance of prostaglandins , the urinary levels of prostaglandin E metabolite ( PGE-M ) were measured . The urinary PGE-M levels in CNSU patients were significantly higher than those in unaffected individuals ( p = 0 . 00013; S1 Fig ) . Using RT-PCR , we demonstrated that splicing of the SLCO2A1 mRNA , derived from biopsy specimens of the small intestine , was altered in affected siblings with the homozygous c . 940+1G>A mutation ( patients 6 and 7 ) compared with a control individual ( Fig 3A ) . Sequencing of the RT-PCR products revealed deletion of the whole exon 7 of SLCO2A1 , leading to a frameshift at amino acid position 288 and introduction of a premature stop codon after six amino acid residues ( p . R288Gfs*7 ) . Sequencing of the RT-PCR products of the transcripts in peripheral blood mononuclear cells from patient A-V–2 revealed that the homozygous c . 1461+1G>C mutation led to a 23-bp frameshift insertion into intron 10 , resulting in a premature stop codon ( p . I488Lfs*11 ) ( S2 Fig ) . For functional analysis of the intact and truncated SLCO2A1 proteins , we investigated the 3H-labeled prostaglandin E2 ( PGE2 ) transport ability in HEK293 cells transfected with intact SLCO2A1 and mutant SLCO2A1 proteins for each identified mutation ( c . 940+1G>A , p . Gly222Arg , p . Arg603X , p . Glu141X , p . Val458Phe , and p . Gly183Arg ) . HEK293 cells transfected with intact SLCO2A1 showed the ability for PGE2 transport . In contrast , HEK293 cells transfected with the mutant SLCO2A1 proteins were unable to uptake 3H-labeled PGE ( p < 0 . 0001; Fig 3B ) . These findings demonstrated that the mutant SLCO2A1 proteins identified in patients lost their functional ability as a PGE transporter . In control sections of normal small intestinal mucosa , SLCO2A1 was expressed on the cellular membrane of vascular endothelial cells in the small intestine , as evaluated by immunohistochemistry and immunofluorescence staining with a specific anti-SLCO2A1 antibody recognizing the fifth extracellular domain coded by exons 9–11 of the SLCO2A1 gene ( Fig 3C ) . We then analyzed the expression of SLCO2A1 in the small intestine of affected individuals with the homozygous c . 940+1G>A mutation ( patients 6 and 7 ) by immunofluorescence staining . However , the immunofluorescence staining did not detect any SLCO2A1 protein in the vascular endothelial cells of the patients ( Fig 3C ) . These results indicated that the entire SLCO2A1 protein was unexpressed in affected individuals with the homozygous c . 940+1G>A mutation , consistent with the results of the mRNA transcript sequencing . To investigate the subcellular localization of SLCO2A1 and the truncated SLCO2A1 protein ( ΔSLCO2A1 ) corresponding to the c . 940+1G>A mutation , we constructed expression vectors for GFP-SLCO2A1 and GFP-ΔSLCO2A1 fusion proteins and transfected them into HEK293 cells . GFP-SLCO2A1 was localized on the cellular membrane ( Fig 3D , arrows ) as well as in the cytoplasm of transfected HEK293 cells , while GFP-ΔSLCO2A1 did not accumulate on the cellular membrane ( Fig 3D ) .
In this study , we performed whole-exome sequencing in five Japanese patients with CNSU and one unaffected individual , and identified the SLCO2A1 gene as the candidate for this disorder . We further confirmed that SLCO2A1 gene mutations were involved in the pathogenesis of CNSU by genotyping of control subjects and other CNSU patients . Moreover , a genetic analysis of 603 patients previously diagnosed as CD revealed that two CNSU patients had been included in this disease group . In total , we identified seven different mutations in the SLCO2A1 gene , comprising two splicing-site mutations , two truncating mutations , and three missense mutations , as the causative gene defects for CNSU . Therefore , we propose a more appropriate nomenclature , “chronic enteropathy associated with SLCO2A1 gene” ( CEAS ) , for this disease . The SLCO2A1 gene encodes a prostaglandin transporter that may be involved in mediating the uptake and clearance of prostaglandins in numerous tissues [14–16] . This gene has already been reported as a causative gene for a subtype of PHO [11] . In fact , three of the seven identified mutations , c . 664G>A , c . 940+1G>A , and c . 1807C>T , have also been reported as causative mutations for PHO [11–13 , 17 , 18] . We found that three male patients with CEAS had all of the major clinical features of PHO , such as digital clubbing , periostosis , and pachydermia . Moreover , either digital clubbing or periostosis was present in seven of 18 patients . These findings indicate that CEAS and PHO share a causative gene and that their clinical features are profoundly influenced by other modifiers . Taken together with the facts that that CEAS predominantly occurs in females and PHO predominantly occurs in males [8 , 17] , a sex-influenced gene or hormone may be the main disease modifier . Zhang et al . [17] reported that two female family members of a PHO patient had no clinical features of PHO , despite having a homozygous SLCO2A1 mutation . Moreover , it is interesting to note that these two siblings both had anemia and hypoalbuminemia , suggesting that they had CEAS . PHO is also known to be caused by mutations of HPGD , encoding 15-hydroxyprostaglandin dehydrogenase ( 15-PGDH ) , as well as SLCO2A1 [19] . The transmembrane prostaglandin transporter encoded by the SLCO2A1 gene delivers prostaglandins to cytoplasmic 15-PGDH , resulting in their degradation [14 , 20] . Because 15-PGDH is the main enzyme for prostaglandin degradation , systemic PGE2 levels are increased in patients with HPGD deficiency . Consistent with the findings in our present investigation , Zhang et al . [11] reported that the urinary levels of PGE2 and PGE-M in SLCO2A1-deficient individuals with PHO are significantly higher than those in controls . In fact , the clinical features of PHO were assumed to be caused by excessive PGE2 . Meanwhile , although elevated levels of PGE2 in gastrointestinal tissues are commonly known to protect against mucosal inflammation via the prostaglandin receptor EP3/EP4 [21–23] , multiple intestinal ulcers occur in CEAS . This discrepancy and the pathogenesis of intestinal ulcers need to be clarified in future studies . Although CEAS is presumed to be unaccompanied by immunological inflammation in its pathogenesis , a portion of CEAS patients can be misdiagnosed as CD because of the shared common clinical features , such as multiple small intestinal ulcers , anemia , and hypoalbuminemia . In this study , two of 603 patients previously diagnosed as CD were found to be affected with CEAS by genetic analysis . Because corticosteroid and anti-tumor necrosis factor-α antibody therapies are ineffective for CEAS , recognition and precise diagnosis of CEAS are critical to avoid unnecessary therapies . The findings of our investigation lead us to conclude that genetic analysis in addition to detailed clinical information including digital clubbing , blood tests , and gastrointestinal examinations are invaluable for distinguishing CEAS from CD . Cases of a similar enteropathy referred to as cryptogenic multifocal ulcerous stenosing enteritis ( CMUSE ) have been reported in Western populations [24–26] . This enteropathy has been shown to be an autosomal recessive inherited disease caused by mutations in the PLA2G4A gene [27] . CEAS and CMUSE share common clinicopathologic features with respect to age of onset , chronic and recurrent clinical course , and nonspecific stenosing small intestinal ulcers [4 , 25] . However , the sex predominance , response to steroid therapy , and lesion sites are obviously different between the two conditions . The PLA2G4A gene encodes cytoplasmic phospholipase A2-α ( cPLA2α ) , which catalyzes the release of arachidonic acid from membrane phospholipids . CMUSE patients with compound heterozygous mutations of PLA2G4A have been reported to show globally decreased production of eicosanoids such as PGE2 and thromboxane A2 , resulting in multiple ulcers of the small intestine and platelet dysfunction [27 , 28] . Because impaired prostaglandin use underlies CEAS , CMUSE , and NSAID-induced enteropathy , we propose a new entity of gastrointestinal disorders , namely “prostaglandin-associated enteropathy” . In conclusion , we have identified loss-of-function mutations in the SLCO2A1 gene as the cause of CEAS . The present findings clearly indicate that CEAS is a genetically distinct entity independent of other gastrointestinal disorders including CD , NSAID-induced enteropathy , and CMUSE . Further studies are needed to elucidate the pathogenesis of CEAS and identify new therapeutic molecular targets for “prostaglandin-associated enteropathy” .
Written informed consent for genetic studies was obtained from each individual . The study was approved by the institutional review board at each collecting site in accordance with the Declaration of Helsinki Principles . We obtained blood samples and family pedigrees from 17 Japanese patients with CNSU and eight unaffected family members in 15 families . The diagnosis of CNSU was based on the published clinical criteria and clinical courses ( S3 Table ) [8 , 29] . Genomic DNA samples from 747 participants in our previous genome-wide association study for ulcerative colitis [9] and 603 patients with CD [10 , 30] were used after excluding subjects who recalled their consent . DNA was extracted from peripheral blood using standard methods . Whole-exome sequencing in five affected individuals ( A-V–2 , B-IV–3 , C-IV–3 , D-II–4 , and D-II–5 ) and one unaffected individual ( A-V–3 ) was performed to identify candidate genetic variants ( Fig 1 ) . Genomic DNA was enriched using a TruSeq Exome Enrichment Kit ( Illumina , San Diego , CA , USA ) according to the manufacturer’s instructions , and paired-end sequencing was carried out with an Illumina HiSeq 2000 instrument . Reads were aligned to the human genome reference sequence ( hg19 NCBI build 37 . 1 ) and decoy sequences using BWA software [31] . Duplicate reads were removed with the Picard program ( http://picard . sourceforge . net/ ) . Recalibration and realignment of the data were accomplished with Genome Analysis Toolkit ( GATK ) [32 , 33] . Single nucleotide variants and small insertions and deletions ( indels ) were identified by GATK Unified Genotyper . The effect of each missense mutation was predicted using SIFT ( http://sift . jcvi . org/ ) [34] , PolyPhen–2 ( http://genetics . bwh . harvard . edu/pph2/ ) [35] , and PROVEAN ( http://provean . jcvi . org/ ) [36] . To compensate for bias in our analysis , such as the possibility of ethnic-specific variants , we genotyped the four candidate variants identified by exome sequencing in 747 unaffected Japanese subjects by Sanger sequencing and restriction fragment length polymorphism analysis ( S4 Table ) . For further validation , Sanger sequencing of all exons of the SLCO2A1 gene in other CNSU patients was performed using standard protocols . Finally , we genotyped the six identified mutation sites in the SLCO2A1 gene in clinically diagnosed CD patients , because CNSU can be misdiagnosed as CD . Urine samples were collected from 15 CNSU patients and 13 unaffected individuals . The PGE-M levels were measured in duplicate using competitive enzyme-linked immunosorbent assays ( Cayman Chemical , Ann Arbor , MI , USA ) . We analyzed the exon 7 and exon 10 boundary mutations using RT-PCR to examine the effects of the splice-site mutations on SLCO2A1 transcription . Total RNA was extracted from biopsy specimens of the small intestine and peripheral blood mononuclear cells using a NucleoSpin RNA Kit ( Macherey-Nagel , Düren , Germany ) or PAXgene Blood RNA Kit ( Qiagen , Hilden , Germany ) . cDNA was synthesized using a PrimeScript First Strand cDNA Synthesis Kit ( TaKaRa , Otsu , Japan ) . The PCR products obtained from the cDNAs were sequenced ( S5 Table ) . A full-length cDNA ( NM_005630 ) expression vector and C-terminally GFP-tagged cDNA expression vector were purchased from OriGene Technologies ( Rockville , MD , USA ) . To construct vectors carrying a mutated cDNA , a KOD -Plus- Mutagenesis Kit ( Toyobo , Osaka , Japan ) was used according to the manufacturer’s instructions . The expression vectors were amplified by inverse PCR with specific primer sets ( S6 Table ) . The PCR products were self-ligated , and transformed into Escherichia coli chemically competent DH5α cells . To correct a frameshift in the downstream of exon 7 , a C-terminally GFP-tagged cDNA expression vector with deletion of exon 7 was amplified again . On the day before transfection , HEK293 cells were trypsinized , counted , and plated onto 12-well plates at a density of 4×105 cells/well . The cells were transfected by adding a premixed solution containing 0 . 4 μg of expression vectors and 2 μl of ScreenFectA ( Wako , Osaka , Japan ) . After 24 hours of incubation , the medium was exchanged twice with warmed Waymouth’s medium ( Life Technologies , Carlsbad , CA , USA ) , and the cells were incubated for 30 minutes at 37°C in uptake medium containing [5 , 6 , 8 , 11 , 12 , 14 , 15-3H ( N ) ]-PGE2 ( PerkinElmer , Waltham , MA , USA ) at 0 . 6 nM . The cells were washed four times with cold Waymouth’s medium , and lysed with 200 μl of RIPA Buffer ( Thermo Fisher Scientific , Hemel Hempstead , UK ) containing a protease inhibitor ( Roche , Basel , Switzerland ) . The total protein concentration was quantified using a BCA Protein Assay Kit ( Thermo Fisher Scientific ) . Next , 150 μl of cell lysate was mixed with 5 ml of MicroScint–20 ( PerkinElmer ) , and scintillation counting was performed in a Tri-Carb 3100TR liquid scintillation spectrometer ( PerkinElmer ) . Formalin-fixed paraffin-embedded tissues were sectioned at 3-μm thickness . After antigen unmasking in 10 mM sodium citrate buffer ( pH 6 ) for 15 minutes at 121°C , the sections were blocked with Protein Block Serum-Free ( Dako , Glostrup , Denmark ) for 30 minutes at room temperature . The sections were then incubated with a diluted anti-SLCO2A1 antibody ( HPA013742; Sigma-Aldrich , St . Louis , MO , USA; antigen sequence: PSTSSSIHPQSPACRRDCSCPDSIFHPVCGDNGIEYLSPCHAGCSNINMSSATSKQLIYLNCSCVTGGSASAKTGSCPVPCAH ) overnight at 4°C , followed by MAX-PO ( MULTI ) ( Nichirei , Tokyo , Japan ) for 30 minutes at room temperature . DAB solution ( Nichirei ) was applied for color development . After the immunocytochemistry , the sections were counterstained with Mayer’s hematoxylin solution ( Nichirei ) . For immunofluorescence , the sections were incubated with a primary antibody mixture of the anti-SLCO2A1 antibody ( HPA013742 ) and an anti-VE-cadherin antibody ( LS-B3780; LifeSpan BioSciences , Seattle , WA , USA ) overnight at 4°C , followed by a secondary antibody mixture of Alexa Fluor 568-conjugated goat anti-rabbit IgG ( H&L ) antibody and Alexa Fluor 488-conjugated goat anti-mouse IgG ( H&L ) antibody ( Life Technologies ) for 30 minutes at room temperature . The stained sections were analyzed using an ECLIPSE TE2000-U ( Nikon , Tokyo , Japan ) . For observation of HEK293 cells expressing GFP-fusion proteins , cells were fixed with 4% paraformaldehyde phosphate buffer solution ( Wako ) for 20 minutes , and then permeabilized with 0 . 1% Triton X–100 ( Sigma-Aldrich ) in D-PBS ( - ) solution ( Wako ) for 20 minutes . Nuclei were stained with 16 . 2 μM Hoechst 33342 ( Life Technologies ) in D-PBS ( - ) solution for 5 minutes . GFP and nuclei were visualized using a 40× objective on an LSM710 Laser Scanning Confocal Microscope ( Carl Zeiss , Oberkochen , Germany ) . The chi-square test and Fisher’s exact test , where appropriate , were used to analyze categorical data . Student’s t-test was used to compare quantitative data between two groups . Dunnett’s method was used for multiple comparisons with a control group . The analyses were performed using JMP Pro statistical package 11 . 2 . 0 ( SAS Institute , Cary , NC , USA ) . Values of p < 0 . 05 were regarded as statistically significant .
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Advanced diagnostic innovations such as capsule endoscopy and balloon endoscopy have provided better understanding of endoscopic findings of small bowel diseases . However , it remains difficult to diagnose small intestinal diseases such as Crohn’s disease , intestinal tuberculosis , and nonsteroidal anti-inflammatory drug-induced enteropathy by the endoscopic findings alone . We previously reported a rare autosomal recessive inherited enteropathy characterized by persistent blood and protein loss from the small intestine . This enteropathy has an intractable clinical course with ineffectiveness of immunosuppressive treatment . In this study , we identified recessive mutations in the SLCO2A1 gene , encoding a prostaglandin transporter , as causative variants of this disorder by exome sequencing of four families , and showed that this disease is distinct from Crohn’s disease . We also showed that the mutations found in the patients caused functional impairment of prostaglandin E2 uptake within cells . The present findings suggest that genetic analysis together with detailed clinical information is invaluable for diagnosis of the disease , and that there may be a concept of enteropathy referred to as “prostaglandin-associated enteropathy” , irrespective of ethnic background .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
A Hereditary Enteropathy Caused by Mutations in the SLCO2A1 Gene, Encoding a Prostaglandin Transporter
|
Tubercidin ( TUB ) is a toxic adenosine analog with potential antiparasitic activity against Leishmania , with mechanism of action and resistance that are not completely understood . For understanding the mechanisms of action and identifying the potential metabolic pathways affected by this drug , we employed in this study an overexpression/selection approach using TUB for the identification of potential targets , as well as , drug resistance genes in L . major . Although , TUB is toxic to the mammalian host , these findings can provide evidences for a rational drug design based on purine pathway against leishmaniasis . After transfection of a cosmid genomic library into L . major Friedlin ( LmjF ) parasites and application of the overexpression/selection method , we identified two cosmids ( cosTUB1 and cosTU2 ) containing two different loci capable of conferring significant levels of TUB resistance . In the cosTUB1 contained a gene encoding NUPM1-like protein , which has been previously described as associated with TUB resistance in L . amazonensis . In the cosTUB2 we identified and characterized a gene encoding a 63 kDa protein that we denoted as tubercidin-resistance protein ( TRP ) . Functional analysis revealed that the transfectants were less susceptible to TUB than LmjF parasites or those transfected with the control vector . In addition , the trp mRNA and protein levels in cosTUB2 transfectants were higher than LmjF . TRP immunolocalization revealed that it was co-localized to the endoplasmic reticulum ( ER ) , a cellular compartment with many functions . In silico predictions indicated that TRP contains only a hypothetical transmembrane domain . Thus , it is likely that TRP is a lumen protein involved in multidrug efflux transport that may be involved in the purine metabolic pathway . This study demonstrated for the first time that TRP is associated with TUB resistance in Leishmania . The next challenge is to determine how TRP mediates TUB resistance and whether purine metabolism is affected by this protein in the parasite . Finally , these findings may be helpful for the development of alternative anti-leishmanial drugs that target purine pathway .
Leishmania spp . are the causative agents of leishmaniasis , a parasitic protozoan disease that affects 12 million people worldwide with an estimated annual incidence of approximately 1 million , including both visceral and cutaneous cases [1] . The leishmaniasis chemotherapy is complicated because most of drugs used are expensive , toxic , and require long periods of supervised therapy [2] . Pentavalent antimonial is the WHO-recommended drug for the treatment of leishmaniasis; however , it has several side effects and reports of parasite resistance have been described worldwide [3] . Cases that are unresponsive to antimonial treatment are usually treated with amphotericin or pentamidine , although these drugs also have several side effects [3] . Miltefosine is the first effective oral drug developed to treat visceral leishmaniasis . It has been used in India for more than a decade [4] and an increase in the failure rate has been reported [5 , 6] . Considering the limitations of the currently used chemotherapy and the lack of effective vaccines for the leishmaniasis , the identification of new drugs and vaccine approaches for the treatment of leishmaniasis is required . A rational strategy for chemotherapeutic exploitation in parasitic diseases can be developed , based on the identification of fundamental metabolic differences between parasite and host . New potential drug targets have been identified in molecular and biochemical studies to identify potential targets of the parasite that can be used in future therapies [7 , 8] . An interesting pathway for exploration in the parasite is the purine metabolism . Purine nucleotides and their derivatives are precursors of a variety of cellular and metabolic processes , including energy production , cell signaling , synthesis of nucleic acids , modulation of enzymatic activities and synthesis of co-enzymes [9–11] . Leishmania and other protozoan parasites are unable to synthesize purine nucleotides de novo and must salvage them from the host [10] . This unique characteristic may be the basis for the susceptibility of Leishmania to purine analogs [12 , 13] . Purine uptake in Leishmania is required for parasite viability during all life cycle stages [14] . Parasite nucleoside transporters , located on the plasma membrane , perform an essential function in uptake of purine nucleosides from the host into the parasite , which is the first step in the salvage process [15] . TUB is a toxic adenosine analog that is incorporated into nucleic acids in microorganisms and in mammalian cells . TUB has been previously described as a potential antiparasitic agent due to its inhibition of purine uptake in Schistosoma mansoni , S . japonicum [16 , 17] , Trypanosoma gambiensi [18] and Leishmania spp . [19 , 20] . Considering the potential antiparasitic activity of TUB , in this study we aimed to identify the potential loci involved in TUB resistance in L . major . This knowledge is essential to understand the mechanism of action and resistance of this compound , as well as for identifying potential drug targets in the parasite . Accordingly , an overexpression/selection method with cosmid genomic libraries of LmjF was used to isolate two loci involved in TUB resistance [21] . One of the isolated cosmids ( cosTUB1 ) contains a locus encoding NUPM1 , a putative transcription-factor-like protein , previously described as toxic nucleoside resistance ( TOR ) [22] . TOR was described in L . amazonensis promastigote mutants resistant to TUB after drug selection in vitro , as an atypical multidrug resistance protein [22–24] . The other isolated cosmid ( cosTUB2 ) contains a locus involved in TUB resistance following overexpression . This locus is not related to the tor gene or to other previously described locus involved in drug resistance in Leishmania [21] . Interestingly , these two loci are associated with two different resistance profiles: while tor also confers resistance to both inosine dialdehyde and allopurinol , the other locus confers resistance to inosine dialdehyde and hypersensitivity to allopurinol [21] . Based on these previous findings , in this study , we mapped , sequenced the genomic regions of two cosmids ( cosTUB1 and cosTUB2 ) and identified the two genes related to TUB resistance in LmjF . TUB resistance gene in cosTUB1 encodes the previously described TOR protein , while the locus in cosTUB2 encodes a hypothetical protein . Of the 8 , 272 protein-coding genes predicted and annotated in LmjF genome , approximately 50% are annotated as hypothetical proteins; most of them are likely involved in essential cellular processes [25 , 26] . Thus , the identification and characterization of TRP , may contribute for increasing the understanding of the purine pathway in Leishmania and the role of this protein in drug resistance mechanisms .
Leishmania major Friedlin ( LmjF ) strain ( MHOM/IL/1980/Friedlin ) promastigotes were grown at 25°C in M199 medium supplemented with L-glutamine , 10% heat-inactivated fetal calf serum , 0 . 25% hemin , 12 mM NaHCO3 , 100 μM adenine , 40 mM HEPES , 50 U/mL penicillin and 50 μg/mL streptomycin . TUB , allopurinol , pentamidine , hygromycin B ( HYG ) and G418 were obtained from Sigma-Aldrich ( St . Louis , MO , USA ) . Transfected parasites were cultured in M199 medium supplemented with increasing concentrations of HYG ( 125 to 500 μg/mL ) or G418 ( 32 to 500 μg/mL ) , depending on the drug resistance marker . Cosmids cosTUB1 and cosTUB2 associated with TUB resistance were previously isolated by an overexpression/selection strategy in LmjF as described by Cotrim et al . [21] . Briefly , two genomic libraries containing 30–40 kb inserts of genomic DNA from LmjF strain constructed in the shuttle vector cLHYG [27] were prepared by shearing or Sau3A partial digestion [21] . After transfection of these two cosmid libraries , parasites were plated on semisolid media in the presence of two independent concentrations of TUB ( 0 . 9 and 1 . 8 μM ) . A total of 39 colonies obtained after 10–15 days of incubation were then transferred to M199 liquid medium containing increasing concentrations of HYG ( from 125 to 500 μg/mL ) to increase the cosmid copy number [21] . Cosmid DNA was recovered from these primary TUB-resistant transfectants , used to transform into Escherichia coli DH5α strain and analyzed by restriction enzyme digestion [21] . Southern blot analysis confirmed the presence of two independent loci involved in TUB resistance , one containing tor gene and the other corresponding to a new locus . To confirm the role in TUB resistance , cosTUB1 and cosTUB2 were transfected back into LmjF and after increasing cosmid copy number , tested for TUB resistance . Deletions of cosTUB1 and cosTUB2 were generated by partial digestion with KpnI and ApaI , respectively , followed by self-ligation , to identify the gene ( s ) involved in TUB resistance . A pTRP construct was generated by total digestion of cosTUB2 with ClaI and EcoRV restriction enzymes , followed by ligation of the 3 kb fragment into the pSNBR vector [28] , previously digested with the same enzymes . Transfections were performed as described by Coburn et al . [29] . Promastigotes from the late logarithmic phase were harvested , washed and resuspended in electroporation buffer . A total of 20–40 μg of cosmid and plasmid DNA was mixed on ice with 4x107 cells in a 2-mm cuvette and subjected to electroporation ( 500 μF , 2 . 25 kV/cm ) using a Bio-Rad Gene Pulser apparatus . Mock transfection was performed in absence of cosmid or plasmid DNA for the negative control while the transfection with cosmid or plasmid DNA of empty vector was performed as control . Transfected parasites were kept on ice for 10 minutes and then transferred to 10 mL M199 medium . The antibiotics HYG or G418 were added after 24 hours , depending on the drug resistance marker . For drug susceptibility analysis , promastigotes ( 106 promastigotes/mL ) were incubated at 25°C in the presence of increasing TUB concentrations for 72 hours , and then the number of parasites was determined using a Coulter T890 ( Beckman , CA , USA ) . The 50% inhibitory concentration ( IC50 ) was measured at the time when control cultures lacking the drug had reached the late logarithmic phase of growth [30 , 31] . The results are expressed as the means ± standard error . Statistical analysis was performed using the non-parametric Kruskal-Wallis test and p < 0 . 05 was considered significant . To identify the genomic region of interest in L . major genome databases , a 1 . 0 kb EcoRI fragment from cosTUB1 and a 1 . 0 kb EcoRV fragment from cosTUB2 were subcloned into a pUC-π vector . The subcloned fragments were sequenced using a MegaBACE 1000 automated sequencer ( GE Healthcare , UK ) with DYEnamic Dye Terminator kit ( GE Healthcare , UK ) , according to the manufacturer’s instructions . Analyses of the nucleotide sequences were performed using Lasergene Software ( DNASTAR , Inc . ) and Clone Manager 9 Software . Sequence data for the remaining regions were obtained from LmjF GeneDB [25] . Nucleotide sequences obtained were used to map the genomic region corresponding to the two different loci using LmjF database . In silico analyses were also conducted to estimate the insert sizes of both cosmids after digestion with different restriction enzymes . In silico analysis of the genomic regions involved in TUB resistance was performed using DNASTAR and Clone Manager 9 Software . BLAST searches of LmjF GeneDB [25] and TriTrypDB [32] were performed using the standard settings . Multiple alignments were performed using the Constraint-based Multiple Protein Alignment Tool ( Cobalt ) [33] . Prediction of transmembrane domains was performed with the TMHMM Server [34 , 35] while the predictions of protein function , sub-cellular localization and the tridimensional TRP structure were performed using ProtFun [36 , 37] , TargetP Server [38 , 39] and Phyre2 [40] , respectively . The sequence data described in this paper are available under the following accession numbers: XM_001685128 . 1 for gene ID LmjF . 31 . 1940 ( nupm1 ) , and XM_001685135 . 1 for gene ID LmjF . 31 . 2010 ( trp ) . All sequence data are also available at www . tritrypdb . org . Total RNA from promastigotes in stationary growth phase and during the growth curve on days 3 , 5 , 7 and 9 , were isolated using TRIzol reagent ( Life Technologies , Carlsbad , CA , USA ) , according to the manufacturer’s instructions . RNA samples were treated with DNase I ( Thermo Scientific , Lithuania , EU ) and RNA concentration and purity were determined using a spectrophotometer at A260/A280 ( Nanodrop ND1000 , Thermo Scientific , USA ) . Reverse transcription was performed using 2 μg of total RNA as template , reverse transcriptase and random primers ( cDNA synthesis kit , Thermo-Scientific , Canada ) , according to the manufacturer’s instructions . Equal amounts of cDNA were run in triplicate in a total volume of 25 μL containing Power SYBR Green Master Mix ( Life Technologies , Warrington , UK ) and the following primers ( 10 μM ) : TRP_F 5´-CGGTGTAGATGAACCAGCAGTAG-3´ , TRP_R 5´-CTCACAGAGGGATTTCGAGAGTG-3´ , GAPDH_F 5´-AACGAGAAGTTCGGCATAGTCGAG-3´ and GAPDH_R: 5´-ACTATCCACCGTCTTCTGCTTTGC-3´ . The mixture was incubated at 94°C for 5 minutes , followed by 40 cycles at 94°C for 30 sec , 64°C for 30 sec and 72°C for 30 sec . A negative control in the absence of reverse transcriptase was included in RT-qPCR assays for check DNA contamination in RNA samples . Reactions were carried out using an Exicycler 96 ( Bioneer , Daejeon , Korea ) . The copy number of the target gene ( trp ) and housekeeping gene ( gapdh ) were quantified in three biological replicate samples , considering the molar mass concentration , according to a standard curve generated from a ten-fold serial dilution of a quantified and linearized plasmid containing the target fragment for each quantification test . The normalized trp/gapdh ratio of the absolute number of molecules of each target was used as parameter of the relative expression of trp in the cosTUB2 and pTRP transfectants relative to LmjF or the line transfected with the empty vector ( pSNBR or cLHYG ) . Analyses were performed using Analysis Exicycler3 Software ( Bioneer , Daejeon , Korea ) . The open reading frame ( ORF ) of gene trp ( LmjF . 31 . 2010 ) was amplified by PCR using the following primers containing the restriction enzyme sites for BamHI and NotI ( underlined ) : TRP_F_BamHI 5´-GGATCCATGGAGTGCATCAACCAAGAGAGC-3´ and TRP__R_NotI 5´-GCGGCCGCTCACATGGCACAGATAAACACC-3´ . The amplified fragment was cloned into the pET28a ( Novagen , USA ) expression vector and sequenced to confirm the insertion direction . The pET-TRP plasmid obtained was then used to transform into E . coli ( BL21 ( DE3 ) CodonPlus-RIL ) . Selected clones were grown aerobically at 37°C in LB medium containing kanamycin ( 30 μg/mL ) and chloramphenicol ( 35 μg/mL ) to a culture OD600 0 . 6–0 . 8 . pET-TRP expression was induced by 1 mM of isopropyl-β-D-thiogalactopyranoside . After induction , the culture was lysed by sonication ( Sonics–VCX500 ) with 20 mM sodium phosphate , 500 mM sodium chloride and 5 mM imidazole . Lysed samples were clarified by centrifugation at 10 , 000 x g , for 15 minutes at 4°C , and inclusion bodies were solubilized with 20 mM sodium phosphate , 500 mM sodium chloride , 5 mM imidazole and 8 M urea . Recombinant TRP was obtained by affinity chromatography with a 1 mL HisTrap HP column ( GE Healthcare , Uppsala , Sweden ) . The purified recombinant TRP was analyzed by SDS-PAGE and then used to produce a rabbit polyclonal anti-TRP antibody by Proteimax Biotechnology ( Sao Paulo , Brazil ) . Approximately 107 promastigotes in the stationary growth phase and during the growth curve on days 3 , 5 , 7 and 9 were washed with PBS and then lysed with lysis buffer ( 100 mM Tris-HCl pH 7 . 5 , 2% Nonidet P40 , 1 mM PMSF and protease inhibitor cocktail ( Sigma-Aldrich , St Louis , MO , USA ) ) . Cells were disrupted by ten freeze/thaw cycles in liquid nitrogen and 42°C , and were then cleared of cellular debris by centrifugation at 12 , 000 x g for 15 minutes at 4°C . Equal amounts of total protein ( 25 μg ) were solved using SDS-PAGE and transferred to a nitrocellulose membrane ( Hybond-C , Amersham Biosciences , Buckinghamshire , England ) using a Trans-Blot SD apparatus ( Bio-Rad , USA ) . The membrane was incubated with Blocking Buffer ( LI-COR Bioscience , Lincoln , NE , USA ) and then with anti-TRP serum ( 1:2000 dilution ) , overnight , at 4°C . After incubation with primary antibody , the membrane was incubated with biotin anti-rabbit antibody ( Santa Cruz Biotechnology , CA , USA ) ( 1:1000 dilution ) for 1 hour at room temperature and then with streptavidin ( Santa Cruz Biotechnology , CA , USA ) ( 1:2000 dilution ) for 30 minutes at room temperature for biotin-streptavidin binding . Anti-α-tubulin ( Sigma-Aldrich , St . Louis , MO , USA ) ( 1:1000 dilution ) was used to normalize the amount of protein in the blot . All steps were followed by washing 3 times with PBS . The membranes were scanned using an Odyssey CLx apparatus ( Li-COR , Lincoln , NE , USA ) in both 700 and 800 nm channels using an Odyssey System . Odyssey Imaging CLx instrument was used at the 5/5 intensity setting ( 700/800 nm ) . Quantification of the protein level was performed with Image Studio2 . 1 Software ( Li-COR , Lincoln , NE , USA ) . TRP target band densities were normalized against α-tubulin for blotting comparisons in LmjF , cosTUB2 and pTRP transfectants . Statistical analysis was conducted using the Mann-Whitney U-test , and p < 0 . 05 was considered significant for three independent experiments . Approximately 106 promastigotes of LmjF and pTRP transfectants in the stationary growth phase were washed with PBS and adhered to cover slips treated with poly-L-lysine ( Sigma-Aldrich , St . Louis , MO , USA ) for 15 minutes . The cells were then fixed with 3% paraformaldehyde for 10 minutes and treated with 50 mM ammonium chloride for 10 minutes . The fixed cells were permeabilized and blocked with 0 . 1% Triton X-100 and 0 . 1% BSA in PBS for 10 minutes at room temperature . To analyze sub-cellular TRP localization , anti-TRP polyclonal antibody ( 1:100 dilution ) was visualized using an anti-rabbit secondary antibody conjugated to Alexa488 ( Life Technologies , Carlsbad , CA , USA ) ( 1:500 dilution ) . Anti-BiP/GRP78 ( BD Bioscience , Iowa , USA ) ( 1:500 dilution ) was visualized using an anti-mouse secondary antibody conjugated to Alexa594 ( Life Technologies , Carlsbad , CA , USA ) ( 1:500 dilution ) . Nuclear and kinetoplast DNA were labeled using DAPI . Each step was followed by washing with PBS 10 times . The coverslips were mounted in ProLong media ( Life Technologies , Carlsbad , CA , USA ) . All imaging was performed at the Molecular Imaging Center ( MIC ) of the University of Bergen , using a Zeiss LSM 510 Meta confocal microscopy . Co-localization images were edited using Photoshop 6 .
Using the overexpression/selection strategy described by Cotrim et al . [21] , a library of 17 , 900 independent genomic cosmid transfectants in LmjF were recovered from semisolid plates in two TUB concentrations . Thirty-nine colonies showing differential survival were recovered and then analyzed by restriction enzyme digestion . cosTUB1a and cosTUB1b were recovered each one from a single colony , while cosTUB2 was recovered from several colonies [21] . Southern blot analysis demonstrated that cosTUB1a and cosTUB1b were involved in TUB resistance conferred by the TOR protein [21] . This protein has been previously described as related to TUB resistance in selected L . amazonensis promastigote mutants [22 , 24] . Since cosTUB1a and cosTUB1b were referred to the same TUB resistance gene , we decided to examine only cosTUB1a followed of mapping , functional and sequencing analysis . Parasites transfected with the cosmid cosTUB1 showed moderate TUB resistance ( 1 . 95-fold resistance ) compared with the LmjF ( S1 Table ) . To map the gene likely involved in the resistance phenotype , a set of deletions was generated using the restriction enzyme KpnI . Four independent deletions were generated , transfected back into LmjF and amplified by HYG selection . Transfected parasites with the deletions cosTUB1-ΔKpnI-III and cosTUB1-ΔKpnI-IV exhibited 2 . 04- and 2 . 82-fold resistance , respectively , compared with LmjF parasites ( S1 Fig and S1 Table ) . No significant differences in resistance were observed among the other deletions compared with LmjF or with transfected parasites carrying the empty vector cLHYG ( S1 Table ) . To identify the gene of cosTUB1 involved in TUB resistance , a 1 . 0 kb EcoRI fragment from this cosmid ( S1 Fig ) was sub-cloned into the pUC-π vector and sequenced . In silico analysis revealed that the cosTUB1 insert corresponds to a genomic DNA region from chromosome 31 of LmjF containing five ORFs ( S1 Fig ) . Two of which encode hypothetical proteins ( LmjF . 31 . 1910 and LmjF . 31 . 1920 ) , and the other three encode peptidase m20/m25/m40 family-like protein ( LmjF . 31 . 1890 ) , dihydrouridine synthase ( LmjF . 31 . 1930 ) and transcription-factor-like NUPM1 protein ( LmjF . 31 . 1940 ) ( S1 Fig ) . According to the map and the functional analysis , the 3 . 0 kb fragment represented in the deletion cosTUB1-ΔKpnI-IV , contained the locus likely involved in TUB resistance ( S1 Fig ) . In this region , we identified the gene that encodes the transcription-factor-like NUPM1 protein ( LmjF . 31 . 1940 ) ( accession number XP_001685180 . 1 ) ( S1 Fig ) . This protein has 77% similarity to NUPM1 of L . amazonensis , which has been previously described as TOR protein and it is related to TUB resistance in selected L . amazonensis promastigote mutants [22 , 24] . According to TriTrypDB , nupm1 encodes a 53 . 1 kDa protein with only one predicted transmembrane domain and no other predicted domain ( S2 Fig ) . The other cosmid , cosTUB2 , was recovered 37 times and presented a different resistance profile compared with cosTUB1 . It conferred a 3 . 78-fold increase in resistance compared with LmjF ( Table 1 ) . Using the same strategy as that described for cosTUB1 , we mapped the likely gene involved in the resistance phenotype through the generation of a set of deletions with the restriction enzyme ApaI ( Fig 1 ) . Four independent deletions were generated , transfected back into LmjF and then selected using HYG selection . Parasites transfected with the cosTUB2-ΔApaI-III deletion exhibited 2 . 0-fold resistance compared with LmjF ( Fig 1 and Table 1 ) . No significant difference was observed among the transfectants containing the other three deletions compared with LmjF or with transfected parasites carrying the empty vector cLHYG ( Table 1 ) . To map the genomic region corresponding to the locus involved in TUB resistance , a 1 . 0 kb EcoRV fragment from cosTUB2 ( Fig 1 ) was sub-cloned into the pUC-π vector and sequenced . Sequence analysis indicated that this DNA fragment corresponded to a second region of chromosome 31 of L . major . In silico analysis indicated the presence of eight ORFs in the 30 kb genomic region of cosTUB2 . Four of these genes were annotated as encoding hypothetical proteins ( LmjF . 31 . 2010 , LmjF . 31 . 2040 , LmjF . 31 . 2050 and LmjF . 31 . 2060 ) , while the other four genes encoded a non-coding RNA ( LmjF . 31 . ncRNA ) , a glycoprotein-like ( GP63-like ) ( LmjF . 31 . 2000 ) , a succinyl-diaminopimelate-desuccinylase-like ( SDD-like ) protein ( LmjF . 31 . 2020 ) and an ubiquitin-fusion protein ( LmjF . 31 . 2030 ) ( Fig 1 ) . In silico data associated with genomic mapping and functional analysis indicated that the gene LmjF . 31 . 2010 , located in the 9 . 6 kb fragment of cosTUB2 and also in cosTUB2-ΔApaI-III ( Fig 1 ) , encodes a hypothetical protein that could be involved in TUB resistance . To confirm LmjF . 31 . 2010 as the gene involved with TUB resistance , we sub-cloned the region encompassing the LmjF . 31 . 2010 gene ( a 3 kb fragment from cosTUB2 digested with ClaI-EcoRI ) into pSNBR vector , previously digested with the same enzymes ( Fig 1 ) . This construct ( pTRP ) was transfected back into LmjF and the amplification and overexpression of this gene was obtained by increasing the concentration of G418 . As expected , the pTRP transfectants exhibited 1 . 91-fold resistance compared with LmjF ( Table 1 ) , confirming the role of LmjF . 31 . 2010 gene in TUB resistance . The trp mRNA expression in LmjF promastigotes was measured by RT-qPCR . Quantification of trp transcripts in the cosTUB2 and pTRP transfectants relative to LmjF or to the transfected line with the empty vector ( cLHYG or pSNBR ) was performed using trp gene as target . The data were normalized by the amount of gapdh transcript . Both sequences corresponded to single copy genes . As shown in Fig 2 , the cosTUB2 transfectant exhibited 19 . 4- and 16 . 8-fold increase in trp mRNA expression compared with LmjF and with cLHYG transfectant , respectively . In contrast , no significant change in trp mRNA expression was observed between the pTRP transfectant and LmjF or pSNBR transfectant ( Fig 2 ) . Additional data obtained during the time-course of growth curve of LmjF promastigotes demonstrated that trp transcript expression was increased in the day 3 , in the logarithimic growth phase . As shown in S3 Fig , trp mRNA transcript expression was 2-fold increase on day 3 compared with days 5 , 7 and 9 . The same profile was observed at protein levels , with an increase of 1 . 5-fold on day 3 compared with days 5 , 7 and 9 ( S3 Fig ) . Western blot analysis of cell lysates from LmjF and parasites transfected with cosTUB2 and pTRP were performed using an anti-TRP polyclonal antibody . As shown in Fig 3 , the signal intensity of TRP expression was significantly increased in the cosTUB2 and pTRP lysates compared with LmjF lysate . Sequence analysis of the trp gene ( LmjF . 31 . 2010 ) ( accession number XP_001685187 . 1 ) indicated that it encodes a 63 . 4 kDa protein . It is conserved in the genus Leishmania , and it is not present in other trypanosomatids , such as Trypanosoma brucei and T . cruzi ( Fig 4 ) . This gene encodes for a protein that contains just one hypothetical transmembrane domain and no other putative conserved domain or peptide signal ( S4 Fig ) . Interestingly , multiple sequence alignments revealed the presence of 11 amino acids specific to L . braziliensis ( LbrM ) ( S4 Fig ) . Indeed , TRP of L . major contains approximately 85% of similarity with its orthologs of the subgenus Leishmania and 54% of similarity with its ortholog of L . ( Viannia ) braziliensis ( S4 Fig ) . Additional in silico data based on the Protein Functional Category and Enzyme Class Database ( ProtFun Server ) , which predicts cellular role and enzyme class based on gene ontology , indicated that TRP is an enzyme involved in the purine and pyrimidine pathway . In silico analysis of TRP using the TargetP 1 . 1 Server , which predicts the sub-cellular localization of eukaryotic proteins revealed the absence of any sequence corresponding to a mitochondrial targeting peptide . 3D protein prediction was performed by submitting the amino acid sequence to the Phyre2 web portal for protein modeling , prediction and analysis . The predicted model of TRP based on heuristics to maximize confidence , percent identity and alignment coverage is shown in Fig 5 . Some disordered regions were observed , but the prediction showed 90% confidence . Some regions have interesting folding patterns with similarities to transmembrane helices , multidrug efflux transporter , hydrolase/transport protein and transferase ( Fig 5 ) . For cellular immunolocalization of TRP , we used antibodies against the recombinant TRP . Considering our hypothesis that TRP is an ER protein , we used an ER marker ( anti-BiP/GRP78 ) . Confocal microscopy showed that TRP is co-localized in ER in stationary phase promastigotes of LmjF and of pTRP transfected line ( Fig 6 ) . We next analyzed whether the transfected lines overexpressing the trp gene were resistant to other drugs . Interestingly , the cosTUB2 and pTRP transfectants showed cross-resistance to pentamidine , with 2 . 0- and 5 . 0-fold resistance , respectively , compared with LmjF ( Table 2 ) . No cross-resistance to allopurinol was observed . Indeed , the cosTUB2 transfectants were more sensitive to allopurinol .
The molecular mechanism of action of compounds used in leishmaniasis treatment is not well known . Overexpression/selection methods have been used for identification of drug targets and potential drug resistant genes [21 , 30 , 31 , 41] . Because of the limited knowledge about the purine metabolism in Leishmania , we proposed in this study to elucidate the purine pathway in Leishmania examining the resistance phenotype after transfection of a cosmid genomic library , followed by drug pressure using TUB . This drug has been demonstrated to have potent activity against promastigotes forms of L . amazonensis , L . braziliensis , L . infantum chagasi and L . major [19 , 20] . The same antiparasitic efficacy has also been reported in vitro against intracellular amastigotes and in vivo infection against L . amazonensis when the drug was associated with the specific inhibitor of nucleoside transport for mammalian cells , the nitrobenzylthioinosine ( NBMPR ) [20] . Two cosmids conferring TUB resistance , cosTUB1 and cosTUB2 , were isolated from transfected parasites with a genomic library constructed in the cLHYG vector [21] . The gene related to TUB resistance in cosTUB1 provided 2 . 82-fold resistance compared with LmjF . In silico analysis of the genomic region in L . major GeneDB revealed that cosTUB1 is located on chromosome 31 and that 1 , 455 base pair gene present in the 3 . 0 kb fragment encodes NUPM1-like protein . A previous study first referred to this protein as TOR because it was involved in TUB resistance in selected L . amazonensis mutants [23] . According to these authors , L . amazonensis becomes resistant to TUB by decreasing the capacity to accumulate this exogenous purine . Later , it was suggested that the decrease was related to the reduction in the activity of purine transporters [24] . The TOR protein mediates resistance by redirecting the adenosine permease from the plasma membrane to the multi-vesicular tubule lysosome [24] and TUB resistant parasites overexpressing tor are unable to uptake the toxic purine and become resistant to the drug [24] . Moreover , TOR could act at the protein level and affect the activity and/or the amount of transporters and other proteins [24] . The protein has similarities to Oct-6 , a mammalian transcription factor of the Pou family [24 , 42] . Members of the Oct family bind to the octamer motif , a cis-acting regulatory element enhancer and stimulate transcription via this octamer motif [42] . In contrast to TOR identification , we could not find any previously described gene in cosTUB2 related to TUB resistance . Similar to cosTUB1 , the cosTUB2 insert contains a genomic region of chromosome 31 , but the region is distinct from that one in cosTUB1 . According to the restriction map and functional analysis , the 9 . 6 kb fragment present in both cosTUB2 and the cosTUB2-ΔApaI-III deletion was suggested to be the region related to TUB resistance . In silico analysis showed that this region coded for a hypothetical protein ( LmjF . 31 . 2010 ) , a succinyl-diaminopimelate-desuccinylase-like ( SDD-like gene ) and part of the glycoprotein 63 ( GP63-like ) . GP63-like is the major surface glycoprotein in Leishmania promastigotes , beside lipophosphoglycan ( LPG ) and GIPLs [43] . Several functions of GP63 have been described in the vertebrate host , including cell adhesion to mammalian cells . It is predominantly expressed in the form present in the insect host and unknown relationship with drug resistance or purine metabolism has been described . Further , SDD-like is not either associated with resistance to toxic nucleosides . Its function is related to synthesis and metabolism of amino acids [44] . Thus , we focused our study on the hypothetical protein LmjF . 31 . 2010 . The coding region of this gene was cloned for generating the construct pTRP , which was transfected into LmjF . The protocol for overexpression/selection of transfectants conferred moderate level of resistance to TUB ( 1 . 91-fold resistance ) compared with that conferred by cosTUB2 ( 3 . 78-fold resistance ) . Despite the low level of resistance mediated by pTRP , there are several indications that trp is involved in TUB resistance . First , the signals for protein translation were intact . Second , the levels of resistance were significant for cosTUB2 ( with a 30 kb insert ) , cosTUB2-ΔApaI-III ( with a 26 kb insert ) and pTRP ( with a 3 kb insert ) . Third , genes that conferred resistance by transfection to other drugs , such as terbinafine and itraconazol [21] , vinblastina [45] , primaquine [46] , pentamidine [30] , antimony , miltefosine and amphotericin [41] presented a moderate resistance profile as observed for trp gene . As an example , the squalene synthetase gene ( sqs1 ) has been identified in a cosmid that conferred resistance to terbinafine ( 1 . 47-fold resistance ) in L . major [21] . The analysis of trp transcripts revealed that the RNA level was increased in cosTUB2 compared with LmjF . In contrast , we observed similar RNA expression levels when comparing pTRP transcripts . Interesting , the increased resistance level conferred by cosTUB2 ( 3 . 78-fold resistance ) versus pTRP ( 1 . 91-fold resistance ) was not correlated with the relative trp mRNA abundance . These results indicate that trp expression could be subjected to some negative regulation . The increased transcription of trp in cosTUB2 transfectants compared with pTRP transfectants can be explained by the action of cis and trans elements as regulatory factors of mRNA generated by cosTUB2 and pTRP . Moreover , cosmids may contain regulatory sequences that are missing in the plasmid . Indeed , the length of the cosmid DNA can provide a chromatin-like structure that allows to enhance the transcription and consequently promote an increase in protein translation [47] . Western blot analysis revealed that the protein levels differed between LmjF and the transfected lines ( cosTUB2 and pTRP ) , with an increase of TRP expression in the transfected lines . The sub-cellular immunolocalization of TRP in the ER suggests that it is associated with the secretory pathway [48] . Proteins located in the ER may be related to the stress response , the downregulation of translation , spliced leader silencing and protein misfolding [49] . In addition , GFP-tagged TOR has been previously demonstrated to be present at multiple locations in Leishmania ( i . e . , mitochondria and Golgi/trans Golgi regions ) except the nucleus [24] . TOR appears to act at the protein level and affect the activities and/or concentration of a class of transporters and other proteins [24] . TUB is transported by adenosine permease , but this purine analog , which is toxic to the parasite , must first enter the cell . According to Detke et al . ( 2007 ) , parasites become resistant to this toxic purine nucleoside due to the functional loss of the appropriate transporter by mutation or amplification of the tor gene , which leads to a decrease of TUB entry into the cell [24] . Nucleoside transport in Leishmania is mediate by transporters located in the plasma membrane of the parasite . There are five different members that have different and selective substrate specificities [14] . The regulation of these transporters occurs via salvage pathway because Leishmania does not synthesize purine de novo [10] . The nucleoside transporters of L . donovani , LdNT1 . 1 and LdNT1 . 2 , were previously described . Both transporters mediate adenosine and pyrimidine uptake [15 , 50] , they are members of the equilibrative nucleoside transporter ( ENT ) family [51 , 52] and exhibit approximately 30% amino acid identity with mammalian ENTs [10] . This low identity is due to differences in the nucleoside transporter members between Leishmania and the mammalian host . It has been previously demonstrated that the use of TUB in association with the nucleoside transport inhibitors can result in highly selective toxicity against the parasite , thereby protecting the host against TUB toxicity [20] . LmaNT3 transporter from L . major was reported with a homology of LdNT1 . 1 with 33% amino acid sequence identity [53] . It mediates the hypoxanthine , xanthine , adenine and guanine uptake . Interestingly , its functions are optimal at neutral pH in the promastigote form [54] . In contrast , LmaNT4 has very low transport activity at neutral pH , but it is functional in the acid pH that is found within acidified phagolysomal vesicles of host macrophages during the intracellular amastigote stage of the parasite [54] . It was also demonstrated that Leishmania overexpressing adenosine permease exhibits an increased sensitivity to TUB [24] . In contrast , resistance to TUB occurs due to functional loss of adenosine and guanosine permease through mutation or amplification of the tor gene [22–24] . The reduction in adenosine permease is due to reduction in the amount of transporter per se and to the re-routing of the normal trafficking of this transporter from the plasma membrane to the multi-vesicular tubule lysosome [24] . Even in relation to the nucleoside transporters , we hypothesize that the cross-resistance to pentamidine exhibited by cosTUB2 and pTRP transfectants is TRP-dependent . It has been reported that several of the 12 ENTs family identified in T . brucei with involvement in the salvage pathway can also transport pentamidine [54] . Pentamidine is a second-line drug used as an alternative to the leishmaniasis treatment with pentavalent antimony . The drug enters into the Leishmania promastigote or amastigote via a high affinity pentamidine transporter [55] . The mitochondrion is an important target and the drug is involved in the binding and disintegration of kinetoplast DNA [56–58] . Integration of the pathways involved in TUB and pentamidine resistance can be reinforced by the immunolocalization of TRP close to the perinuclear network , although additional studies are required to elucidate this relationship . In contrast , no cross-resistance was observed for allopurinol . Allopurinol is known to inhibit enzymes of the purine salvage pathway in Leishmania [59] . The mechanism of action of allopurinol is involved in the conversion to ribonucleoside triphosphate analogs and incorporation into RNA , thereby disrupting macromolecular biosynthesis [60] . Interestingly , TRP overexpression led to a 2-fold increase in allopurinol susceptibility [21] . Three other hypothetical proteins ( LmjF . 31 . 2040 , LmjF . 31 . 2050 and LmjF . 31 . 2060 ) , non-coding-RNA ( LmjF . 31 . ncRNA ) and ubiquitin-fusion protein ( LmjF . 31 . 2030 ) were identified in cosTUB2 . Although the term ncRNA is commonly used for RNA that does not encode a protein , it does not mean that the RNA has no genetic information and function . It has been described studies of the involvement of ncRNAs in RNA splicing , editing , translation and turnover [61] . In contrast , ubiquitin is a conserved protein , with a difference of only 3 amino acids between Saccharomyces cerevisiae and humans [62] . Protein ubiquitylation is a recognized signal for protein degradation that can control post-translational modifications [55] . It is also known that internalization and retargeting of membrane proteins is frequently initiated by ubiquitination [63 , 64] . Although LmjF . 31 . ncRNA and LmjF . 31 . 2030 were represented in cosTUB2-ΔApaI-I and cosTUB2-ΔApaI-IV , respectively , no TUB resistance per se was observed . We also verified that TRP contains only one hypothetical transmembrane domain and no putative conserved domain . The identification of a single hypothetical transmembrane domain confirms our co-localization , indicating that it is not a membrane transporter , in contrast with the ENTs described in Leishmania that contain 11 hypothetical transmembrane domains [52 , 65] . The location of the transmembrane domain of TRP in the C-terminal region suggests that this protein can be anchored . Comparative analysis based in TriTrypDB demonstrated that trp is specific in the genus Leishmania , with no ortholog identified in T . brucei or T . cruzi . In silico analysis revealed that trp gene is located in the same genomic region of chromosome 31 of L . infantum ( LinJ . 31 . 2050 ) , L . tarantolae ( LtaP . 31 . 2440 ) and L . braziliensis ( LbrM . 31 . 2270 ) . According to TriTrypDB , trp gene is constitutively expressed , however our findings demonstrated that trp is more expressed in logarithmic phase . This result can emphasize the likely relation with purine pathway and the potential role of this protein during the replication of the parasite . All these results indicate the importance of characterizing a hypothetical protein not only by functional genomics , but also according to its general biological features , allowing the acquisition of new knowledge about signaling pathways , metabolism , stress response , drug resistance and in the identification of new therapeutic targets . Purine transport can be considered a potential target , since the mechanism of action is different in Leishmania and its host . Aoki et al . ( 2009 ) demonstrated that the association with a specific inhibitor of the nucleoside transport for mammalian cells , NBMPR , protects infected mammalian host from the toxic effects of TUB . NBMPR inhibits only the mammalian nucleoside transport , thus protecting the host and not the parasite from the TUB toxicity , similarly as proposed in Schistosoma model [16 , 17] . In conclusion , the TRP , initially annotated as a hypothetical protein was described in this work as involved with TUB resistance .
|
The identification of genes associated with drug resistance has contributed for understanding of the mechanisms of action of compounds against Leishmania , as well as , in the identification of the resistance mechanisms mediated by the proteins encoded by these genes . Differently from the mammalian host , Leishmania is unable to synthetize purine nucleotides de novo and must rescue preformed purines from its host . Due to this metabolic difference between host and parasite , the purine metabolism can be considered as a potential target for drug targeting . TUB is a toxic adenosine analog that was already demonstrated as effective against Leishmania . Using a strategy of gene overexpression after cosmid genomic library transfection , we isolated , mapped , sequenced and identified two genes involved in TUB resistance in L . major . In one of the cosmids , we identified NUPM1-like protein , an atypical multidrug resistance protein previously described in L . amazonensis involved in TUB resistance . The other cosmid contains a novel resistance marker involved in TUB resistance , described here as the TRP . Co-localization of TRP in the ER of LmjF and in silico structural predictions indicated that TRP might be an ER lumen protein . Our findings may be useful to elucidate the purine pathway in the parasite and to understand the role of TRP in the mechanism of TUB resistance .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"glycosylamines",
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] |
2016
|
Characterization of a Novel Endoplasmic Reticulum Protein Involved in Tubercidin Resistance in Leishmania major
|
In 1994 , combined active and passive screening reported 1469 cases from the historic Gambian Human African Trypanosomiasis ( gHAT ) foci of West Nile , Uganda . Since 2011 systematic active screening has stopped and there has been reliance on passive screening . During 2014 , passive screening alone detected just nine cases . In the same year a tsetse control intervention was expanded to cover the main gHAT foci in West Nile to curtail transmission of gHAT contributing to the elimination of gHAT as a public health problem in the area . It is known that sole reliance on passive screening is slow to detect cases and can underestimate the actual true number . We therefore undertook an active screening programme designed to test the efficacy of these interventions against gHAT transmission and clarify disease status . Screening was conducted in 28 randomly selected villages throughout the study area , aiming to sample all residents . Whole blood from 10 , 963 participants was analysed using CATT and 97 CATT suspects ( 0 . 9% ) were evaluated with microscopy and trypanolysis . No confirmed cases were found providing evidence that the gHAT prevention programmes in West Nile have been effective . Results confirm gHAT prevalence in the study area of West Nile is below the elimination threshold ( 1 new case / 10 , 000 population ) , making elimination on course across this study area if status is maintained . The findings of this study can be used to guide future HAT and tsetse management in other gHAT foci , where reduced caseloads necessitate a shift from active to passive screening .
Human African Trypanosomiasis ( HAT ) is caused by species of Trypanosoma brucei s . l . transmitted by tsetse flies , with case fatality rates believed to be close to 100% [1 , 2] . In East and Southern Africa Trypanosoma brucei rhodesiense causes Rhodesian HAT ( rHAT ) , an acute and zoonotic form of the disease . In Central and West Africa Trypanosoma brucei gambiense causes Gambian HAT ( gHAT ) , the chronic form of disease and is generally considered an anthroponosis [3 , 4] . gHAT has been targeted for elimination globally by reducing incidence to less than 1 new case per 10 , 000 people at risk in 90% of foci , alongside a global total burden of less than 2000 cases by 2020 [5] . In 2017 , one of the metrics to assess elimination as a public health problem was revised as ‘total area at risk reporting ≥ 1 case per 10 , 000 people per year’ [6] . The only country where both forms of the disease occur is Uganda with gHAT present in the north west [7] and rHAT in central and south-eastern parts of the country [8] . Unregulated movement of untreated cattle risk rHAT extending its distribution [9] . Control of gHAT classically relied upon medical surveillance involving either active or passive screening of the population followed by treatment of detected cases . In practice , when prevalence decreases , exhaustive active screening by mobile teams is progressively replaced by passive screening with diagnosis of patients presenting at local health centres [10] . Prompt and correct diagnosis of HAT requires specialist diagnostics , facilities and skilled staff which are frequently not present at most health centres in remote rural areas . Consequently , misdiagnosis and under-reporting of HAT cases by passive screening is an acknowledged problem [11–14] . In Uganda , the annual number of reported gHAT cases declined from 2066 in 1990 to 1469 cases in 1994 and on to just nine cases in 2014 [15] . The driver behind this decline being the large active and passive screening programme by Ugandan National Sleeping Sickness Programme and Médecins Sans Frontières ( MSF ) France between 1987 and 2002 [16] . As case numbers declined surveillance shifted from large-scale active screening to passive screening . It is plausible that the current low case numbers could be an underestimate due to less effective screening . The last large-scale active screening in West Nile was undertaken by MSF Spain and the Ministry of Health ( MoH ) supported by WHO in 2010 and 2011 . A total of 74 , 254 people were screened with 22 people diagnosed positive and treated for gHAT at an overall prevalence of 0 . 029% [17] . All parasitological confirmed cases reported since then have been detected at MoH facilities by passive screening only . As a contribution to eliminate gHAT as a public health problem in Uganda , tsetse control operations have gradually been expanding in West Nile since 2011 . This involves the deployment of insecticide treated Tiny Targets [18] throughout the traditional foci of gHAT managed by the Co-ordinating Office for the Control of Trypanosomiasis in Uganda ( COCTU ) supported by Liverpool School of Tropical Medicine ( LSTM ) . To evaluate the status of gHAT in areas where Tiny Targets have been deployed , an active screening survey for gHAT has been conducted in the historical gHAT foci of Arua , Maracha , Koboko and Yumbe . Areas of Arua and Maracha have had the Tiny Target tsetse control project since 2011 , while in Koboko and Yumbe Tiny Targets were introduced in 2014 .
Sampling activities were conducted in accordance with the Helsinki Declaration . This study was designed in collaboration between LSTM , COCTU , and the National Sleeping Sickness Control Program , Ministry of Health ( MoH ) . Ugandan National Council of Science and Technology granted formal ethical approval ( protocol number: HS1749 ) . All samples were collected using the existing national framework for trypanosomiasis screening , employing laboratory technicians from Omugo Health Centre ( one of the principal gHAT diagnostic facilities in West Nile region ) . Care was taken to minimise changes in the techniques used for this study from the standard operating procedure already familiar to the health team . This study investigated the historical gHAT foci situated north east of Arua town . Between 2000 and 2012 the study site generated 1448 gHAT cases out of the national total of 3940 . The foci extend from 3°06’17 . 32” to 3°37’10 . 53” latitude , and 30°56’23 . 28” to 31°25’57 . 65” longitude and encompasses areas of Arua , Maracha , Koboko and Yumbe districts . This study area hosts vector control operations through deployment of Tiny Targets . In Arua and Maracha districts control started at scale in 2012 followed by Koboko and Yumbe in late 2014 [18] . HAT cases reported to WHO from each of these areas during the period 2000–2012 were 689 in Arua and Maracha and 759 from Koboko and Yumbe . These two regions form the sub areas of this survey . The population is largely rural , practising small-scale crop farming focussed on food and tobacco . The rivers in the study area include the Enyau , Kochi and Ore along with their smaller tributaries . These rivers support the typical riverine habitat suitable for Glossina fuscipes fuscipes , the principal T . b . gambiense vector in this area [18 , 19] . Mapping of the study area was carried out using a combination of open source GIS shapefiles and data from the WHO . National boundaries , rivers and water bodies were obtained from DIVA-GIS [27] . Human population data has been obtained from the Centre for International Earth Science Information Network [28] . Data from the HAT Atlas , provided by WHO , were used to produce a map of the gridded density of HAT cases across the study area . This data was used to show density of gHAT cases based upon reported origin position of each case within cells of a 1km2 grid covering the West Nile region . All GIS processing has been conducted using QGIS [29] .
The sampled population comprised 51 . 9% females and 48 . 1% males ( Fig 3 ) and the age distribution was in general accordance with the population composition of Uganda , with more young than old . Direct comparison between results from this study and the United Nations’ collated population for Uganda places the UN’s curve from young to old as being far more gradual [30 , 31] than our data ( Fig 3 ) . Initial CATT-WB screening showed agglutination for 0 . 88% of people screened ( 97/10963 , CI: 0 . 7–1 . 1 ) ( Table 1 ) . Following the diagnostic algorithm of these initial positives , 59 . 2% ( 61/97 , CI: 52 . 5–72 . 5 ) displayed CATT agglutination at 1:4 dilution , 26 . 2% ( 27/97 , CI: 19 . 2–37 . 9 ) at 1:8 dilution and 13 . 6% ( 14/97 CI: 8 . 1–23 . 0 ) at 1:16 dilution . Results of CATT-WB conducted in the sub-areas shows no significant difference ( p = 0 . 8521 ) between Arua and Maracha ( 69/7647 ) and Koboko and Yumbe ( 28/3316 ) . Sixty-seven of the 97 CATT-WB suspects were from the selected study cluster villages . The remaining 30 CATT-WB suspects were from other villages ( total number of identified attendees from unselected villages was 463 ) . From selected sample villages , 0 . 64% were CATT-WB suspects ( 67/10500 CI: 0 . 54–0 . 73 ) . CATT-WB suspects from non-selected villages represent 6 . 48% ( CI: 4 . 4–9 . 1 ) . Number of CATT-WB per sample site is shown in Fig 4 . None of the screened participants that were serologically positive by CATT on whole blood ( 0/97 , CI: 0–3 . 7 ) were confirmed positive using CTC microscopic analysis to identify trypanosomes . Only one CATT-WB suspect had palpable lymph nodes ( lymph was examined microscopically and found negative ) . All CATT-WB suspects were given appointments for follow up screening after three months and 79 of the 97 suspects attended ( 76 . 6% ) , with none confirmed positive by microscopy . As these individuals had previously been identified as CATT suspects , they were not re-examined by CATT during follow up . As there were no cases diagnosed by microscopy it is not possible to compare the two sub areas of this study . Analysis of blood indicated that three of 97 undiluted ( CI: 0 . 6–9 . 0 ) CATT-WB suspects had previously been infected with T . b . gambiense . The original sampling locations of these three individuals is plotted in Fig 4 . All three were from Arua district in neighbouring locations and while it appears from the map that two are from the same village , records show one was from a neighbouring non-selected village . One trypanolysis suspect had agglutination registered for all dilutions of CATT ( 1:4 , 1:8 , 1:16 ) . Two only had agglutination on the whole blood test , no agglutination in dilutions . None had palpable lymph nodes . The individual with consistent agglutination at all dilutions is a past HAT patient who was treated 15 years previously . Geographic assessment of home village locations of the three trypanolysis positive suspects shows they were within 0 . 5km of the Enyau river system , a documented tsetse habitat [18] . There is no significant difference in the findings of trypanolysis between the sub areas ( p = 0 . 5545 ) , 3/66 from Arua and Maracha and 0/28 from Koboko and Yumbe . Five of the CATT-WB suspects have previously been HAT cases and while all had received treatment , their staging status is unclear from records . Four were from selected survey villages and one had come from an un-selected village . WHO’s HAT case data from 2000 and 2012 shows the same villages sampled in this study had produced 18 HAT cases . Four of the 18 ( 22 . 2% ) were identified and resampled during this screening . Given the area that village clusters were selected from has reported 1448 gHAT cases in 892 villages , there was 1430 cases reported to the WHO from the 864 unselected villages .
Reduction of HAT cases in West Nile has been driven by intensive active and passive medical screening and treatment of infected people between the years of 1987 and 2002 [16] . Recently , as case numbers have dropped , active screening has been scaled back in conjunction with reinforcement of passive screening . This shift has been accompanied by establishment of an effective tsetse control programme which has reduced tsetse apparent densities by over 80% [18] . Our results indicate that the previous combined active and passive screening strategy had a successful impact to reduce HAT cases , which has recently been maintained through strategic passive screening . While tsetse control has not produced the low prevalence of gHAT observed , it will have reduced the risk of undiagnosed infected individuals being fed upon and parasite becoming transmitted [38] . While the sampling turnout for this study ( ~72% ) is considered satisfactory , it highlights the difficulties and risk of relying only on screening and treatment for controlling HAT . This is particularly pertinent in less accessible areas where repeated team visits are impossible , leaving many individuals unscreened . The study was focussed on one of the most active foci of gHAT in Uganda and findings have relevance to other foci since all Ugandan gHAT foci are now benefiting from combined reinforced passive screening and tsetse control . Since this study concluded , new cases of gHAT from Uganda have remained low; 4 cases in 2015 , 4 in 2016 and 2 in 2017 ( both of which were in South Sudanese refugees and not Ugandan nationals ) . Deterioration of security in South Sudan has resulted in an influx of >1 million refugees entering West Nile [39] . This is highly relevant to this study and the Ugandan HAT situation . While effort has been invested and gains made against HAT in Uganda , the WHO HAT atlas [40 , 41] shows that there are foci of gHAT in neighbouring areas of South Sudan [15] . Large numbers of people have fled these areas and have settled in refugee camps in various districts of West Nile . Consequently , there is urgent need for raising awareness about HAT in the refugee population and screening for gHAT in refugee camps . Alongside this is a necessity for tsetse control , which focusses on tsetse habitat around the camps and protects the South Sudanese refugees and Ugandan communities from re-emergence of gHAT . This study demonstrates that the transition from active screening to enhanced passive screening has been successful in providing an accurate representation of gHAT prevalence . The authors therefore recommend that enhanced passive screening should form the basis for control in areas where prevalence is low ( be that naturally or having been reduced by active screening operations ) . In regions of high prevalence active screening still has an important role but should be replaced by enhanced passive screening ( as used in West Nile [42] ) once a low prevalence has been achieved . As an example , when active screening operations find fewer than five cases per 10 , 000 people screened , priority can shift to reinforced passive screening combined with focussed active screening at home locations of new cases . Specific thresholds for prioritising reinforced passive screening should be decided by the national MoH with guidance from WHO . This will reduce the burden of cost on the healthcare system . Alongside screening programmes recent advances in vector control and chemotherapy show promising contributions to gHAT control [42] . Considerations should be taken to implement their usage in future operations as outlined below . In Uganda , gHAT elimination as a public health problem was attained on the back of the work done prior to the introduction of large-scale vector control . No cases have occurred in areas where Tiny Targets have been deployed . This provides early indication that vector control has helped to maintain incidence at a very low level through directly interrupting transmission . Tsetse control provides a necessary means of continuing the suppression of HAT transmission [38 , 43] by minimising the risk posed by infected individuals who miss medical screening and could pass on infection . The consistent stand out risk group in this and other studies is the working age males who represent low attendance figures in screening operations . In addition , tsetse control is especially pertinent if trypanosomes can survive undetected , for example in the skin and fat deposits remaining accessible to feeding tsetse , but are elusive to detection by microscopic analysis . Vector control will also negate any risk posed by potentially viable animal reservoir infections of T . b . gambiense [44] . Advances in orally-administered drugs for gHAT are highly promising since they offer the prospect of an easier and safer treatment for HAT cases with a simpler treatment regimen [45] thereby reducing the burden on health care systems . Treatment options such as nifurtimox-eflornithine combination therapy remain valuable particularly as second line treatment against second stage gHAT . Fexinidazole has been proven capable of diffusing outside of blood and lymph system , into both fat and skin [46] . This presents exciting scope for eliminating transmission from any potential asymptomatic cases through treatment of highly reactive serological or trypanolysis suspects ( providing test results are relayed from the lab to local health teams to follow up ) . Mathematical models [38 , 47] predict that combination of tsetse suppression with medical screening intervention is needed to ensure that WHO’s target of eliminating gHAT as a public health problem ( i . e . , < 1 case per 10 , 000 people at risk by 2020 ) is achieved globally . In Uganda , the health care structure is well developed aiding the success of passive surveillance [48] which permitted the possibility of effective control based largely on active and passive screening . The strategy of reducing incidence through vector control will be important as an accompaniment to medical surveys [43] , particularly in regions of gHAT endemicity that lack health centres capable of accomplishing gHAT passive screening .
|
The number of gHAT cases across West Nile , Uganda has declined in the last 20 years . This decline is due to the impact of programmes of active and passive case detection and treatment which have recently been combined with tsetse control operations ( post 2011 ) . We carried out an active survey of gHAT to evaluate the prevalence in areas where vector control has been introduced . Our results confirm that the overall prevalence of gHAT is below 1 case per 10 , 000 people at risk in the historical foci and shows that results from passive screening are providing an accurate picture of gHAT prevalence in the area .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"african",
"trypanosomiasis",
"geographical",
"locations",
"tropical",
"diseases",
"uganda",
"social",
"sciences",
"parasitic",
"diseases",
"parasitic",
"protozoans",
"social",
"geography",
"protozoans",
"neglected",
"tropical",
"diseases",
"infectious",
"disease",
"control",
"africa",
"human",
"geography",
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"and",
"occupational",
"health",
"infectious",
"diseases",
"geography",
"zoonoses",
"protozoan",
"infections",
"trypanosomiasis",
"people",
"and",
"places",
"health",
"screening",
"trypanosoma",
"eukaryota",
"blood",
"anatomy",
"physiology",
"earth",
"sciences",
"biology",
"and",
"life",
"sciences",
"organisms"
] |
2019
|
Gambian human African trypanosomiasis in North West Uganda. Are we on course for the 2020 target?
|
In Latin America , 18 million people are infected with Trypanosoma cruzi , the agent of Chagas' disease , with the greatest economic burden . Vertebrate calreticulins ( CRT ) are multifunctional , intra- and extracellular proteins . In the endoplasmic reticulum ( ER ) they bind calcium and act as chaperones . Since human CRT ( HuCRT ) is antiangiogenic and suppresses tumor growth , the presence of these functions in the parasite orthologue may have consequences in the host/parasite interaction . Previously , we have cloned and expressed T . cruzi calreticulin ( TcCRT ) and shown that TcCRT , translocated from the ER to the area of trypomastigote flagellum emergence , promotes infectivity , inactivates the complement system and inhibits angiogenesis in the chorioallantoid chicken egg membrane . Most likely , derived from these properties , TcCRT displays in vivo inhibitory effects against an experimental mammary tumor . TcCRT ( or its N-terminal vasostatin-like domain , N-TcCRT ) a ) Abrogates capillary growth in the ex vivo rat aortic ring assay , b ) Inhibits capillary morphogenesis in a human umbilical vein endothelial cell ( HUVEC ) assay , c ) Inhibits migration and proliferation of HUVECs and the human endothelial cell line Eahy926 . In these assays TcCRT was more effective , in molar terms , than HuCRT: d ) In confocal microscopy , live HUVECs and EAhy926 cells , are recognized by FITC-TcCRT , followed by its internalization and accumulation around the host cell nuclei , a phenomenon that is abrogated by Fucoidin , a specific scavenger receptor ligand and , e ) Inhibits in vivo the growth of the murine mammary TA3 MTXR tumor cell line . We describe herein antiangiogenic and antitumor properties of a parasite chaperone molecule , specifically TcCRT . Perhaps , by virtue of its capacity to inhibit angiogenesis ( and the complement system ) , TcCRT is anti-inflammatory , thus impairing the antiparasite immune response . The TcCRT antiangiogenic effect could also explain , at least partially , the in vivo antitumor effects reported herein and the reports proposing antitumor properties for T . cruzi infection .
Chagas′ disease affects 16 million people in South America , with 14 . 000 deaths per year and 0 . 7 million disability-adjusted life-years [1] . T . cruzi has a variety of molecules that modulate several effector arms of the immune system [2] , calreticulin ( TcCRT ) being one of them [3] . TcCRT , first isolated in our laboratory [4] , [5] , is highly homologous with human calreticulin ( HuCRT ) [6] , an exceedingly pleiotropic chaperone molecule [7] . In spite of its primary endoplasmic reticulum ( ER ) location , TcCRT is also expressed on the cell membrane [3] . Based on their capacity to bind laminin [8] and to inhibit endothelial cell proliferation , both HuCRT and its N-terminal fragment , vasostatin or N-TcCRT , display antiangiogenic properties in vitro and in vivo [9] , [10] . These HuCRT properties are paralleled by inhibitory activities on several tumor models [11]–[13] . Identifying these properties in TcCRT may define important aspects of the host/parasite interaction . We have recently reported that TcCRT is strongly antiangiogenic in the chorioallantoid membrane in chicken eggs ( CAM assay ) [14] . Since angiogenesis modulators behave differently across species [15] , we verified this effect in different experimental set ups in mammals , Homo sapiens sapiens included . Thus , TcCRT and its vasostatin-like domain , inhibit angiogenesis in the ex vivo rat aortic ring assay . It also affects key cellular angiogenic parameters in human endothelial cell cultures , such as proliferation , chemotaxis and cell morphogenesis into tubular-like structures in Matrigel . These results correlate with TcCRT binding and internalization in these cells . Perhaps , the TcCRT antiangiogenic ( and anti-complement ) properties result in anti inflammatory outcomes , thus inhibiting the host antiparasite immune response . Also , at least a partial explanation for those reports [16] , [17] proposing anti-tumor effects for trypanosome infection is herein provided . Although anti-tumor effects have been reported for several decades now , for a variety of infections with other microbial agents [18] , [19] , pathogen molecules mediating those statistically based tumor resistances , have been poorly defined . In synthesis , here we describe that a parasite chaperone molecule , most likely by interacting with endothelial cells , and inhibiting angiogenesis , interferes with tumor growth .
Human umbilical vein endothelial cells ( HUVECs ) were isolated [20] , following informed patient's written consent ( University of Chile Hospital Bioethics Committee ) . The human endothelial EAhy926 cell line ( kindly provided by Dr . Gareth Owen , Pontifical Catholic University , Chile ) , was maintained in Iscove's Modified Dulbecco's Medium ( IMDM , Invitrogen , USA ) with 10% fetal bovine serum ( FBS , Invitrogen , USA ) and 100 units/ml penicillin/streptomycin ( Sigma , USA ) . HUVECs were 80% pure by flow cytometry and immunofluorescence using anti CD31 monoclonal antibodies ( Sigma , USA ) as a marker . The cells were cultured in M199 medium ( Sigma , USA ) , with 20% FBS , 2 mM glutamine ( Invitrogen , USA ) , 100 units/ml penicillin/streptomycin , 100 µg/ml endothelial cell growth supplement ( ECGS ) ( BD Biosciences , USA ) , and 10 µg/ml heparin ( Sigma , USA ) in gelatin-coated flasks . TcCRT , its R-domain ( R-TcCRT ) and HuCRT were obtained from E . coli [3] , [21] . N-TcCRT ( amino acids 20–193 , GenBank accession no . AF162779 ) was amplified by PCR using Tli DNA polymerase ( Promega , USA ) . Primers were: ( 5′-GGAATTCCACGGTGTACTTCCACGAG-3′ ) and ( 5′- CTCGAGCCAGTCTTCTTCGAGCTG-3′ ) . N-TcCRT DNA was ligated into the EcoRI and XhoI sites of the pET-28b ( + ) plasmid ( Novagen , UK ) . Competent E . coli TOP10F′ bacteria were transformed , plated and selected with 50 µg/ml ampicillin . E . coli BL21 ( DE3 ) pLysS was transformed with the plasmid and grown in the presence of 34 µg/ml chloramphenicol with 50 µg/ml kanamycin . After adding isopropyl β-D-thiogalactoside and 3 h incubation , the cells were sonicated , centrifuged , and the supernatants filtered . The recombinant proteins were purified using His Bind resin ( Novagen , UK ) , eluted with buffer containing 1 M imidazole , and dialyzed against 2 mM Tris-HCl and 150 mM NaCl , pH 7 . 4 . Both , TcCRT and N-TcCRT were tested for endotoxin by the Limulus Amebocyte Lysate Kinetic-QCL assay ( BioWhittaker , USA ) and contained <5 EU/10 mg protein . The R-TcCRT domain ( aa 136–281 ) was expressed and purified as previously described [3] . This ex vivo angiogenesis assay [22] , was performed with slight modifications . Six week old Sprague-Dawley rats , from our Animal Facility were used in this experiment . Briefly , the animals were sacrificed by CO2 inhalation , their thoracic aortas dissected and sliced into 1 mm thick rings . Two or three rings per well were placed on a 24-well plate and embedded in 100 µl Matrigel ( BD Biosciences , USA ) , followed by 30 min incubation . Wells were overlaid with 300 µl of FBS-supplemented M199 medium with 100 µg/ml ECGS and phosphate buffered saline ( PBS ) or several TcCRT concentrations . The rings were incubated for 7 days and visualized under phase contrast in a Nikon Eclipse E400 microscope . Fields were photographed and the length of capillaries measured using Adobe Photoshop software . For each experiment and in sextuplicate , 3 capillaries ( shortest , medium and longest ) per ring were measured . The average length was considered as 100% . The statistical validation of these experiments was defined by the Student's t-test . 24-microwell plates were filled with 300 µl Matrigel/well and polymerized for 1 h at 37°C . 70×103 HUVECs/well were suspended in FBS-supplemented M199 medium , with 100 µg/ml ECGS and several TcCRT , N-TcCRT , lypopolisaccharide ( LPS ) , HuCRT or R-TcCRT concentrations . The cells were layered on the gel . After 6 h incubation , morphogenesis was assessed by phase contrast microscopy and images were imported into the Adobe Photoshop program . Tubular capillary-like structures were quantified by manual counting in 40× fields , in quadruplicates , as previously described [23] . Data were analyzed by one way ANOVA . Values are reported as means ± SEM . Comparison of means was performed by the Bonferroni method . With HUVECs , the assays were performed in Boyden chambers , while Transwell chambers ( Costar , USA ) were used with EAhy926 cells [24] . HUVECs were pretreated for 24 h with PBS , LPS , or variable TcCRT concentrations in FBS-supplemented M199 medium . EAhy926 cells were pretreated with IMDM containing several TcCRT concentrations . 7 . 5×104 HUVECs or 5×104 EAhy926 cells/chamber were washed , resuspended in serum-free medium , and placed in the upper compartment , with or without TcCRT or LPS . Supernatants from NIH3T3 cells ( for HUVECs ) or 10% FBS ( for EAhy926 ) were used as chemo attractants in the lower chamber . After 6 h ( HUVECs ) or 16 h ( EAhy926 ) incubation , the cells on the upper filter surface were removed , and those on the lower surface , fixed and stained . Filters were photographed with CCD optics and a digital analysis system ( Image ProPlus , Media Cybernetics , Silver Spring , MD ) and nine fields per filter were counted ( HUVECs ) . EAhy926 cell migration was measured by densitometry analysis at 595 nm . All experiments were performed in triplicates . Data were analyzed by one way ANOVA . Values are reported as means ± SEM . Comparison of means was performed by the Bonferroni method . These assays were quantified using MTT ( 3-[4 , 5-dimethylthiazol-2-yl]2 , 5-diphenyltetrazoliumbromide , Calbiochem , USA ) or crystal violet reagents . Briefly , in the MTT assay , 2 , 500 HUVECs/well were seeded in sestuplicate in 96-well plate and growth , in the presence of various TcCRT , N-TcCRT or HuCRT concentrations , was assessed at 24-h periods over 4 days . Then , MTT was added , incubated for 4 . 5 h , solubilized in DMSO and the absorbance was read at 550 nm . The same assay was performed with 2 , 000 VERO cells , as a negative control showing that recombinant TcCRT did not affect the in vitro growth of an unrelated cell line . Data were analyzed by one way ANOVA , followed by the Bonferroni test . Values are reported as means ± SEM . In the crystal violet assay , the same number of HUVECs were seeded in gelatin-coated wells and treated with R-TcCRT at different concentrations . The number of viable cells was measured over time with the crystal violet reagent , following standard procedures . TcCRT was labeled with the FluoReporter FITC Protein Labeling Kit ( Molecular Probes , USA ) . HUVECs or EAhy926 cells were incubated with 1 µM TcCRT , FITC-TcCRT or FITC-TcCRT plus 10 µM unlabelled TcCRT , for 1 h . After washing , the cells were fixed with 4% paraformaldehyde , for 15 min at room temperature , washed and mounted in 50% glycerol , containing 4′-6-diamidino-2-phenylindole ( DAPI ) . Slides were visualized in a Nikon Eclipse E400 epifluorescence microscope . Protein uptake was detected by incubating the cells for 30 min , in medium containing 1 µM FITC-TcCRT , alone or in the presence of 25 µg/ml fucoidin ( Sigma , USA ) . Images were collected using the LSM510 Software system attached to a Zeiss ( Oberkochen , Germany ) LSM510meta confocal microscope . The TcCRT and HuCRT effects on in vivo growth of the TA3 MTXR murine mammary tumor cell line was assessed in 2 independent experiments , performed 6 months apart , in adult female A/J mice . Four animals were used in the first experiment and 6 in the second one . In both experiments , the animals were inoculated s . c . , every other day , with 50 µg TcCRT or HuCRT or solvent , during 25 days . At day 0 , the animals were challenged with 5×105 tumor cells . Tumor size was determined with a digital caliper ( Mitutoyo Corp , Japan ) , in a double blind procedure , as previously described [25] . The experiments were validated by using the Wilcoxon Signed Rank test ( GraphPad Prism 4 ) . P values≤0 . 05 were considered as statistically significant . Six week old New Zealand rats and adult ( 20–25 g ) female A/J mice were obtained from our Central Animal Facility . Experiments were performed in compliance with the “Guide for the Care and Use of Laboratory Animals” , National Research Council , Washington DC , USA , 2002 . All procedures with these animals were approved by the local Bioethics Committee ( Bioethics Committee , Faculty of Medicine , University of Chile ) . Surgeries and sacrifices were performed by the Animal Facility Veterinary Surgeons .
Two representative experiments are shown in Figure 1 , A–B . Micro vessels are observed after culturing the aortic rings for 1 week ( Figure 1A , control ) . Incubation with 1 µM TcCRT mediated complete capillary growth abrogation ( Figure 1A , TcCRT ) . A dose-dependent antiangiogenic effect is observed ( Figure 1B ) , until reaching complete capillary growth arrest . In Figure 1C , quantification of this TcCRT inhibitory capacity is shown . At concentrations of 0 . 1 and 1 . 0 µM , about 50% and 100% inhibition is respectively observed . In separate experiments , the vasostatin like N-TcCRT also inhibits angiogenesis in this ex vivo experimental model ( data not shown ) . A set of representative experiments is shown in Figure 2 . In a 5-hour culture , control non-treated HUVECs generated a typical cell network ( Figure 2A ) . Although strong inhibitory effects were observed with 1 µM HuCRT ( Figure 2B ) , when N-TcCRT ( Figure 2C ) and TcCRT ( Figure 2D ) were compared at equal molarities with HuCRT , the effects of the parasite–derived molecules were clearly stronger than those of the human counterpart . Figure 2E shows the quantification of these assays . The TcCRT inhibitory effect was dose-dependent down to 0 . 1 µM ( data not shown ) , while R-TcCRT did not affect capillary morphogenesis ( Figure 2F–H ) . HUVECs migration , as a response to the strong angiogenic factors present in NIH/3T3 cell conditioned media , was inhibited in a dose-dependent manner by TcCRT . LPS , at concentrations similar to those present in the TcCRT 1 µM preparation , showed no detectable effects ( Figure 3A ) . Treatment with TcCRT also significantly inhibited migration of Eahy926 cells in response to FBS , over the same dose range ( Figure 3B ) . Figure 4 summarize these experiments . TcCRT inhibited endothelial cell proliferation in a dose-dependent manner , when they were stimulated with ECGS ( Figure 4A ) . Maximum inhibition ( 60% ) was observed with 1 µM TcCRT , at 96 hours ( Figure 4B ) . A similar activity was also observed when TcCRT or N-TcCRT were added to HUVECs stimulated with basic fibroblast growth factor ( bFGF ) ( Figure 4C ) . R-TcCRT , up to 1 µM , had no significant effects on HUVECs proliferation ( Figure 4D ) . TcCRT did not affect VERO cell proliferation ( Figure 4E ) , used as negative control . Although both HuCRT [8] and TcCRT bind to laminin , only the former interferes with the adhesion of endothelial cells to this molecule ( data not shown ) . Therefore , the TcCRT antiangiogenic effect may be explained by other mechanisms , such as direct interaction with endothelial cells . FITC-TcCRT binds to live HUVECs ( Figure 5C ) . This binding is reversed by a molar excess of the unlabeled protein ( Figure 5D ) . Given the similarity between the DAPI and FITC-TcCRT mediated signals in this experiment ( Figure 5C , merge ) , confocal microscopy was used to test if TcCRT was internalized after binding to the cell surface . After 30 min incubation , TcCRT accumulates around the HUVECs nuclei , in punctuate structures ( Figure 5E ) , a phenomenon also observed in EAhy926 endothelial cells ( data not shown ) . In order to better substantiate the TcCRT internalization by endothelial cells , an enlargement of a representative cell is shown ( extreme right panel in Figure 5E ) . TcCRT internalization seems to be receptor-dependent , since fucoidin , a specific scavenger receptor ligand [26] , [27] , abrogated TcCRT uptake ( Figure 5F ) . The TcCRT and HuCRT effects on the in vivo growth of the TA3 MTXR murine A/J mammary tumor cell line was assessed in adult mice , in two independent experiments , performed 6 months apart ( Figure 6A–B ) . Under the experimental conditions used , only the parasite chaperone molecule displayed significant ( p = 0 . 0078 ) inhibitory effects on this tumor cell line , in both cases ( Figure 6A–B ) . In one experiment ( Figure 6A ) , TcCRT displayed a stronger antitumor effect , than the human orthologue ( p = 0 . 0078 vs p = 0 . 1094 ) . In the second experiment , HuCRT also had an effect ( Figure 6B , p = 0 . 0078 ) . However , again TcCRT had a stronger antitumor effect than HuCRT ( p = 0 . 0078 ) ( Figure 6B ) .
We have shown that TcCRT strongly inhibits capillary growth in the CAM in vivo assay [14] . Since angiogenesis modulators behave differently , not only across species , but also depending on the assay used [15] , we studied the TcCRT antiangiogenic properties in the rat , a natural T . cruzi host . The ex vivo rat aortic ring assay provides a model closer to the physiologic in vivo situation , since endothelial cells are in a quiescent state , in a natural histological environment . In this assay , TcCRT completely abrogates capillary growth , in a dose-dependent manner ( Figure 1 ) . Capillary morphogenesis in Matrigel is a valid in vitro correlate of in vivo angiogenesis . As shown in Figure 2 , when TcCRT , N-TcCRT and HuCRT were compared in their capacities to inhibit morphogenesis , only the parasite-derived molecules significantly interfered with this process . The relevant TcCRT aminoacid sequence spans residues 20–191 , corresponding to N-TcCRT . R-TcCRT did not affect capillary morphogenesis , in spite of its overlapping with N-TcCRT in aminoacids 136–191 . Chemotaxis is an essential step in capillary morphogenesis and angiogenesis . In HUVECs and Eahy926 cells , migration was inhibited in a dose-dependent manner by TcCRT ( Figure 3 ) . Cell migration inhibition by TcCRT may explain ( at least partly ) its potent effects on in vitro capillary morphogenesis and ex vivo capillary formation . These results agree with those describing the HuCRT capacity to increase cell binding to extracellular matrix , with consequent cell migration inhibition [28] , [29] . As shown in Figure 4 , TcCRT and N-TcCRT share the HuCRT capacity to specifically inhibit endothelial cell proliferation , a key initial event in angiogenesis [10] . These effects were not observed in a different cell line , like fibroblasts , used as negative controls . In HuCRT , the smallest anti-proliferative fragment spans aa 120–180 [10] . Since , as observed in the morphogenesis assay , R-TcCRT had no significant effect on HUVECs proliferation , relevant residues also map between aa 20–135 . TcCRT interferes with pro angiogenic bFGF ( Figure 4C ) , by unknown mechanisms . HuCRT also inhibits the proliferation of endothelial cells from diverse origins , such as FBHE [10] , BAECs [30] , HUVECs [31] and ECV304 [32] , in response to bFGF and VEGF . R-TcCRT did not affect HUVECs proliferation ( Figure 4D ) , nor morphogenesis ( Figure 2F–H ) . HUVECs proliferation inhibition by TcCRT may imply its involvement in the cell cycle or , alternatively , in cell death induction . TcCRT added at different concentrations to 24 , 72 and 96 h HUVECs cultures did not induce apoptosis . Therefore , in the TcCRT-mediated inhibition of cell proliferation , a cytostatic effect , rather than apoptosis induction , may be mediated by the parasite molecule . Recombinant proteins from E . coli , are normally contaminated with LPS , an antiangiogenic molecule [33] . In all the experiments discussed above , LPS was ineffective at concentrations equivalent to those present in the recombinant TcCRT preparations . Although both HuCRT and TcCRT bind laminin , only the former interferes with endothelial cell adhesion and , as a consequence , with angiogenesis . Thus , the antiangiogenic TcCRT effects could be explained by other mechanisms , such as direct TcCRT interaction with endothelial cells . Alternatively , TcCRT could be internalized and fulfill other functions in the intracellular compartments . We now show that TcCRT binds to endothelial cells , followed by internalization . The transduction pathways involved are unknown . SREC-I ( scavenger receptor expressed by endothelial cell-I ) could be involved in these phenomena . HuCRT binds SREC-I , is endocytosed , and delivers associated peptides for cross presentation via MHC- I [34] , [27] , a fact compatible with our observations on the fucoidin ( a specific SREC-I ligand [26] , [27] ) capacity to inhibit TcCRT internalization by HUVECs ( Figure 5E ) . Besides being an endocytic receptor , SREC-I is an interesting candidate for signal transduction . Its intracellular domain comprises almost half of the molecule , surprisingly large among known scavenger receptors . It also contains several potential phosphorylation consensus sites [35] , [36] . These results are compatible with the possibility that TcCRT internalization is a requisite to mediate its antiangiogenic effects on endothelial cells . Whether TcCRT interferes with the endothelial cell cytoskeleton , is unknown . Perhaps , the parasite ability to inhibit angiogenesis interferes with immune/inflammatory responses against this aggressor . On the other hand , the role of angiogenesis in solid tumor progression has been long established in a variety of experimental models [37] . For six decades now , several reports have proposed a possible growth inhibitory effects that several T . cruzi strains may have on multiple transplanted and spontaneous tumors , in animals and humans [16] , [17] , [38] . The induction of specific immune anti-tumoral responses [39] and/or the secretion of “toxic substances” by the parasite [16] , [40] were invoked to explain these effects , but no experimental evidences have been provided . Maybe , TcCRT , by interacting with endothelial cells and preventing neoangiogenesis , interferes in tumor growth and metastasis . For these reasons we tested the TcCRT and Hu-CRT capacity to inhibit in vivo the growth of a murine mammary tumor ( TA3 MTXR ) . Only TcCRT displayed significant anti-tumor effects in both experiments . Moreover , the parasite molecule displayed stronger effects than HuCRT . Although maximum efforts were made to perform the experiments under similar conditions , the tumor growth was different by about 2-fold , in the experiments shown in Figure 6 A–B . The cell line is maintained in our laboratories , as ascites tumor in A/J mice , with weekly passages and the experiments were performed six months apart . Thus , although the conclusions drawn from both experiments are basically the same , we cannot rule out minor variations in handling , site of inoculation or in the cell line itself that could explain the different overall tumor growth observed in both experiments . While the prevalence of tumor aggressions in wild and domestic T . cruzi hosts has not been assessed , in humans they may reach almost epidemic dimensions ( i . e . mammary , prostate , ovary and cervix-uterine cancers , taken altogether ) . Thus , the TcCRT capacity to delay tumor growth , together with its anti inflammatory properties ( derived from its complement inhibition capacity ) , may represent an evolutionary parasite adaptation , with final increased infectivity . In synthesis , in this report we show that T . cruzi calreticulin has potent antiangiogenic activities , both on rat arterial ( aortic ring assay ) and human venous ( HUVECs ) endothelial cells . These properties map to the N-TcCRT domain in the parasite molecule . TcCRT plays key in vitro antiangiogenic roles , expressed as inhibition of capillary morphogenesis , proliferation and migration of endothelial cells . TcCRT internalization by endothelial cells is perhaps necessary in the antiangiogenic process . These facts , together with those previously reported by us , showing that TcCRT is a potent in vivo inhibitor of angiogenesis in a third vertebrate species ( CAM assay ) , allow us to propose that the TcCRT antiangiogenic effects may be implicated in inflammatory and antineoplastic effects , with benefits for the parasite in its interactions with the vertebrate host . These findings may open interesting possibilities for the development of new antineoplastic strategies , especially if we consider that the parasite molecule displays stronger antiangiogenic and anti-tumor effects than its human counterpart . Biotechnological implications of these findings may be envisaged . Whether the antiangiogenic properties were consolidated , first in the parasite chaperone molecule , and HuCRT conserved some of these properties , as an evolutionary relict or , alternatively , the parasite hijacked this activity from its vertebrate host , remains an open question .
|
In Latin America , 18 million people are infected with Trypanosoma cruzi , a protozoan that causes Chagas' disease . Vertebrate calreticulins ( CRTs ) are multifunctional , intra- and extracellular calcium binding , chaperone proteins . Since human CRT ( HuCRT ) inhibits capillary growth ( angiogenesis ) and suppresses tumor growth , the presence of these functions in T . cruzi CRT ( TcCRT ) may have interesting consequences in the host/parasite interactions . Previously , we have cloned and expressed TcCRT and shown that , when translocated from the endoplasmic reticulum to the area of trypomastigote flagellum emergence , it promotes infectivity , inactivates the complement system , an innate defense arm and inhibits angiogenesis in the chorioallantoid chicken egg membrane . TcCRT inhibits angiogenesis , since it interferes with endothelial cell multiplication , migration and capillary morphogenesis in vitro , as well as angiogenesis in rat aortic rings . The parasite molecule also displays important antitumor effects . In these activities , TcCRT is more effective than the human counterpart . Perhaps , by virtue of its capacity to inhibit angiogenesis , TcCRT is anti-inflammatory , thus impairing the antiparasite immune response . The TcCRT antiangiogenic effect could also explain , at least partially , the in vivo antitumor effects reported herein and the reports proposing antitumor properties for T . cruzi infection .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"immunology/cellular",
"microbiology",
"and",
"pathogenesis",
"cell",
"biology/cellular",
"death",
"and",
"stress",
"responses",
"cell",
"biology/morphogenesis",
"and",
"cell",
"biology",
"cell",
"biology/cell",
"growth",
"and",
"division",
"cell",
"biology",
"infectious",
"diseases/neglected",
"tropical",
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"cell",
"biology/microbial",
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"immunology",
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"immunology/immunity",
"to",
"infections"
] |
2010
|
Antiangiogenic and Antitumor Effects of Trypanosoma cruzi Calreticulin
|
Generation of induced pluripotent stem cells ( iPSCs ) opens a new avenue in regenerative medicine . One of the major hurdles for therapeutic applications is to improve the efficiency of generating iPSCs and also to avoid the tumorigenicity , which requires searching for new reprogramming recipes . We present a systems biology approach to efficiently evaluate a large number of possible recipes and find those that are most effective at generating iPSCs . We not only recovered several experimentally confirmed recipes but we also suggested new ones that may improve reprogramming efficiency and quality . In addition , our approach allows one to estimate the cell-state landscape , monitor the progress of reprogramming , identify important regulatory transition states , and ultimately understand the mechanisms of iPSC generation .
Recent studies have shown that cellular reprogramming can be achieved by manipulating a small number of genes [1] , [2] . This includes the generation of induced pluripotent stem cells ( iPSCs ) from somatic cells [3]–[6] and the conversion of one differentiated cell type directly to another ( transdifferentiation ) [7]–[10] . These findings hold enormous promise for disease modeling and regenerative medicine . However , the reprogramming efficiency is often low and the mechanistic process of reprogramming remains largely unknown . In addition , recent studies have shown that iPSCs generated by the present recipes are different from embryonic stem cells ( ESCs ) on such as exonic mutation [11] , copy number variation [12] , chromosome aberration [13] , epigenetic [14] and immunogenicity [15] deviation from ESCs . Resolving these problems is essential to realize the full potential of therapeutics based on cellular reprogramming . One of the possible reasons for the above problems of the current iPSCs is that reprogramming recipes utilize suboptimal combinations of reprogramming factors . Most studies to date first identify a pool of candidate reprogramming factors ( around 20 ) that are differentially expressed in two cell types [3] , [4] , [8]–[10] . If overexpression of all these factors can convert one cell type to another , sequential removal or adding of these factors one at a time is then conducted to find whether a factor is crucial for reprogramming , through which a minimal set of reprogramming factors is found . Such a procedure requires a significant amount of effort and the greedy search for combinations of the preselected factors does not necessarily find the optimal reprogramming recipe . Efficiently finding optimal reprogramming recipe requires a systematic search for any perturbation ( not necessarily limited to a preselected set of factors ) to the cell that can achieve the most effective reprogramming . Achieving this goal requires de novo prediction of phenotypes , i . e . predicting the consequences ( reprogramming ) of perturbations . Recent studies showed that gene expression can be predicted based on TF binding information [16]–[18] and prediction of phenotypes based on the topology of genetic networks is feasible [19]–[27] . These studies illustrated the great potential of systems biology approach in understanding fundamental principles of biology and developing therapeutic treatments . However , none of these existing methods was designed for or applied to searching for optimal reprogramming recipe . Therefore , new systems biology methods are still needed for such purpose . Having a mechanistic picture of cellular reprogramming requires a comprehensive understanding of the biological system of interest . There are numerous theoretical studies on cell fate decision based on differential equations , but they often focus on simplified circuits that do not embody the molecular details required to understand the mechanisms of how cellular reprogramming is achieved [28] , [29] . Epigenetic landscape [30] has been used to explain cell differentiation during development and cell fate reprogramming [31]–[33] . The landscape concept has been widely appreciated in protein folding/binding [34] , [35] and more recently in genetic network analysis [36]–[41] . Particularly , recent studies have provided quantitative models for theoretical understanding of development from a landscape viewpoint [40] , [41] . However , the current methods of calculating network landscapes are time consuming and thus limited to small networks ( often <20 genes ) [38] , which cannot illustrate the mechanistic procedure of iPS reprogramming or transdifferentiation . In this study , we present a new approach to systematically search for optimal reprogramming recipes and to provide mechanistic insights in reprogramming human cells from the perspective of the cell-state potential landscape , as encouraged by our previous work on the model organism budding yeast [42] , [43] . Based on a network curated from literature that includes the major regulatory interactions known in human embryonic stem cells ( hESCs ) , we developed a method to make de novo predictions of gene expression changes upon perturbations such as overexpression or knockdown of genes . Our predictions correlated well with knockdown experiments in hESCs . In addition , our model allows efficient calculation of the probability of any cell state for a large network . These features made it possible to systematically search for optimal reprogramming recipes and to establish the cell-state landscape . Without knowledge of any successful reprogramming recipes , the recipes we identified included several experimentally confirmed ones that were only recently published [44] . Our study fills the gap between theoretical and experimental studies on iPSCs , and illustrates a framework to facilitate experimental design and mechanistic interpretation of the experimental observations .
We first collected evidence from literature and manually constructed a genetic network involved in regulating pluripotency and hESC differentiation ( Figure 1A , Table S1 and S2 in Text S1 ) . We did not employ any data mining or bioinformatics methods in constructing the network to avoid false regulatory interactions . We started with a set of marker genes of pluripotency and differentiation lineages ( Table S3 in Text S1 ) and extensively searched the literature for regulatory paths between any pair of genes . This constructed network is composed of direct regulatory interactions between 52 nodes , including the three key regulators of ESC ( Oct4 , NANOG and Sox2 ) , six protein complexes ( Oct4-Sox2 , Oct4-Foxd3 , LEF1-bCat , Mad-Max , Myc-Max , Myc-Sp1 ) as well as marker genes for the differentiation lineages ( Table S3 in Text S1 ) . Considering the difference between human and mouse ESCs , we focused on regulatory interactions that have direct evidence in the hESC . The completeness and correctness of this network were partially confirmed by its capability to correctly predict the gene expression changes upon Oct4 knockdown ( see below ) . As shown in previous studies , estimating the landscape of a network requires calculating the probability of each cell state . To accomplish this task , we considered each protein in the network as either active or inactive , i . e . each node is a binary variable . We then used a dynamic Bayesian network ( DBN ) [45] to model the feedback loops in the network . The probability of each node represents how likely the protein is active . DBN simulates the evolving stochastic characteristics of the network via temporal organization of a 2-time slice Bayesian network ( 2TBN ) . In order to transform the cyclic hESC network to a DBN , we need to break all cyclic regulations and unroll the network into a series of acyclic graphs ( 2TBN ) , in which interface proteins either emit or receive feedbacks in the original network . We employed a searching procedure to identify interface proteins such that the unrolled 2TBN not only reduced the computational complexity but also preserved biological meaningful links ( see Methods and Text S1 for details ) ( Figure 1B ) . As the DBN evolved to its steady state , information was updated and propagated through these interface proteins from the current time slice to the next using the interface algorithm [45] . To parameterize the DBN of the genetic network , ideally one should learn the parameters from a large set of temporal functional data that reflects the regulations between proteins in the network . Due to the lack of such data in hESCs , we designed a knowledge-based model that converted functional links in the curated genetic network to mathematically meaningful parameter constraints ( see Text S2 for details ) [46] , [47] . Next , we exploited the Monte Carlo Markov Chain ( MCMC ) method to sample DBN parameter values based on these constraints . Each set of parameter samples formed an instance of the DBN model . All the model instances were averaged to conduct DBN inference , which allowed calculation of the joint probability of all the proteins in the network given specific evidence . Compared with the previous approaches of calculating network landscapes using either Boolean network or differential equations , our model is much more efficient . Compared with the conventional DBN model , our method avoids determination of the large number of parameters by reverse-engineering . Our study showed that this model was sufficient for reprogramming recipe discovery ( see below ) . To compute the network landscape , we need to consider all possible extracellular conditions . Because the exact transduction of extracellular signals to TFs is largely unknown in hESCs , we chose an alternative approach by manipulating the expression levels of the three key hESC regulators , which are Oct4 , Sox2 and NANOG . This mimics the effects of extracellular conditions for maintaining pluripotency or inducing differentiation . We calculated the joint probabilities of all the nodes in the network when setting Oct4/Sox2/NANOG to various activity levels and then summed these probabilities to estimate the network landscape ( see Text S2 ) . The obtained landscape ( Figure 1C ) represents the steady state probability of the system . The landscape of the system at a certain time during differentiation can be obtained by specifying the activity of the three key hESC regulators . We found two states with significantly higher probabilities than the rest of the states and they respectively correspond to the hESC and differentiated states ( Figure 1C ) , as defined by the activity of the 22 marker proteins ( Table S3 in Text S1 ) . When all the 11 ES markers are active ( 1 ) and when all the 11 differentiation markers are inactive ( 0 ) , the network represents a hESC state; the differentiated state is defined as the opposite activity composition of these 22 markers . These two states are separated by barrier states with smaller probabilities . These barriers prevent transformation between cell types by noise . This result is similar to the epigenetic landscape proposed to describe the differentiation process of ESCs [30]–[33] . To our knowledge , this is the first landscape generated for a genetic network of a reasonably large size ( 52 nodes ) that can reflect the molecular details of regulation on self-renewal and differentiation of hESC . In order to search for reprogramming recipes , we first need to show that our model could predict the consequences of cellular perturbations . Knockdown of master regulators Oct4 or NANOG was recently performed in hESCs and gene expression changes were measured by either microarray ( Won et al . , submitted and [48] ) or PCR [49] . These datasets were used to test our model . In the framework of the DBN , we modeled the knockdown of a gene by clamping its activity value to a specific level and then conducting inference . After the DBN converged , the ratio between the probability of each node in the perturbed and the undisturbed hESC state was calculated . This ratio was compared with the experimental gene expression change . The undisturbed hESC state in this study was hypothesized to be the joint probability of all nodes when the hESC master regulators Oct4 , NANOG and Sox2 were all clamped to an active state ( see Methods and Text S2 ) . When we pooled six OCT4 and NANOG knockdown experiments together in hESC , the DBN predicted values correlated well with the experimental measurements ( Pearson correlation coefficient = 0 . 6731 and p-value = 1 . 34*10−30 ) ( Figure 2 ) . This correlation varied per individual experiment and fell in a range between 0 . 55 and 0 . 92 ( Figure 2 and S1 in Text S1 ) . For example , a comparison between predicted and experimental gene expression changes after day 3 , 5 and 7 of OCT4 knockdown yielded Pearson correlation coefficients of 0 . 83 , 0 . 92 and 0 . 74 , respectively . Note that our predictions were made solely based on the genetic network topology , without any other functional data . The accuracy of de novo phenotypic predictions based on cellular perturbations demonstrated that our DBN model can reliably search for iPS reprogramming recipes . We used two types of criteria to judge the success of a predicted recipe: ( 1 ) Gene expression similarity to the ESC and ( 2 ) Reprogramming efficiency . The predicted state of reprogrammed cells should achieve an expression signature similar to hESCs ( see Text S1 and S2 ) . This similarity was measured by the root mean square deviation ( RMSD ) and the Pearson and Spearman rank correlation coefficients between the reprogrammed and the hESC expression levels ( joint probabilities ) . The reprogramming efficiency was defined as the percentage of cells predicted to be in differentiated states ( 11 hESC markers off and 11 differentiation markers on ) that could be reprogrammed to any attractor representing the ES state ( 11 hESC markers on and 11 differentiation markers off ) . To simulate the heterogeneity of the differentiated cell states , we started from 100 , 000 randomly initialized states of the 30 non-markers in the network , then appropriately clamped the proteins involved in the specific reprogramming recipe , and finally evolved all proteins' states until convergence by following their maximum a posterior ( MAP ) pathways in DBN evolution ( see Text S2 ) . We calculated the expression similarity and reprogramming efficiency for all 163 , 185 possible combinations of overexpressing 4 out of the 46 individual genes in the network . We found that most of the overexpression combinations did not achieve reprogramming as indicated by a reprogramming efficiency of zero . Indeed , only 962 recipes had an efficiency greater than 0 . We found that efficiency was not necessarily correlated with expression similarity ( Figure S2 in Text S1 ) . The expression similarity measurement reflected how similar the final state was to the hESC state by comparing expression levels between all the 52 nodes . Reprogramming efficiency only checked 22 nodes in the network ( 11 hESC markers and 11 differentiation markers ) . A high reprogramming efficiency did not thusly guarantee a final cell state that was similar to a hESC state . Therefore , an optimal recipe should have both high efficiency and high expression similarity to the hESC state ( low RMSD and high Pearson and Spearman correlation coefficients ) . Among the 962 recipes with an efficiency larger than 0 , we found three experimentally validated recipes using OCT4 ( O ) , SOX2 ( S ) , KLF4 ( K ) , c-MYC ( M ) or PRDM14 ( P ) Encouraged by this observation , we further confirmed the success of the 3-factor ( OSK ) and 5-factor ( OSKMP ) experimental recipes ( Table 1 ) . The predicted reprogramming efficiencies were consistent with the experimental observations: OSKMP is more efficient than OSKM , OSKP is more efficient than OSK [44] . When either OCT4 or SOX2 was removed from a recipe , the reprogramming efficiency became zero , which was also observed in the experiments that leaving out either OCT4 or SOX2 could not generate iPSC [44] . In addition , both reprogramming efficiency and gene expression similarity measurements clearly distinguished those successful recipes from the experimentally unsuccessful ones ( Table 1 ) . It is worth noting that our predicted recipes solely relied on the network topology and did not use any information from Chia et al . [44] and our method [46] , [47] , [50] was developed before the publication of [44] . The consistency between our predictions and the experimental observations is encouraging . Our observation of many possible successful reprogramming recipes is consistent with the epigenetic landscape concept [30]–[33] , which also shows that a large number of transition routes exist between two cell types . To confidently select for new reprogramming recipes , we first ranked the 962 4-factor recipes with efficiency>0 plus the experimental 3-factor ( OSK ) and 5-factor ( OSKMP ) recipes using each of the four criteria ( reprogramming efficiency , RMSD , Pearson and Spearman ) . An average rank score was then computed to rank these recipes . Next , based on the individual distributions of RMSD , Pearson and Spearman of these recipes ( Figure S3 ( a ) – ( c ) in Text S1 ) , we set a cutoff of 3-standard deviations for each criterion . 113 4-factor recipes ( including OSKP ) plus the 3-factor ( OSK ) recipe passed the cutoff ( Dataset S1 ) . Based on the averaged rank score , the top 10 recipes under each composition of master regulators are listed in Table 2 . All candidate reprogramming recipes ( Dataset S1 and Table 2 ) contained at least two of the three master regulator genes ( OCT4 , SOX2 , NANOG ) . In fact , all recipes without at least two master regulators had a reprogramming efficiency of zero . OCT4 was indispensible in generating iPSCs while SOX2 and NANOG were mutually replaceable ( Figure 3 ) . KLF4 and PRDM14 could substantially increase reprogramming efficiency . In addition , KLF4 , c-MYC or PRDM14 could be substituted by other factors such as ZIC3 , PBX1 or LMCD1 . Although the mechanisms of how these additional factors function in iPSC generation are unclear , we speculate that their importance is due to either their positive feedbacks to the three master regulators such as PBX1 and ZIC3's activation on NANOG , or repression of the differentiation genes such as LMCD1's repression of GATA6 , which is a repressor of NANOG ( Figure 1 ) . Other than the OSN , we found KLF4 , PBX1 , ZIC3 and PRDM14 occurred more than 20 times in the 113 recipes ( Figure 3 ) . All 16 combinations of these 4 genes with OSN were included in the candidate list ( Dataset S1 and Table 3 ) . We examined whether the knockdown of individual genes would further enhance efficiency of overexpression-only reprogramming recipes . We took the five experimentally confirmed overexpression recipes as the templates and added an additional single gene knockdown . Among the 230 ( = 5×46 ) recipes , only the knockdown of GATA6 could significantly increase reprogramming efficiency for every experimental recipe without deteriorating the gene expression similarity ( Table 4 ) . This may be due to GATA6's repression of NANOG , which when attenuated would improve NANOG expression in a reprogramming recipe . The knockdown of GATA2 also increased the reprogramming recipe efficiency but it did not always increase the expression similarity ( Dataset S2 ) . Over 60% of single gene knockdowns including differentiation markers such as SOX17 , GATA3 , T , CDX2 , hCGa , hCGb , AFP , and FOXA2 had negligible effect on reprogramming efficiency of the original recipes ( see Figure S4 in Text S1 ) . Surprisingly , knockdown of GATA4 , which is a differentiation marker , prevented reprogramming , which might be due to its downregulation of GATA6 . On the other hand , knockdown of non-marker genes including PRDM14 and LMCD1 also deteriorated iPSC generation . Our analyses illustrated the importance of choosing the right combination of perturbations and the usefulness of our computational modeling to quickly screen a large number of recipes . To better understand the mechanisms of iPSC generation , we monitored how reprogramming proceeded . We first calculated the potential of each cell state and found the most probable route ( MAP path ) starting from a differentiated state to its converged final state ( see Methods and Text S2 ) . The reprogramming progresses of the experimentally confirmed recipes are shown in Figure 4A as an illustration . Consistent with the landscape concept , there were many reprogramming paths and all of them went through two barriers , one surrounding the differentiation attractors and another near to the hESC attractors . Different paths had a variable number of steps between the two barriers that defined three modes of reprogramming . Interestingly , if GATA6 was knocked down , all the reprogramming paths had a decreased duration between the two barriers and quickly found the converged state ( Figure 4B and C ) . We also monitored the gene expression changes of the 22 marker genes during reprogramming . Compared with the successful iPSC paths , those converged to non-ES states showed relatively higher expression of GATA6 and GATA2 ( Figure 4D and E ) . Despite the difference of evolving steps , the three modes of reprogramming showed similar temporal gene expression patterns ( Figure 4E , Table 5 and 6 ) , which suggested there might be common transition states during reprogramming . We counted the number of initial states that passed a specific state during their evolution and this number was defined as the dynamical flux of the cell state . Using an arbitrary cutoff of 1000 for the flux , we found several states as highly probable transition states during reprogramming . Figure 5 shows the transition states for the 5 experimental recipes with and without GATA6 knockdown . A prominent pattern emerged from these states was that GATA6 was partially repressed by overexpressed OCT4 and this repression activated NANOG . NANOG's activation then led the consequent activation of pluripotent genes and repression of differentiation genes . Knockdown of GATA6 would enhance this regulation to facilitate reprogramming .
In this study , we curated a genetic network composed of direct regulatory interactions that regulate self-renewal and differentiation of hESC . We developed a machine learning method to make de novo predictions of gene expression changes upon perturbations to the network . Our predictions were validated by a strong correlation between predicted and experimental values in OCT4 and NANOG knockdown experiments . We conducted a systematic search for new recipes that could achieve reprogramming . In addition to recovering several experimentally confirmed recipes , our study provided a wealth of new recipes that serve as a guide for improving experimental iPSC generation . Our theoretical analyses suggested knocking down additional genes , such as GATA6 , would further enhance experimentally known reprogramming recipes . Since our framework is general , it is also applicable to other cellular reprogramming such as transdifferentiation . We defined two criteria to assess whether a recipe could achieve reprogramming: reprogramming efficiency and gene expression similarity to hESCs . We noticed that the calculated reprogramming efficiencies were much higher than the experimentally observed ones of around 0 . 01% to 0 . 1% . There are several possible reasons . In our modeling , overexpression or knockdown of genes is 100% efficient but in reality the efficiency of such perturbations is imperfect . In addition , our modeling only considers whether the recipe can induce pluripotency . It did not consider the proliferation efficiency of the reprogrammed cell . Therefore , the calculated values should be taken as an upper bound of the reprogramming efficiency . When calculating gene expression similarity to hESCs , we used the gene expression profile of the state with active OCT4 , SOX2 and NANOG genes to represent the normal hESC . Once the upstream signaling pathways of these master regulators are defined , a better modeling strategy would incorporate environmental signals into the network and let extracellular signals control the activities of the master pluripotency regulators . Although the present results successfully recovered experimentally validated recipes and suggested new ones , many aspects of our approach can be improved . With the advancement of technologies to manipulate hESCs , new regulatory interactions will be quickly discovered , which will expand the genetic network used in this study . This is expected to improve the accuracy of gene expression prediction and recipe identification . With the availability of additional data such as temporal gene expression of self renewal or induced differentiation , our DBN model can be further trained by incorporating such data to improve/expand the network to better consider proliferation efficiency ( for reprogramming efficiency ) and the resemblance ( for reprogramming quality ) of the reprogrammed cells to the natural targeted cells [43] . In summary , this study and our previous work on yeast [43] suggest a new systematic strategy to find recipes for a desired reprogramming task . Namely , a genetic network regulating the original and target cell types is constructed from existing knowledge or learned by incorporating experimental data . A mathematical model such as DBN or probabilistic Boolean network can be used to conduct inference on gene expression or other phenotypes based on the network , which allows exhaustive or comprehensive search of perturbations ( recipes ) that can convert the phenotype representing the original cell type to that representing the target cell type . This work is a proof-of-concept study that forms the foundation of applying such strategy to find effective recipes to achieve any cellular reprogramming with satisfactory efficiency and quality .
We constructed DBN model , referred as QK-DBN , by utilizing only qualitative knowledge ( QK ) to make quantitative probabilistic inference . In the full Bayesian approach , we consider the model's uncertainty in probabilistic inference and perform probabilistic inference by model averaging: given evidence E , qualitative knowledge Ω and quantitative observation D , the ( averaged ) conditional distribution of the remaining variable X is calculated by integrating over the models: ( 1 ) where P ( D|m ) is the likelihood of the model and P ( m|Ω ) represents the model's prior probability given the qualitative knowledge . In the extreme case , there is no available quantitative data , i . e . D = null . It is still possible to make Bayesian probabilistic inference of Eq . 1 based on the knowledge Ω alone and the evidence E . ( 2 ) Each DBN model m is determined by its structure and parameter vector . The Bayesian model space ( all possible DBN models ) is thus defined by: 1 ) a set of model structures S = {sk , k = 1 , … , K}; 2 ) for each structure sk , a continuous ensemble of conditional probability table ( CPT ) configurations . The BN/DBN model space can be written as M = { ( sk , ) , k = 1 , … , K} . For every structure sk , each possible parameterization in the CPT configuration ensemble defines a member BN/DBN i . e . m = { ( sk , θ ) |k = 1 , … , K} and the distribution of a single BN/DBN model is normalized against all models as ( 3 ) where is the normalization scalar . ( 4 ) We assume that the qualitative knowledge Ω regarding the network structure is consistent and certain , i . e . expert is fully certain about the dependence and direction of the influential relationships between two variables . Then the probability distribution of the model structure P ( sk|Ω ) is a Dirac delta function peaked at a specified structure sk , . Given the k-th model structure , the qualitative constraints define a set of possible parameter configurations ( see Text S2 ) . Thusly , the conditional probability of each parameter vector θ given the k-th structure and qualitative constraints is equal to the probability of this vector belonging to the set of possible parameter configurations defined by the constraints in Ω , i . e . . Therefore , the normalization factor in Eq . 3 and 4 is equal to the size of the constrained parameter space . ( 5 ) Combining Eq . 1 to 5 , we get: ( 6 ) ( 7 ) Integration during Bayesian inference ( in Eq . 4 ) can become intractable by analytical methods . In this case , we employ Markov chain Monte Carlo ( MCMC ) to compute the empirical value of the inference in Eq . 7 . To efficiently generate samples satisfying the constraints , we exploited a rejection sampling method . The idea is to generate more samples from the current “unexplored” region so that the entire parameter space can be explored evenly . First , we generated samples from the proposed distribution and then rejected the samples inconsistent with constraints . The second step was to enhance sampling in the under-sampled space ( see Text S2 for details ) . We built a DBN by unrolling the cyclic hES network ( Figure 1B ) . Since outgoing interfaces separate the current network from the past , only the potential function over the outgoing interface is required when forwarding the network belief at the current step to the next step . Computationally , we need to store this vector of information . The size of this vector is where n is the number of the variables in the outgoing interface and m is the number of the discrete values a variable can take ( m = 2 in this study ) . To reduce computational cost and memory load , we should keep n as small as possible . We have developed a scheme to identify an optimal set of interface nodes . We firstly employed depth-first search [51] to identify all the nodes involved in the non-repeating loops in the curated genetic network as candidates for the interface nodes ( Table S1 in Text S2 ) , which are presumably important for the network's stochastic dynamics . The candidate interface nodes involved in all the non-repeating loops are listed in Table S2 and Figure S3 in Text S2 . Next , to reduce inference complexity , we minimized the interface set . In particular , we ranked all candidates by a heuristic score = B/ ( 1−A ) . If this candidate is a must-cut node ( loops must be cut at this node in order to keep the unrolled graph acyclic , such as auto-regulation node ) , A = 1 , then its score becomes positive infinity ( maximum ) . Otherwise A = 0 and score = B . B is the number of total loops of which this node is a member . If a node is not in a loop , then B = 0 and score = 0 . The values of A and B associated with each interface candidate are listed in Table S2 in Text S2 . We iteratively picked nodes with the biggest score value from the list and cut all outgoing edges which are part of any loop from this node . We repeated this step until all loops were broken . All selected nodes compose the outgoing interface and these nodes are {NANOG , SP1 , Oct4-Sox2 , CDX2 , PIAS1 , GATA6 , FOXA2 , FOXA1} ( yellow-colored nodes in Figure 1B ) . Once these interface nodes were identified , we used the interface algorithm [52] to convert the DBN into junction tree and performed the message-passing algorithm [53] in the junction tree to infer both the joint probability over all variables and the marginal probability of each variable . After message-passing converged and the junction tree became a consistent tree [53] , we calculated the joint probability over all variables in the junction tree as , where and represent the cluster and sepset potentials respectively . The marginal probability of a variable X was calculated by , i . e . we could pick any cluster U or sepset S that contains the variable X and integrate out its potential function against other variables in this cluster or sepset . Let G = {g1 , g2 , … , gN} , represent the gene expression levels of the genes in the network . We assumed the nodes in the DBN model are binary variables which take value of 0 or 1 . Value “0” means that this gene is minimally expressed and “1” means is maximally expressed . The probability of a gene being max-/min-expressed ( under condition E ) is a continuous value in the range of [0 , 1] . When a gene is max-expressed , the probability of its node being “1” is 1 , i . e . P ( gi = 1|E ) = 1 . When a gene is min-expressed , the probability of its node being “1” is 0 , i . e . P ( gi = 1 ) = 0 . Therefore , we consider this probability positively proportional to the expression level . The higher the probability of gi = 1 is , the higher the gene's expression level is . Let gi , max and gi , min represent the maximum and minimum expression level of the i-th gene gi , respectively . gi|E is the expression level of gi under condition E and is gi expression range . The ( marginal ) probability/belief of gi being max-/min-expressed is a random value in [0 , 1] which is linearly proportional to the expression level ( intensity ) of this node: ( 8 ) where Ki is a constant . We can further simplify the above equation by rescaling the minimum expression level of gi to 0 and the expression range to [0 , gi , max|E] . In this case , the probabilities can be simplified as: ( 9 ) The gene expression ratios between two conditions can be directly evaluated as: ( 10 ) where and are unknown scalars . The ratio between the probabilities is linearly proportional to the ratio between the gene expression levels . E1 and E2 are two experimental conditions , such as a control and a knockdown experiment , which are modeled as evidence in DBN . Therefore , we can predict the ratios of the gene expressions between knockdown and control experiments by calculating the ratios between the marginal probabilities of this gene under these conditions . In this study , we assumed that cell states can be uniquely defined by the expression levels of all genes in the genetic network . We can calculate the potential energy of each state as , where P ( Si ) is the probability of i-th state of the network and Ui is the potential energy of this state . A collection of the potential energy values of all states in this network can be represented as , where M = 2N and N is the number of genes in the constructed network . We can calculate all potential energy values in from the converged junction tree ( see above ) . To compute the landscape of the genetic network , we need to consider the potentials under all possible ( at least most representative ) conditions . For our purpose of studying iPSC generation and the differentiation of the hESC , we chose to mimic the most representative scenarios during iPSC generation by varying the expression levels of the three master regulators OCT4 , SOX2 and NANOG in hESCs from 0 to 1 with a small interval of 0 . 2 . In DBN inference , for each combination of the levels of these regulators , we clamped their probabilities accordingly and simulated ( Energy under j-th condition ) . Lastly , we calculated and normalized for all possible j , and then sum them to get the full landscape . Let E1 denote the hESC state . Since the three master hESC regulators OCT4 , SOX2 and NANOG are max-expressed in hESC , without losing generality , we clamped their marginal probability to 1 in our simulation . Then , by QK-DBN inference , we calculated the marginal probabilities of all the genes in the network in the hESC and these probabilities formed a vector of probabilities . Similarly , let E2 represent the perturbation conditions specified by an iPS recipe . To search for iPS recipes in our simulation , starting from the differentiation states ( OCT4 , SOX2 and NANOG initialized to 0 ) , we evolved the DBN given a specific reprogramming perturbation . Consequently , by QK-DBN , for each reprogramming recipe E2 , we calculated the marginal probabilities for all the genes in the network given this perturbation . These marginal probabilities under E2 also form a vector . Since these marginal probabilities are proportional to their gene expression levels , we could directly evaluate a recipe by comparing vectors and . We employed root-mean-square distance ( RMSD ) , Pearson correlation , and Spearman correlation to evaluate the distance from to . We explored the cell state transition pathways during reprogramming . As mentioned above , the cell state is defined by the expression levels of all the genes in the network . In DBN , , where and denote the expression levels of all genes at time t and t−1 respectively . We formulated the probability propagation in DBN for cell states as , where and denote the cell state at time t and time ( t−1 ) . The probability of the current cell state is equal to the integration of the product of state transition probability and the cell state probability at the last time step . To simplify the computation , we applied maximum-a-posterior ( MAP ) estimation to predict the state-transition pathway . Namely , at each time step t , we picked the state which maximizes the cell state posterior at the current time step as the current cell state , . Note that the estimated pathway by MAP is not necessarily global maximum .
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Converting somatic cells back to the stem cell state ( called induced pluripotent stem cells or iPSCs ) exemplifies the recent advancement of cellular reprogramming that holds great promise for developing regenerative medicine . Generation of iPSCs is often achieved by overexpressing three to four genes in somatic cells that are critical for regulating pluripotency . Developing optimal reprogramming recipe is a non-trivial task that requires significant effort . We present here a computational method that can facilitate discovery of effective recipes to generate iPSCs with high efficiency and better quality . In addition , our approach provides a new way to estimate the landscape in the cell-state space and monitor the trajectory of cellular reprogramming from a differentiated cell to an iPS cell . This work provides not only practical recipes for iPSC generation but also theoretical understanding of the reprogramming process .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"physics",
"systems",
"biology",
"developmental",
"biology",
"stem",
"cells",
"induced",
"pluripotent",
"stem",
"cells",
"embryonic",
"stem",
"cells",
"biophysics",
"theory",
"regulatory",
"networks",
"biology",
"computational",
"biology",
"biophysics",
"simulations",
"biophysics",
"signaling",
"networks"
] |
2011
|
Systematic Search for Recipes to Generate Induced Pluripotent Stem Cells
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Mimivirus , or Acanthamoeba polyphaga mimivirus ( APMV ) , a giant double-stranded DNA virus that grows in amoeba , was identified for the first time in 2003 . Entry by phagocytosis within amoeba has been suggested but not demonstrated . We demonstrate here that APMV was internalized by macrophages but not by non-phagocytic cells , leading to productive APMV replication . Clathrin- and caveolin-mediated endocytosis pathways , as well as degradative endosome-mediated endocytosis , were not used by APMV to invade macrophages . Ultrastructural analysis showed that protrusions were formed around the entering virus , suggesting that macropinocytosis or phagocytosis was involved in APMV entry . Reorganization of the actin cytoskeleton and activation of phosphatidylinositol 3-kinases were required for APMV entry . Blocking macropinocytosis and the lack of APMV colocalization with rabankyrin-5 showed that macropinocytosis was not involved in viral entry . Overexpression of a dominant-negative form of dynamin-II , a regulator of phagocytosis , inhibited APMV entry . Altogether , our data demonstrated that APMV enters macrophages through phagocytosis , a new pathway for virus entry in cells . This reinforces the paradigm that intra-amoebal pathogens have the potential to infect macrophages .
Acanthamoeba polyphaga mimivirus ( APMV ) , isolated from amoebae cultures , was identified for the first time in 2003 in our laboratory [1] . This double-stranded DNA virus is classified in the Mimiviridae family [1] . AMPV is probably responsible for pneumonia as suggested in human pneumonia [2] , and evidenced in APMV-inoculated mice [3] . APMV is a giant icosahedral enveloped virus surrounded by fibrils about 750 nm in diameter [4] , a morphology that is reminiscent of that of Iridoviruses , Asfarviruses and Phycodnaviruses . It is larger than that of small mycoplasma , such as Ureaplasma urealyticum , and is comparable in size to that of intracellular bacteria , such as Rickettsia conorii or Tropheryma whipplei [1] , [2] . It has been suggested that APMV infects ameoba by phagocytosis , but this has not been demonstrated [5] . Viruses have evolved a variety of mechanisms to deliver their genes and accessory proteins into host cells . The first step of viral invasion consists of passing through the host cell's plasma membrane , which is a major barrier for invading agents . Several internalization pathways have been described , differing in the size of the endocytic vesicles , the nature of the cargo and the mechanism of vesicle formation . They include clathrin-mediated endocytosis , caveolin-mediated endocytosis , macropinocytosis and phagocytosis [6] ( Figure 1 ) . Clathrin-mediated endocytosis is characterized by the clustering of ligated transmembrane receptors into clathrin-coated pits of about 120 nm . Vesicular stomatitis virus and Semliki Forest virus enter host cells through clathrin-mediated endocytosis [7] , [8] , although vesicular stomatitis virus might also entry host cells through a degradative endosome-mediated endocytosis [9] . Endocytosis can also involve caveolae pits , which are small vesicles of 50 to 80 nm enriched with caveolin , cholesterol and sphingolipids; simian virus uses this route to enter host cells [10] ( Figure 1 ) . The criterion of particle size is not sufficient to predict the mechanism of internalization , since clathrin and caveolin are also involved in the internalization of particles larger than 1 µm by macrophages [11] , [12] . Macropinocytosis traps large amounts of macromolecules and fluid . This endocytic pathway , which is independent of receptors and dynamin-II , is associated with actin-dependent plasma membrane ruffling [7] , [13] and is inhibited by amiloride analogs [14] , [15] . Macropinocytosis leads to the formation of macropinosomes , which are large vesicles ( >1 µm ) characterized by the presence of rabankyrin-5 [16] . It has been shown that , in certain conditions , vaccinia virus and human immunodeficiency virus are able to use macropinocytosis to invade host cells [17] , [18] . Phagocytosis , which is restricted to professional phagocytes , consists of the uptake of large particles ( >500 nm ) , microorganisms , cell debris and apoptotic cells . It is initiated by the interaction of cell surface receptors , such as mannose receptors , Fc receptors and lectin receptors , with their ligands , which are present at the particle surface , and leads to particle internalization through an actin-dependent mechanism [19] . In contrast to macropinocytosis , phagocytosis requires dynamin-II , a ubiquitously expressed GTPase that has a critical role in the scission of forming clathrin-coated endocytic vesicles from the plasma membrane and the formation of phagosomes . Indeed , dominant negative forms of dynamin-II inhibit phagocytosis at the stage of membrane extension around particles [6] , [7] , [20] . Herpes simplex virus infects target cells , primary human corneal fibroblasts and Chinese hamster ovary cells , through a phagocytosis-like mechanism [21] , but such a mechanism is not restricted to professional phagocytes . In the present study , we provide evidence that APMV particles were internalized by macrophages , but not by non-professional phagocytic cells , leading to a productive cycle of virus replication . We also demonstrate that APMV invaded macrophages through phagocytosis . This is the first evidence that a virus is internalized by macrophages via a mechanism normally used by bacteria and parasites .
In first intention , we observed that APMV is internalized by professional phagocytes , but not by non-professional phagocytic cells ( Figure 2A ) . Indeed , APMV enters human myeloid cells , including circulating monocytes , monocyte-derived macrophages and THP-1 myelomonocytic cells . APMV also enters mouse myeloid cells , such as bone marrow-derived macrophages ( BMDM ) , RAW 264 . 7 macrophages and J774A . 1 macrophages . In contrast , APMV was not internalized by non-phagocytic cells , including human lung fibroblast HEL299 , mouse fibroblast L929 cells , human epithelial A431 or HeLa cells , or Neuro-2a mouse neuronal cells . The absence of APMV internalization by non-phagocytic cells was not due to delayed uptake or a low viral burden , since APMV particles were not internalized when the incubation time and the viral load were increased . Our results clearly show that APMV infected phagocytic cells but was unable to infect non-professional phagocytes . For technical reasons , we decided to use RAW 264 . 7 macrophages to investigate the mechanism of APMV entry . The uptake of APMV by RAW 264 . 7 macrophages was assessed by real time PCR . With a PFU-to-cell ratio of 10∶1 , APMV uptake was detected after 2 hours and increased thereafter . It reached a plateau after 8 hours that was maintained for 24 hours ( Figure 2B ) . The uptake increased in a dose-dependent manner . Using a PFU-to-cell ratio of 50∶1 , viral DNA copies were detected after 1 hour of incubation with macrophages and increased to a plateau after 8 hours . When macrophages were infected with APMV using PFU-to-cell ratios of 100∶1 and 200∶1 , the time course of infection was similar to those observed with lower doses of viral particles . The number of viral DNA copies associated with macrophages was roughly proportional to the infective doses of APMV ( Figure 2B ) . In subsequent experiments , a dose of 50 PFU per cell was used to infect cells for 6 hours: this time was defined as t = 0 for further experiments . The infection cycle of APMV within RAW 264 . 7 macrophages was also evaluated . Macrophages were infected for 6 hours ( t = 0 ) , extensively washed to discard unbound viruses , and incubated for an additional period of 30 hours . As determined by real time PCR , the number of viral DNA copies increased significantly ( p<0 . 05 ) during this period ( Figure 2C ) . APMV was cytopathogenic for macrophages , since only 23±13% of macrophages were viable 30 h after infection . The increased number of viral DNA copies was associated with cycles of productive APMV replication . Indeed , serial dilutions of macrophage extracts were added to amoebae and amoebae lysis was determined . APMV particles produced by macrophages replicated within amoebae ( Figure 2D ) , indicating that APMV internalization by RAW 264 . 7 macrophages led to a productive cycle of viral infection . Taken together , these data suggest that APMV infected the macrophages and led to a productive cycle of viral infection . As clathrin- and caveolae-mediated endocytic pathways are usually used by viruses to enter and infect cells , their role in APMV entry was investigated by immunofluorescence and confocal microscopy . Macrophages overexpressing GFP-caveolin-1 ( GFP-Cav1 ) were incubated with APMV for 15 min; APMV particles did not colocalize with GFP-caveolin-1 ( Figure 3A ) . The lack of colocalization of viral particles with GFP-caveolin-1 was not due to delayed acquisition of GFP-caveolin-1 , since any colocalization was observed over a period of 8 hours . However , APMV particles colocalized with clathrin ( Figure 3B ) . After 15 min of incubation , 14±5% of viral particles colocalized with clathrin . This percentage increased after 30 min , reached a maximum at 1 hour ( 83±14% ) and progressively decreased to reach 2±0 . 5% at 8 hours ( Figure 3C ) . To assess the role of clathrin in APMV internalization by macrophages , we used chlorpromazine , an inhibitor of clathrin-mediated endocytosis [22] . The activity of chlorpromazine was tested through the inhibition of transferrin uptake . Chlorpromazine inhibited transferrin uptake in a dose-dependant manner with 50 µM chlorpromazine mediating maximum inhibition ( Figure 4A and Figure 4B ) . Chlorpromazine-treated macrophages were incubated with APMV for 6 hours in the presence of 20 µM monensin to limit cell toxicity resulting from long-term incubation with chlorpromazine [22] . APMV uptake by RAW 264 . 7 macrophages was not affected by chlorpromazine regardless of its concentration ( Figure 4B and Figure 4C ) . In these experimental conditions , chlorpromazine at concentrations between 5 and 50 µM did not affect the viability of macrophages ( Table 1 ) . As an alternative approach to the use of inhibitory drugs , we used the expression of a dominant negative mutant of Eps15 ( EΔ95/295 ) that inhibits clathrin-dependent endocytosis [23] . RAW 264 . 7 macrophages were transfected with a construct encoding a GFP fusion protein of the Eps15 deletion mutant ( GFP-EΔ95/295 ) and a construct encoding GFP as control . First , GFP-EΔ95/295 activity was tested through the inhibition of transferrin uptake ( Figure S1 ) [24] . The expression of GFP did not affect the transferrin uptake ability of macrophages ( Figure S1A and Figure S1B ) since transferrin was completely internalized by GFP-expressing macrophages as compared to untransfected cells . In contrast , overexpression of GFP-EΔ95/295 inhibited transferrin uptake by RAW 264 . 7 macrophages ( Figure S1C and Figure S1D ) while GFP-overexpressing macrophages internalized transferrin ( Figure S1B and Figure S1D ) . Second , RAW 264 . 7 macrophages overexpressing GFP-EΔ95/295 or GFP were infected with APMV for 6 hours , and APMV uptake was determined by immunofluorescence . The expression of GFP had no effect on APMV internalization since 94 . 5±4% of APMV particles were internalized by GFP-expressing macrophages as compared to untransfected cells ( Figure 5A and Figure 5B ) . Macrophages overexpressing GFP-EΔ95/295 also internalized APMV ( Figure 5C ) : 96±3% of APMV particles were internalized by macrophages compared to GFP-expressing macrophages ( Figure 5D ) . Taken together , these data suggest that caveolin-1 and clathrin are not involved in APMV uptake despite the colocalization of viral particles with clathrin . As the degradative endosome-mediated endocytic pathway might be used by viruses to enter and infect cells , its role in APMV entry was investigated using Lamp-1 ( Lysosomal-associated membrane protein-1 ) , a marker of late endosomes and lysosomes , and the lysotracker red DND99 , a weakly basic amine that selectively accumulates in compartments with low pH such as lysosomes . RAW 264 . 7 macrophages were incubated with APMV for different periods of time , and the colocalization of the organisms with Lamp-1 and lysotracker red DND99 was assessed by immunofluorescence and confocal microscopy . APMV particles did not colocalize with Lamp-1 ( Figure 6A ) and lysotracker red DND99 ( Figure 6B ) . This was not due to delayed colocalization of viral particles with Lamp-1 and lysotracker red DND99 since any colocalization was observed over a period of 6 hours ( Table 2 ) . These results suggest that APMV entry into macrophages did not involve the late endocytic pathway . Since endocytic pathways such as clathrin- , caveolin- and degradative endosome-mediated endocytosis were not involved in APMV entry , we asked whether APMV used macropinocytosis or phagocytosis to invade macrophages . The uptake of APMV by RAW 264 . 7 macrophages was studied by electron microscopy . Since APMV particles are large , this study was performed without immunolabeling ( Figure 7A ) . Viruses were bound directly at the cell body ( Figure 7B ) or attached to elongated cellular extensions ( Figure 7C ) . Then , cup-like indentations were formed at the cell surface ( Figure 7D ) , large cell extensions started to embrace viral particles ( Figure 7E ) and , ultimately , completely surrounded these particles ( Figure 7F ) . Subsequently , a large smooth-surfaced endocytic vesicle was detected under the plasma membrane ( Figure 7G ) . The internalization process of APMV was rapid since viral particles were frequently found within 5 min . Later , endocytic vesicles were found deeper in the cytoplasm ( Figure 7H ) and , occasionally , fused with each other ( Figure 7I ) . The morphology of the internalized vesicles did not obviously change up to 3 hours post-infection ( p . i . ) ( data not shown ) . As the formation of protrusions is usually associated with macropinocytosis and/or phagocytosis , our results suggest that APMV infects macrophages through macropinocytosis or phagocytosis . Since actin polymerization is required for the phagocytic and macropinocytosis cup formation [6] , [25] , we examined the role of the actin cytoskeleton on APMV uptake by RAW 264 . 7 macrophages . First , the interaction of APMV particles with filamentous actin ( F-actin ) was investigated using Alexa Fluor 488-phalloidin and confocal microscopy . F-actin accumulated at the site of APMV entry ( Figure 8A ) . About 90% of APMV particles were associated with F-actin accumulation after a 5-min incubation with macrophages . A 3D reconstruction showed that F-actin surrounded viral particles ( Figure 8B and Video S1 ) . Second , RAW 264 . 7 macrophages were pretreated with cytochalasin D , an inhibitor of actin polymerization , for 30 min and then infected with APMV particles for 6 hours in the presence of cytochalasin D . Viral uptake was determined by immunofluorescence . Cytochalasin D inhibited APMV entry in a dose-dependent manner: in the presence of 0 . 2 and 1 µM of cytochalasin D , infection was decreased by 32±8% and 73±3% , respectively , and cytochalasin D at 5 µM completely inhibited APMV uptake ( Figure 8C ) . The inhibition of APMV entry was not due to a toxic effect of cytochalasin D on macrophages , since cytochalasin D did not affect macrophage viability ( Table 1 ) . These results show that APMV uptake by macrophages is related to the reorganization of F-actin cytoskeleton . Since PI3Ks are known to be involved in macropinocytosis and phagocytosis , we examined the potential role of PI3Ks in APMV entry . RAW 264 . 7 macrophages were pretreated with LY294002 , a specific inhibitor of PI3Ks , for 30 min , then infected with APMV for 6 hours in the presence of LY294002 , and viral entry was determined by immunofluorescence . LY294002 inhibited the internalization of APMV in a dose-dependent manner ( Figure 9A ) . This inhibition was not due to a toxic effect of LY294002 since viability of RAW 264 . 7 cells was not affected by LY294002 ( Table 1 ) . We also studied the phosphorylation of kinases such as Akt and ERK . Akt was phosphorylated after 5 min of macrophage exposure to APMV . The phosphorylation of Akt was transient since it was undetectable thereafter ( Figure 9B ) . ERK-1 and ERK-2 were also phosphorylated after 5 min of stimulation; however , their activation was sustained for at least 1 hour ( Figure 9B ) . Taken together , these results show that the PI3K pathway was involved in APMV entry into macrophages . To investigate the role of macropinocytosis in the entry of APMV into macrophages , we studied the effect of an inhibitor of macropinocytosis on APMV entry and the colocalization of APMV with rabankyrin-5 , a marker of macropinocytosis . 5-ethyl-N-isopropyl amiloride ( EIPA ) is a specific inhibitor of macropinocytosis that blocks Na+/H+ exchange [14] , [15] . First , EIPA activity was tested through the inhibition of FITC-dextran uptake [14] . EIPA at 10 µM partially inhibited FITC-dextran uptake with complete inhibition at 100 µM ( Figure 10A and Figure 10B ) . Second , RAW 264 . 7 macrophages were pretreated with different doses of EIPA for 30 min , infected with APMV for 6 hours in the presence of EIPA , and APMV uptake was determined by immunofluorescence . APMV uptake was not affected by EIPA , regardless of its concentration ( Figure 10C and Figure 10D ) . In these experimental conditions , EIPA between 10 and 100 µM did not affect the viability of macrophages ( Table 1 ) . Third , we studied the colocalization of APMV with rabankyrin-5 , a marker of macropinocytosis , using FITC-dextran ( MW , 500 . 000 Da ) as a positive control [16] . After 5 min , FITC-dextran fully colocalized with rabankyrin-5 ( Figure 11A ) . In contrast , APMV particles did not colocalize with rabankyrin-5 ( Figure 11B ) . The absence of colocalization of APMV with rabankyrin-5 was not due to delayed acquisition of rabankyrin-5 , since APMV and rabankyrin-5 did not colocalize over a period of 5 min to 6 hours ( data not shown ) . Taken together , these results showed that APMV particles did not enter macrophages through macropinocytosis , suggesting a role for phagocytosis in APMV entry . Since phagocytosis , but not macropinocytosis , requires dynamin-II , we investigated the role of dynamin-II in APMV uptake by immunofluorescence . RAW 264 . 7 macrophages were transfected with GFP-tagged dynamin-II ( dynII-wt ) , a dominant-negative variant of dynamin-II ( dynII-K44A ) , and GFP as a control . First , we controlled that dynamin-II was not involved in other endocytic pathways than phagocytosis [26] . Indeed , we investigated the involvement of dynamin-II in clathrin-mediated endocytosis through the transferrin uptake inhibition ( Figure S2 ) . The expression of GFP had no effect on the ability of macrophages to internalize transferrin ( Figure S2A and Figure S2B ) . Macrophages transfected with dynII-wt ( Figure S2C ) or dynII-K44A ( Figure S2D ) also internalized transferrin ( Figure S2E ) , demonstrating that dynamin-II is not required for clathrin-mediated endocytosis . We also investigated the role of dynamin-II in macropinocytosis through the inhibition of dextran uptake ( Figure S3 ) . The expression of GFP had no effect on the ability of macrophages to internalize fluorescent dextran ( Figure S3A and Figure S3B ) . Macrophages transfected with functional dynamin-II ( Figure S3C ) or dynII-K44A ( Figure S3D ) internalized dextran in a similar way ( Figure S3E ) demonstrating that dynamin-II is not required for macropinocytosis . Second , the role of dynamin-II in the phagocytosis process was checked using Mycobacterium avium , a bacterium known to enter macrophages through phagocytosis [27] . The expression of GFP had no effect on the ability of macrophages to internalize M . avium ( Figure 12A and Figure 12B ) : 89±7% of organisms were internalized by GFP-transfected macrophages compared to untransfected cells . Macrophages transfected with dynII-wt also internalized M . avium ( Figure 12C ) . In contrast , M . avium internalization was significantly ( p<0 . 05 ) decreased in macrophages transfected with dynII-K44A ( Figure 12D ) as compared to macrophages expressing a functional dynamin-II , since only 18±6% of bacteria were internalized by macrophages transfected with dynII-K44A ( Figure 12E ) . These results clearly show that dynamin-II was required for phagocytosis . Third , the role of dynamin-II in APMV entry was investigated . The expression of GFP had no effect on the ability of macrophages to internalize APMV ( Figure 13A and Figure 13B ) : 90±5% of APMV particles were internalized by GFP-expressing macrophages as compared to untransfected cells . Macrophages transfected with functional dynamin-II also internalized APMV ( Figure 13C ) . In contrast , macrophages transfected with the dominant-negative variant of dynamin-II did not internalize APMV ( Figure 13D ) . In these cells , only 13±4% of APMV particles were associated with macrophages as compared to macrophages that expressed a functional dynamin-II ( p<0 . 05 ) ( Figure 13E ) . These results clearly show that the entry of APMV within macrophages occurs through phagocytosis .
Different internalization pathways are used by viruses ( Figure 1 ) , including clathrin- or caveolae-mediated endocytosis , macropinocytosis , phagocytosis-like and other endocytic pathways that are poorly characterized such as degradative endosome-mediated endocytosis or phagocytic-like process [9] , [28] . To our knowledge , no virus has yet been described to enter professional phagocytes by phagocytosis , a mechanism used by phagocytic cells to ingest particles of more than 0 . 5 µm or by bacteria and parasites to infect eukaryotic cells . APMV is able to multiply rapidly in Acanthamoeba polyphaga , a free living amoebae that possesses major phagocytic activity [2] . It was previously found that microorganisms that replicate in amoeba , such as bacteria or yeasts , should be able to survive to the microbicidal activity of macrophages [29] . The role of professional phagocytes , namely monocytes , macrophages and neutrophils , is to eliminate invading microorganisms through phagocytosis , an active and highly regulated mechanism that involves specific cell surface receptors and signaling cascades mediated by the Rho family of GTPases [6] , [19] . It has been recently shown that a phagocytosis-like process is used by herpes simplex virus , a relatively large virus , to infect fibroblastic cells , which are non-professional phagocytes [21] . Several lines of evidence showed that APMV entered macrophages via a phagocytosis process . First , APMV particles entered different professional phagocytes but were unable to infect non-professional phagocytes , although we cannot exclude the hypothesis that a macrophage specific receptor is needed for APMV entry . Moreover , APMV uptake by phagocytes led to a productive cycle of functional viruses . Second , the engulfment of APMV required F-actin . Indeed , cytochalasin D , which inhibits actin polymerization , blocked APMV entry in macrophages in a dose-dependent manner . F-actin labeling with a specific probe and ultrastructure analysis showed cell membrane protrusions at the entry site of APMV and 3D-image reconstruction demonstrated a progressive engulfment of viral particles . Similar indentations and F-actin involvement have been described to be associated with macropinocytosis or phagocytosis events [16] , [19] , suggesting that APMV internalization could occur through macropinocytosis or phagocytosis . Third , we clearly showed that APMV did not enter macrophages through macropinocytosis since EIPA , a specific inhibitor of macropinocytosis , did not affect APMV entry into macrophages . Furthermore , rabankyrin-5 , a rab5 effector that localizes in large vacuolar structures corresponding to macropinosomes [16] , did not colocalize with APMV . Fourth , activation of the PI3K pathway mediates multiple cellular functions , including phagocytosis [19] . We showed that APMV activated PI3Ks , since a specific inhibitor of PI3Ks , LY294002 , blocked APMV entry into macrophages . In addition , APMV activated downstream effectors , such as Akt and ERK . The classical endocytic pathway might be involved in or contribute to AMPV entry into macrophages , since caveolae- and clathrin-dependent endocytic routes , known to be used by viruses to infect cells , are also involved in the phagocytic process [11] , [12] . First , we found that APMV did not colocalize with caveolin-1 , suggesting that a caveolae-dependent pathway was not involved in APMV entry . Second , we found that APMV colocalized with clathrin , but the prevention of clathrin-coated pits by chlorpromazine and the over-expression of a dominant negative mutant of Eps15 did not affect APMV uptake by macrophages . It has also been demonstrated that Chlamydia colocalize with clathrin , but the knock-down of clathrin does not affect the bacterial entry [11] , [12] . Clearly , these results exclude a role for the clathrin-dependent endocytic routes in APMV uptake by macrophages . Degradative endosome-mediated endocytosis [9] may be also involved in APMV uptake by macrophages . However , APMV did not colocalize with Lamp-1 and lysotracker red DND99 , suggesting that the degradative endosome-mediated endocytosis was not involved in APMV internalization . Finally , we showed direct evidence that APMV entered macrophages through phagocytosis . We focused on dynamin-II because it is essential for phagocytosis , but not for macropinocytosis [6] , [20] , [26] or clathrin-mediated endocytosis [26] . Dynamin-II is essential for the formation of macrophage phagosomes and functions at the stage of membrane extension around the particle [20] . We showed that macrophages transfected with a dominant-negative mutant of dynamin-II , which cannot bind or hydrolyze GTP , exhibited decreased APMV uptake , whereas the active form of dynamin-II enabled APMV entry . However , we cannot rule out a contribution of a lipid raft , caveolin-independent mechanism in APMV entry [30] . Our results demonstrate that the giant virus APMV penetrated macrophages through a phagocytic process normally used by bacteria or parasites ( Figure 14 ) . Our finding adds one supplementary pathway to already known strategies of virus to enter cells ( Figure 1 ) . APMV uptake by macrophages led to a productive viral cycle , suggesting that APMV used a strategy not previously reported to survive and replicate within host cells . One of the most intriguing observations in this study is that the process of APMV internalization by macrophages closely resembles APMV internalization by amoebae at the cellular level [5] . As a Trojan horse , free-living amoebae might play a role as a reservoir for intracellular bacteria , in the transmission of pathogens , the selection of virulence traits and in the adaptation of bacteria to macrophages . In support of this , several amoebae-resistant microorganisms , such as Legionella pneumophila , Coxiella burnetii , Parachlamydiaceae and Cryptococcus neoformans [29] , [31] , which are fastidious intracellular bacteria and emerging pathogens , have been shown to be pathogens for macrophages . However , this is the first demonstration that a virus pathogen for amoeba might be also a pathogen for macrophages . In our study , in addition to demonstrating that APMV infects macrophages through phagocytosis , we highlight the fact that APMV is a pathogen for macrophages as it is for amoebae [2] , suggesting that phagocytic cells are a human target of APMV . Comparative studies of traits permissive for amoebae and macrophage survival may reveal additional insights into the pathogenesis of APMV infection . We can hypothesize that APMV replicates within alveolar macrophages , leading to human and murine pneumonia . This may help to develop new therapeutic approaches for pneumonia resistant to current treatments .
APMV particles were isolated and purified from infected amoebae as previously described [1] . Briefly , APMV-rich-supernatants of infected amoebae were collected and viruses were purified on gastrographin . Purified viruses were then suspended in PBS before being stored at −80°C . The viral titre was determined as follows . Serial dilutions of APMV particles were incubated with 105 amoebae per well in 24-well plates containing Page's modified Neff's amoeba saline [32] . The plates were incubated at 32°C and cells were examined daily to determine amoebal lysis [3] . Amoebal cocultures were fixed with 10% formaldehyde and subsequently stained with 1% crystal violet . The results are expressed as plaque-forming units ( PFU ) /ml . Human monocytes were isolated from peripheral blood mononuclear cells and differentiated into macrophages , as previously described [33] . Mouse BMDM were obtained from a 7-day culture of bone marrow cells in RPMI 1640 containing 10% fetal calf serum ( FCS ) and 15% of a supernatant of L929 cells rich in Macrophage Colony-Stimulating Factor [34] . Human monocyte THP-1 cells ( ATCC N° TIB-202 ) were also grown in RPMI 1640 containing 10% FCS . Mouse RAW 264 . 7 macrophages ( American Type Culture Collection , ATCC N° TIB-71 ) and J774A . 1 macrophages ( ATCC N° TIB-67 ) , and human epithelial A431 cells ( ATCC N° CRL-1555 ) were grown in Dulbecco's Modified Eagle Medium ( DMEM ) high glucose containing 10% FCS . Mouse neuronal Neuro 2A cells ( ATCC N° CCL-131 ) , mouse fibroblast L929 cells ( ATCC N° CCL-1 ) , human lung fibroblast HEL299 cells ( ATCC N° CCL-137 ) and human epithelial HeLa cells ( ECACC 93021013 ) were grown in minimum essential medium ( MEM ) containing 10% FCS . All media were supplemented with 2 mM L-glutamine , 100 UI/ml penicillin and 100 µg/ml streptomycin and were purchased from Invitrogen Life Technologies ( Eragny , France ) . Cell viability assays were performed using the EZ4U kit ( Biomedica ) according to the manufacturer's recommendations . This method is based on the capacity of living cells to reduce tetrazolium salts into intensely colored formazan derivatives . Briefly , cells were incubated at 37°C with a solution of tetrazolium salts provided by the manufacturer . After incubation , the conversion tetrazolium salts in red formazan derivatives was measured using a microplate-reader set at 450 nm and 620 nm , as a reference . The cell viability was expressed as percentage compared to control . The GFP-EΔ95/295 plasmid was kindly provided by A . Benmerah ( Institute Cochin , Paris , France ) . Dynamin-II ( dyn-wt ) and dynamin-II K44A ( dyn-K44A ) plasmid constructs , both GFP-tagged , were kindly provided by M . Mc Niven ( Mayo Clinic and Foundation , Rochester , USA ) . The GFP empty vectors and GFP-caveolin-1 ( GFP-Cav1 ) constructs were prepared as previously described [35] . RAW 264 . 7 macrophages were transfected with dyn-wt , dyn-K44A , GFP-EΔ95/295 , GFP-Cav1 and GFP plasmid constructs using Nucleofactor ( Amaxa Biosystems ) , according to the manufacturer's recommendations . RAW 264 . 7 macrophages were seeded in 24-well plates ( 5×104 cells per well ) for 16 hours and then infected with APMV ( 10 to 200 PFU/macrophage ) for different periods . After extensive washing of cell preparations to discard unbound viruses , viral uptake was determined by qPCR . In some experiments , macrophages were incubated for an additional period of 30 hours to determine APMV replication cycle . In brief , the macrophage culture supernatant was recovered and pooled with macrophage lysate , and DNA was extracted using the QIAamp DNA MiniKit ( Qiagen ) according to the manufacturer's instructions . The number of viral DNA copies was calculated using the LightCycler-FastStart DNA Master SYBR Green system ( Roche ) . The selected primers specific for APMV were BCFE ( 5′-TTATTGGTCCCAATGCTACTC-3′ ) and BCRE ( 5′-TAATTACCATACGCAATTCCTG-3′ ) [1] . In each PCR run , a standard curve was generated using serial dilutions ranging from 10 to 108 viral DNA copies using the LightCycler software ( LC-Run version 5 . 32 ) . Results are expressed as the number of viral DNA copies . RAW 264 . 7 macrophages were seeded on 12-mm diameter glass coverslips in 24-well plates ( 5×104 cells per well ) for 16 hours . To determine the role of clathrin , cytoskeleton reorganization , PI3K activation and macropinocytosis in APMV uptake , macrophages were pretreated for 30 min with chlorpromazine , cytochalasin D , LY294002 and EIPA , respectively . All inhibitors were purchased from Sigma-Aldrich ( St . Louis , MO ) . Macrophages were then infected with APMV ( 50 PFU/cell ) for 6 hours , extensively washed to discard unbound viruses and fixed in 3% paraformaldehyde or methanol . Fixed macrophages were permeabilized with 0 . 1% Triton X-100 and immunofluorescence labelling was performed according to standard procedures [36] . Rabbit and mouse polyclonal antibodies directed against APMV were generated in our laboratory [3] . After fluorescent labelling , macrophages were mounted with Mowiol and examined in fluorescence and differential interference contrast ( DIC ) modes with an Olympus microscope ( Bx511 ) equipped with a Nikon digital camera ( Sight DS5M ) using a 60× objective lens . Pictures were processed with Adobe Photoshop V5 . 5 software . Macrophage infection was scored by examining at least 200 macrophages per experimental condition: 50 microscope fields , with at least 3 macrophages per field containing at least 3 virus , were randomly selected . The percentage of uptake was calculated as the product of the mean number of viral particles per infected macrophage and the percentage of infected macrophages ×100 . When chlorpromazine and EIPA were used , the quantification of grey values of thresholded , fluorescent images of at least 20 cells was performed using ImageJ software ( NIH , http://rsb . info . nih . gov/ij ) [16] . The results are expressed as the percentage of uptake relative to the control . RAW 264 . 7 macrophages were serum starved , pretreated with chlorpromazine for 30 min , and incubated with 50 µg/ml Alexa 488- or Alexa 555-conjugated transferrin ( Molecular Probes ) for 15 min at 37°C . To remove external transferrin , macrophages were acid washed ( 0 . 1 M glycine , 0 . 1 M NaCl , pH 3 . 0 ) , then fixed with 3% paraformaldehyde and mounted with Mowiol . Control cells were processed as described without chlorpromazine pretreatment . Dextran uptake by macrophages was assayed as follows . Macrophages were pretreated with EIPA for 30 min and then incubated with 3 mg/ml FITC- or Alexa 555- conjugated dextran ( lysine fixable; MW , 50 kDa , Molecular Probes ) for 30 min . After washing , macrophages were fixed with 3% paraformaldehyde and mounted with Mowiol . Control cells were processed as described without EIPA pretreatment . Imaging , and transferrin and dextran uptake were determined as described above . RAW 264 . 7 macrophages were seeded on 12-mm diameter glass coverslips in 24-well plates ( 5×104 cells per well ) for 16 hours and then infected with APMV ( 50 PFU/cell ) . Rat antibodies specific for Lamp-1 ( clone 1D4B ) were purchased from DSHB ( Iowa , USA ) , and rabbit antibodies specific for rabankyrin-5 and clathrin were kindly provided by M . Zerial ( MPI-CBG , Dresden , Germany ) and S . Meresse ( CIML , Marseille , France ) , respectively . Secondary Alexa antibodies were a generous gift from M . Zerial . The distribution of F-actin was studied using a specific probe , Alexa Fluor 488-phalloidin ( Molecular Probes ) , and standard procedures . RAW 264 . 7 macrophages were incubated with Lysotracker red DND99 ( Molecular Probes ) at 100 nM for 2 hours and , after washing , they were infected with APMV . After fluorescent labelling , macrophages were mounted with Mowiol and examined as described above . At least 60 macrophages were examined for each experimental condition and results are expressed as the percentage of APMV particles that colocalized with fluorescent markers . Macrophages were selected as following: 50 microscope fields , with at least 3 macrophages per field containing at least 3 viruses , were randomly selected . In some experiments , macrophages were examined by laser scanning microscopy using a confocal microscope ( Leica TCS SP2 ) with a 63X/1 . 32-0 . 6 oil CS lens and an electronic Zoom 4X . Optical sections of fluorescent images were collected at 0 . 25-µm intervals using Leica Confocal Software and processed using Adobe Photoshop V5 . 5 software or Imaris software for the 3D reconstruction . RAW 264 . 7 macrophages ( 5×104 cells per assay ) seeded on glass coverslips were infected with APMV ( 500 PFU/macrophage ) . After different periods , they were fixed with 2 . 5% glutaraldehyde buffered with 50 mM sodium cacodylate , 50 mM KCl and 2 . 5 mM MgCl2 ( pH 7 . 2 ) for 30 min at room temperature . Samples were rinsed several times with cacodylate buffer and post-fixed with ice-cold 2% osmium tetroxide for 1 hour . After washing in distilled water , the samples were stained with 0 . 5% uranyl acetate for 18 hours . Dehydration was performed using graded concentrations of ethanol ( 50–100% ) and propylenoxide ( 100% ) . Macrophages were embedded in Epon , sectioned and stained with uranyl acetate ( 2% in methanol ) and lead citrate . Samples were examined with a FEI Morgagni 268 ( D ) transmission electron microscope [37] , [38] . RAW 264 . 7 macrophages ( 5×106 cells per assay ) were incubated with APMV ( 50 PFU/macrophage ) for different periods at 37°C . Macrophages were washed with cold PBS , scraped and pelleted at 500× g for 10 min at 4°C . Western blotting was performed as described elsewhere [39] . Briefly , macrophages were incubated in ice-cold lysis buffer containing 1% Triton X-100 , sodium orthovanadate and protease inhibitors ( Complete , Roche Diagnostics ) . Samples were pelleted at 4 , 000× g for 20 min at 4°C . Protein content of supernatants was adjusted and 50 µg proteins were loaded on sodium dodecyl sulfate-12% polyacrylamide gels ( SDS-PAGE ) under reducing conditions . After electrophoresis , proteins were transferred to nitrocellulose membranes . Unreacted sites were blocked in a solution containing 0 . 05% Tween 20 and 5% milk for 2 hours . After washing , blots were washed and incubated with a 1∶1 , 000 dilution of polyclonal antibodies directed against phosphorylated Akt ( Ser473 ) or monoclonal antibodies specific for phosphorylated ERK-1/ERK2 ( Thr202/Tyr204 ) for 60 min . Antibodies were purchased from Cell Signaling . After washing , nitrocellulose blots were incubated with a 1∶1 , 000 dilution of peroxidase-conjugated F ( ab′ ) 2 anti-mouse immunoglobulin G ( Amersham ) for 60 min and visualized using an enhanced chemiluminescence detection kit ( ECL , Amersham ) . Membranes were stripped , reprobed with polyclonal anti-Akt antibodies and anti-ERK antibodies ( Cell Signaling ) , respectively , and incubated with secondary antibodies before ECL visualization . M . avium spp avium ( ATCC N° 25291 ) was cultured in Middlebrook 7H9 broth ( Life Technologies ) supplemented with 10% Middlebrook oleic acid , albumin , dextrose , and catalase enrichment ( OADC ) and 0 . 2% Tween 80 , as previously described [40] . Bacteria were frozen at −80°C in 7H9 medium with 10% OADC . Organisms were labelled with 1 µg/ml Texas red succinimidyl ester ( Molecular Probes ) according to the manufacturer's protocol [41] , and bacterial viability was checked . RAW 264 . 7 macrophages were seeded on 12-mm diameter glass coverslips in 24-well plates ( 5×104 cells per well ) for 16 hours , then infected with fluorescent organisms ( bacterium-to-cell ratio of 10∶1 ) . After 6 hours , cells were extensively washed to discard unbound bacteria and fixed in 3% paraformaldehyde . The results are given as mean±SD . Student's t test analysis was performed using the GraphPad 4 Prism software . Differences were considered significant when p<0 . 05 .
|
The giant ( 750 nm ) double-stranded DNA virus Acanthamoeba polyphaga mimivirus ( APMV ) is likely responsible for pneumonia . We demonstrate here that APMV was internalized by macrophages but not by non-phagocytic cells , leading to productive replication . We also show that APMV invaded macrophages through phagocytosis . This is the first evidence that a virus is internalized by macrophages via a mechanism normally used by bacteria and parasites . This finding adds a supplementary pathway to already known strategies used by viruses to enter cells . This underlines that intra-amoebal pathogens also infect macrophages . Finally , we can hypothesize that APMV replicates within alveolar macrophages , leading to human and murine pneumonia .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"microbiology/microbial",
"evolution",
"and",
"genomics",
"microbiology"
] |
2008
|
Ameobal Pathogen Mimivirus Infects Macrophages through Phagocytosis
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Dengue is endemic to the rural province of Kamphaeng Phet , Northern Thailand . A decade of prospective cohort studies has provided important insights into the dengue viruses and their generated disease . However , as elsewhere , spatial dynamics of the pathogen remain poorly understood . In particular , the spatial scale of transmission and the scale of clustering are poorly characterized . This information is critical for effective deployment of spatially targeted interventions and for understanding the mechanisms that drive the dispersal of the virus . We geocoded the home locations of 4 , 768 confirmed dengue cases admitted to the main hospital in Kamphaeng Phet province between 1994 and 2008 . We used the phi clustering statistic to characterize short-term spatial dependence between cases . Further , to see if clustering of cases led to similar temporal patterns of disease across villages , we calculated the correlation in the long-term epidemic curves between communities . We found that cases were 2 . 9 times ( 95% confidence interval 2 . 7–3 . 2 ) more likely to live in the same village and be infected within the same month than expected given the underlying spatial and temporal distribution of cases . This fell to 1 . 4 times ( 1 . 2–1 . 7 ) for individuals living in villages 1 km apart . Significant clustering was observed up to 5 km . We found a steadily decreasing trend in the correlation in epidemics curves by distance: communities separated by up to 5 km had a mean correlation of 0 . 28 falling to 0 . 16 for communities separated between 20 km and 25 km . A potential explanation for these patterns is a role for human movement in spreading the pathogen between communities . Gravity style models , which attempt to capture population movement , outperformed competing models in describing the observed correlations . There exists significant short-term clustering of cases within individual villages . Effective spatially and temporally targeted interventions deployed within villages may target ongoing transmission and reduce infection risk .
Dengue remains a major public health concern throughout global tropical and subtropical regions . An estimated 390 million people are infected by the mosquito-borne virus each year , of which 96 million develop symptomatic disease [1] . Thailand , like most countries in Southeast Asia , has experienced endemic dengue circulation of all four serotypes for decades [2] , [3] . An effective dengue vaccine remains elusive and intervention measures will continue to rely on mosquito control for the foreseeable future . These efforts include the detection and removal of potential oviposition sites , the spraying of insecticides , and potentially the future releases of Wolbachia-infected mosquitoes that have been shown to reduce the mosquitoes' ability to transmit dengue [4] . Effective use of these measures requires a good understanding of the spatial distribution of cases . Of particular use is an understanding of where other cases are likely to be found on detection of an index case . Characterizing the spatial dependence between dengue cases can also provide insight into potential mechanisms of disease spread . The home locations of individuals hospitalized with dengue in Bangkok have been shown to exhibit significant spatial dependence at distances of around a kilometer [5] . Such spatial structure suggests focal transmission events are driving viral dispersal in this large , super-urban population . The situation in rural areas , which make up the majority of the country , may be markedly different . Phylogenetic studies have shown widespread genetic and serotype diversity across the rural Thai province of Kamphaeng Phet with some clustering of lineages within villages [6] , [7] . In addition , cluster studies in the same region detected infected individuals within 15 days of an index case at distances of 100 m within villages [8] , [9] . However , the extent at which spatial dependence is observed in these areas is not known . Unlike continuously inhabited urban centers such as Bangkok , rural communities in Thailand tend to be separated by wide expanses of uninhabited farmland or forests . The distance between neighboring rural communities is typically far beyond the short flight range of the main dengue vector , Aedes aegypti [10] . For sustained transmission to occur between rural communities , movement of infected individuals is likely necessary . If human movement between neighboring communities were key to DENV dispersal in this region , we would expect short-term spatial dependence between cases occurring at between-community scales . Further , we would expect that patterns of population flows would correlate with the spatio-temporal location of infections . It has previously been shown that individuals tend to move to larger and closer communities [11]–[13] . Such population flows can be captured using gravity models that incorporate the size of populations and the distance between them . Similar approaches have previously been used in phylogenetic analyses to describe dengue viral flow in Vietnam [14]–[16] . Appropriate data necessary to describe the spatio-temporal patterns of dengue virus require , 1 ) a long time series , 2 ) availability of address data for patients , and proper diagnostics to confirm DENV infection . We used a unique dataset that meets all of these criteria: the geocoded home addresses of 4 , 768 individuals who were admitted to the provincial hospital in Kamphaeng Phet , Thailand over a fourteen-year period ( 1994–2008 ) . The objective of our study was to characterize the short-term spatial dependence between dengue cases , to quantify the correlation in the long-term epidemics experienced by different communities and to explore the ability of human movement models to describe the observed correlations .
Data were collected from existing records without personal data . The research components of this project received approval from the Ethical Research Committee of Faculty of Public Health , Mahidol University and U . S . Army Medical Research Materiel Command ( USAMC-AFRIMS Scientific Review Committee ) review and approval . Kamphaeng Phet is a largely rural province in northern Thailand with an area of 8 , 600 km2 ( Figure 1 ) [17] . It had a population of 797 , 000 people in the 2010 census , mainly residing in villages . The largest town in the province is the capital ( Mueang Kamphaeng Phet ) with 30 , 000 inhabitants . The landscape is dominated by rolling hills with large portions of the province covered by forests . Since 1994 , the Armed Forces Research Institute of Medical Sciences ( AFRIMS ) has conducted dengue surveillance at Kamphaeng Phet Provincial Hospital ( KPPPH ) . KPPH is the largest hospital in Kamphaeng Phet , located in the capital , and such receives referral cases as well as walk-in patients of all ages from throughout the province . For each suspected dengue case , DENV infection is confirmed using semi-nested RT-PCR and IgM/IgG ELISA . In addition , home address information is collected on each patient . We geocoded the home address down to the village level for each individual using detailed base maps of the region . Individuals from the same village were given the same coordinates ( Table 1 ) . To characterize the short-term spatial dependence between rural dengue cases , we used the statistic on all cases occurring outside the provincial capital [5] . This statistic estimates the probability of two cases occurring both within distances d1 and d2 and within a month of each other relative to the independent probabilities of observing two cases within d1 and d2 over the entire time series and of observing two cases within a month of each other over the whole study area . This approach therefore measures the interaction in time and space of cases and has previously been used to characterize the spatial dependence of dengue cases in Bangkok [5] . Where is the set of cases that occur both within a 30 day period and within d1 and d2 of case i; is the set of cases within d1 and d2 of case i over the entire time series and is the set of cases that occur within a 30 day period from case i over the study area . Importantly , as underlying spatial biases such as population density and hospital utilization rate differences impact both the numerator and the denominator in the same way , they do not bias our estimates of spatial dependence . We estimate as follows ( see [5] for details ) :We generated bootstrapped confidence intervals for by resampling the cases with replacement 500 times . Ninety-five percent confidence intervals were calculated from the 2 . 5% and 97 . 5% quantiles from the resulting distribution . Patterns of spatial dependence may have changed over the time period of the study . We therefore recalculated using cases from annual incremental five-year windows from between 1994 and 2008 . We explored whether any short-term spatial dependence between individual cases resulted in correlation in the epidemics experienced by different communities . In this analysis , to avoid excessively small numbers of cases per location over the entire time period , villages were grouped into clusters by placing a grid over the province . The distance between each grid point was 3 km and villages were assigned to the closest grid point . Only village clusters with at least 40 cases over the time series were used in the analysis . The population of each village cluster was extracted from LandScan data [18] . LandScan uses a combination of satellite imaging and census data to construct population estimates throughout the world . To make the epidemic curves between locations as comparable as possible , we down-sampled each epidemic curve ( to create “down-sampled curves” ) by randomly selecting 40 cases ( the minimum number of cases at within a village cluster ) with replacement from all the cases that occurred at that location . We calculated the Pearson correlation coefficient between all pairs of down-sampled curves . We calculated the loess curve of the relationship between the Euclidean distance and correlation between village cluster pairs . We repeated the down-sampling process 500 times and reported the mean of the resulting distribution . In addition , 95% confidence intervals for the loess curves were estimated from the 2 . 5% and 97 . 5% quantiles . We compared our estimate of the expected correlation by distance separating communities to a theoretical complete-synchrony scenario where there was no distance effect . The complete-synchrony distribution was generated by randomly reassigning the location of all cases , keeping the month in which they occurred fixed . The total number of cases within any location over the whole time series was unchanged . The resulting distribution is that expected under a scenario of complete synchrony of cases over the province . The mean and confidence intervals for the complete-synchrony distribution were calculated by repeating the process above in generating down-sampled curves , repeating each resampling event 500 times . There exist alternative measures of correlation . We explored the consistency of our findings to a different measure: the Spearman rank correlation coefficient . In this sensitivity analysis , we recalculated the correlation coefficients for both the observed data and the theoretical complete-synchrony scenario . Gravity models can be used to describe population flows [11]–[13] . Here we used them to explore their ability to explain the correlation in the epidemic curves between pairs of village clusters:where pop1 and pop2 are the populations of the two settlements and dist is the Euclidean distance between the two settlements . By log-transforming the equation , we can estimate the exponents α and β through linear regression:We used Akaike's Information Criterion ( AIC ) to compare the performance of the gravity model to an intercept only model and a univariate model incorporating Euclidean distance only ( Table 2 ) [19] . All of the models were performed using the correlation coefficients from each set of down-sampled curves ( 500 in all ) . We reported the mean coefficient across all sets of down-sampled curves for each model . In addition we calculated 95% confidence intervals using the 2 . 5% and 97 . 5% quantiles from the distribution of coefficient estimates . All analyses were conducted in R 2 . 15 . 2 [20] .
Between 1994 and 2008 , 4 , 768 dengue inpatients at KPPPH were successfully geocoded ( 93% of all cases ) ( Table 1 ) coming from 568 different villages ( Figure 1 ) . The provincial capital , where KPPPH was located , had 732 cases ( 15% of all cases ) . The mean age of cases was 11 . 0 years and 59% of cases suffered from the more severe hemorrhagic form of the disease ( Table 1 ) . On average , villages were separated by 1 . 4 km from their closest neighboring village . We characterized the short-term spatial dependence between the home locations of the cases presenting at KPPH using the φ ( d1 , d2 ) statistic . We found that cases were 2 . 9 times more likely ( 95% confidence interval of 2 . 7–3 . 2 ) to occur both within the same community and to be infected within the same month of each other than the independent probabilities of occurring within the same community over the entire study period and occurring within the same month across the entire province ( Figure 2 ) . This fell to 1 . 4 times ( 1 . 2–1 . 7 ) for communities separated by between 0 . 5 km and 1 . 5 km and to 1 . 2 times ( 1 . 1–1 . 3 ) for communities separated by 2 . 5 km −3 . 5 km . We observed significant spatial dependence , albeit at low levels , at distances up to 5 km . However , when we divided the entire time series into smaller subsets covering five year time periods only , there was a clear trend in the spatial extent of spatial dependence ( Figure S1 ) . Cases from the 1990s exhibit spatial dependence at larger distances than more recent cases . To explore whether short-term spatial dependence between individual cases resulted in similar patterns of disease observed between communities , we compared the correlation of the epidemic curves between communities by the distance separating them . We divided the villages into 24 village clusters with each village cluster having at least 40 cases over the 14 years . The locations of the village clusters are illustrated by the red dots in Figure 1 . The mean correlation in the monthly epidemic curves between all village cluster pairs was 0 . 19 , however , there existed substantial structure in the correlation: village clusters that were under 5 km apart had a mean correlation of 0 . 28 ( 95% confidence interval of 0 . 25–0 . 31 ) , whereas village clusters separated by between 20 km and 25 km had a mean correlation of 0 . 16 ( 95% confidence interval: 0 . 14–0 . 17 ) ( Figure 3 ) . We estimated that a ( theoretical ) scenario of complete synchrony across the entire province would result in a mean correlation of 0 . 32 , irrespective of distance between village clusters ( Figure 3 ) . This correlation was much less than 1 . 0 as there are fewer cases than locations for many time points resulting in occasional small peaks in the epidemic curves that were not matched across all locations . The correlation under full synchrony and the observed correlations looked very similar when the alternative Spearman rank correlation coefficient was used instead ( Figure S2 ) . We explored whether different statistical models could explain the observed correlation between community-pairs ( Table 2 ) . We found that univariate model incorporating only the Euclidean distance separating communities explained only 7% of the variance in the correlations ( Table 3 ) . Incorporating population sizes ( model 3 ) substantially improved the fit of the model although the majority of the variance remained unexplained ( R2 of 0 . 13 ) . Model 3 was also strongly favored by AIC [21] .
We have used a large dataset from a long time series with geocoded addresses to explore the spatial patterns of dengue cases in a rural region with endemic circulation . We have shown substantial short-term clustering of dengue cases within communities , consistent with transmission chains circulating at small spatial scales . We observed a large drop in the clustering of cases from within-community to between community scales . Our findings suggest that upon discovering an infected individual , there is a significant risk that other individuals from his or her village will also be infected . The removal of mosquitoes in that community could potentially reduce the risk of onward transmission . While lower than within-community estimates , significant short-term spatial dependence was nevertheless observed at inter-settlement scales . This observation is consistent with viral movements between neighboring communities , distances greater than the flight range of the dengue vector [10] . These findings point to a potential role for human movement in driving the spread of the virus . This was further supported by a clear reduction in the correlation in the epidemic curves between communities with increasing spatial separation between them . Gravity models are regularly used to describe human population flows [11]–[13] . Here a related formulation of gravity models that describes the correlation in the epidemic curves between communities was found to outperform competing models . This finding supports previous findings from gravity models fit to phylogeographic data from southern Vietnam [15] . Human movement has also been suggested to play a major role in the dengue epidemic in Iquitos , Peru [22] . Spatial correlation in ecological conditions ( e . g . , vector density ) or in behavioral factors ( e . g . the use of screens on windows ) between communities may also explain these observations . We cannot definitively differentiate between these potential explanations here . Further research using information on the infecting pathogen , such as serotype or genetic information could help disentangle these competing hypotheses . Our findings of focal patterns of disease support the results of previous cluster studies in the region [8] , [9] . In addition , a previous study in Bangkok observed short-term spatial dependence in the homes of hospitalized cases between 1995 and 1999 at distances up to around 1 km [5] . Overall , we observed spatial dependence at larger distances than in the Bangkok study although when we looked at 5-year subsets of the data , the spatial extent of clustering was shorter among more recent cases . Higher levels of movement across the province as a whole suppresses spatial dependence by promoting the global mixing of the population . Our observations are therefore consistent with increased movement across the province in more recent years . Mosquito control efforts are widely used throughout Southeast Asia and center on the use of insecticides . Insecticide fogging has been shown to temporarily reduce the number of mosquitoes in any location [23] . However , the ability of insecticides to reduce the risk of dengue infection remains unclear . Insecticide effectiveness may be limited by an inability to reduce mosquito density sufficiently or for a long enough period to prevent transmissions from viremic individuals . This is supported by a lack of a clear relationship between vector density and dengue transmission risk [24] . In addition , spraying may be too spatially restricted , allowing mosquitoes outside spray zones to rapidly repopulate fogged spaces . Finally spraying is sometimes only deployed in outdoor areas whereas Aedes aegypti mosquitoes tend to be found inside households . Estimating the impact of insecticides on dengue infection is difficult . The majority of dengue infections are not detected and the appropriate characteristics of control populations for any study are unclear . Nevertheless , further studies are needed to provide a sound evidence base for the widespread use of these measures . The study has some limitations . The mean correlation between the epidemics experienced by pairs of communities appeared low ( mean of 0 . 19 ) . However , this was only slightly less than expected if all cases at any time point were randomly distributed throughout the communities ( mean of 0 . 32 ) , resulting in synchronous epidemics . This low level of correlation occurs because of the small numbers of cases ( all the epidemic curves were down-sampled to only 40 cases ) . Even in the scenario of complete synchrony , tiny fluctuations were regularly present in the epidemic curve in one location and not in the curves of others , deflating correlation . These observations illustrate the problems in using the absolute correlation as a marker of similarity when many time points have no cases . Nevertheless , trends in correlation over distance and comparisons to a distribution expected under complete synchrony remain useful . Our data consists of cases that presented at hospital only . The majority of infections , however , result in asymptomatic or only mildly symptomatic . The spatial dependence between these infections may be different . We could only geocode individuals to the village level . We could not therefore explore spatial differences within any village . Future work using exact home locations may allow elucidation of finer scale spatial dependence between case homes . Finally , the relationship between gravity models fit to population flows directly and those fit to the correlation in epidemic curves may be complex and setting specific . Further work using simulated data may help provide insight into their relationship . In conclusion , cases of dengue appear highly spatially correlated within villages in rural Thailand; however , neighboring communities nevertheless appear to observe correlated epidemics . Human movement patterns may be a key driver of dengue dispersal in this region . Future studies that incorporate movement diaries or GPS trackers would help describe population flows and allow the development of mechanistic models for the dispersal of dengue .
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Transmission of dengue virus has long been studied in Kamphaeng Phet , Northern Thailand , but how cases are related in time and space is still unclear , as is the role of human movement in generating these patterns . Because of these knowledge gaps , public health officials cannot make educated decisions on how to target vector control interventions and mechanisms of virus dispersal are not known . We mapped the homes of dengue cases admitted to the main hospital in the province capital from 1994–2008 and quantified the spatial correlation between them . We found an almost three times greater chance that cases from the same month came from the same village than expected , given the overall distribution of cases . Some clustering was also observed between cases in neighboring villages with the overall epidemics experienced by neighboring communities also more correlated than epidemics in villages farther apart . The short-term clustering observed within individual villages implies that effective spatially targeted interventions deployed within villages may reduce infection risk . As the distance between neighboring communities exceeds the typical flight range of the dengue vector , these findings also suggest a potential role for human movement in driving the wider spread of the virus .
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2014
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The Spatial Dynamics of Dengue Virus in Kamphaeng Phet, Thailand
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Inflammation has long been implicated as a contributor to pathogenesis in many CNS illnesses , including Lyme neuroborreliosis . Borrelia burgdorferi is the spirochete that causes Lyme disease and it is known to potently induce the production of inflammatory mediators in a variety of cells . In experiments where B . burgdorferi was co-cultured in vitro with primary microglia , we observed robust expression and release of IL-6 and IL-8 , CCL2 ( MCP-1 ) , CCL3 ( MIP-1α ) , CCL4 ( MIP-1β ) and CCL5 ( RANTES ) , but we detected no induction of microglial apoptosis . In contrast , SH-SY5Y ( SY ) neuroblastoma cells co-cultured with B . burgdorferi expressed negligible amounts of inflammatory mediators and also remained resistant to apoptosis . When SY cells were co-cultured with microglia and B . burgdorferi , significant neuronal apoptosis consistently occurred . Confocal microscopy imaging of these cell cultures stained for apoptosis and with cell type-specific markers confirmed that it was predominantly the SY cells that were dying . Microarray analysis demonstrated an intense microglia-mediated inflammatory response to B . burgdorferi including up-regulation in gene transcripts for TLR-2 and NFκβ . Surprisingly , a pathway that exhibited profound changes in regard to inflammatory signaling was triggering receptor expressed on myeloid cells-1 ( TREM1 ) . Significant transcript alterations in essential p53 pathway genes also occurred in SY cells cultured in the presence of microglia and B . burgdorferi , which indicated a shift from cell survival to preparation for apoptosis when compared to SY cells cultured in the presence of B . burgdorferi alone . Taken together , these findings indicate that B . burgdorferi is not directly toxic to SY cells; rather , these cells become distressed and die in the inflammatory surroundings generated by microglia through a bystander effect . If , as we hypothesized , neuronal apoptosis is the key pathogenic event in Lyme neuroborreliosis , then targeting microglial responses may be a significant therapeutic approach for the treatment of this form of Lyme disease .
Lyme borreliosis is the most prevalent vector-borne illness in the northern hemisphere [1] , [2] . Transmission from the animal reservoir to the human host occurs via an Ixodes tick bite into the skin , where B . burgdorferi , the spirochete that causes Lyme disease , can then disseminate hematogenously to various organs , including the heart , joints and both the peripheral and central nervous systems [2]–[4] . A prominent , recurring and yet only partially understood feature of Lyme disease is the presence of inflammatory infiltrates within infected tissues [1]–[5] . Evidence of neurological involvement occurs to varying degrees in both the central and peripheral nervous systems of Lyme disease patients , and inflammation is often associated with the neurological manifestations that define neuroborreliosis [2] , [5]–[8] , [9] , [10] . Upon gaining access to the central nervous system ( CNS ) , B . burgdorferi may induce cerebrospinal fluid pleocytosis , meningoradiculitis and cranial neuritis as well as encephalopathies with neurocognitive abnormalities [3] , [5] , [6] , [11] . This complex process of B . burgdorferi-induced pathology in the CNS has many aspects as yet to be clarified , with reactive inflammation potentially being one of the principal contributors to neuronal dysfunction . Although B . burgdorferi lacks lipopolysaccharide ( LPS ) , the organism does produce spirochetal lipoproteins that can induce inflammation [11]–[17] . By signaling through CD14 , binding Toll-like receptors ( TLR ) 2 and 1 and subsequent activation of NFκβ , lipoproteins have been shown to generate inflammatory mediators in a variety of cell types [14] , [18]–[21] . Recent studies with TLR2 and/or MyD88-deficient mice in which B . burgdorferi-induced inflammatory infiltrates were greater than that of wild type controls , indicated however , that there are alternative pathways regulating the inflammatory response to infection with B . burgdorferi [11] , [22] , [23] . As neuronal dysfunction is further analyzed in the light of these findings , it becomes necessary to consider which cells of the CNS milieu can function most aggressively/efficiently in creating an inflammatory environment that will contribute to clearance of the pathogen , but may in the process , harm nearby neurons . Using in vitro and ex vivo experiments , investigators have shown that B . burgdorferi can induce potent production of inflammatory cytokines and chemokines in microglia , the resident macrophage cells of the CNS [2] , [6] , [12] , [24] . The secretion of these mediators is likely a vital step in the development of inflammatory reactions , as chemokines , depending on their CC/CXC family sequence , may recruit distinct immune-effector cells , including monocytes , lymphocytes or neutrophils to sites of inflammation . Additionally , many cytokines and chemokines become involved in apoptosis , cell cycle regulation and angiogenesis [3] , [6] , [23] , [25]–[28] . While a similar response is observed in astrocytes , the repertoire range and scale of concentration is typically much lower than that of activated microglia [2] , [12] , [29] . Importantly , although astrocytes are considered to be the primary CNS support cells , activated microglia can also secrete a host of soluble agents , such as glia-derived neurotrophic factor , that are potentially neuroprotective [30]–[32] . As the majority of molecules produced by activated microglia are however considered to be pro-inflammatory and neurotoxic [2] , [30] , [31] , [33] , the microglial response to CNS infection with B . burgdorferi could tip the scales toward neuronal damage and death rather than survival in Lyme neuroborreliosis . We have argued that the associated neurocognitive symptoms of Lyme neuroborreliosis may be the result of neuronal dysfunction resulting from inflammatory mediators released in response to infection with B . burgdorferi . Serving as an in vivo proof of concept to this hypothesis , our laboratory has recently shown the production of IL-6 by astrocytes , as well as the induction of oligodendrocyte and neuronal apoptosis in brain tissues taken from rhesus macaques that received intraparenchymal stereotaxic inoculations of live B . burgdorferi [6] . With the goal of beginning to establish cause and effect relationships between glial cell responses to B . burgdorferi and neuronal apoptosis , we quantified the production/secretion of inflammatory cytokines and chemokines in purified rhesus brain cortex microglia and astrocytes , in human neuronal cells from the SH-SY5Y ( SY ) neuroblastoma cell line , and in combinations of the above cells co-cultured with live B . burgdorferi or recombinant purified lipidated outer-surface protein A ( L-OspA ) . SY cells were cultured in three dimensions as opposed to monolayer culture , as we had shown that that mode of cultivation significantly narrows the phenotypic gap between neuronal cell lines and primary neurons [34] . We also determined the extent of B . burgdorferi-induced apoptosis in each of the above cellular combinations . Using microarray analysis , we further examined the principal inflammation and apoptosis pathways affected by B . burgdorferi in these cells . Our findings suggest a bystander effect in which the neurotoxic surroundings generated by microglia may contribute to neuronal cell damage .
Brain tissues used in this study were collected from rhesus macaques ( Macaca mulatta ) . These animals were not experimentally infected with B . burgdorferi and were culled from the breeding colony because of chronic diarrhea or injury . The procedure used for euthanasia was consistent with the recommendations of the American Veterinary Medical Association's Panel on Euthanasia and was approved by Tulane University's Institutional Animal Care and Use Committee . Dulbecco's modified Eagle's medium ( D-MEM ) -F-12 with L-glutamine and 15 mM HEPES buffer , D-MEM high glucose with L-glutamine , F-12 ( Ham ) with glutamax , penicillin ( 100 units/ml ) , streptomycin ( 100 units/ml ) , amphotericin B ( 0 . 25 µg/ml ) , non essential amino acids ( NEAA ) ( 100 units/ml ) , sodium pyruvate ( 100 units/ml ) , sodium bicarbonate ( 7 . 5% solution ) , trypsin ( 0 . 25% ) /EDTA ( 0 . 38 g/ml ) , trypan blue™ , normal goat serum ( NGS ) , Alexa-562 ( red ) -conjugated secondary antibody , and the ToPro ( blue ) nuclear stain were each from Invitrogen . Primocin was from Invivogen . Fetal bovine serum was from Hyclone/Thermo Scientific and Cytodex-3™ micro-carrier beads were from Amersham Biosciences . Granulocyte-macrophage colony-stimulating factor ( GM-CSF ) , L-leucine methyl ester ( LME ) , Barbour-Stoenner-Kelly-H medium with rabbit serum , rifampicin , amphotericin , fish skin gelatin ( FSG ) , propidium iodide ( PI ) ( red ) and anti-GFAP antibody were from Sigma-Aldrich . Lipidated ( L-OspA ) and unlipidated ( U-OspA ) outer surface protein A were a kind gift from GlaxoSmithKline . Paraformaldehyde ( PFA , 2% ) was from USB Corporation , and Triton X-100 from ICN Biochemicals . HuD primary antibody was from Santa Cruz Biotechnology . IBA-1 antibody was from WAKO . Three-micrometer pore diameter polyester transwell culture inserts ( Becton Dickinson , Falcon ) were incorporated into our co-culture models to physically separate SY cells from microglia . The SY cells were seeded directly into 24 well dishes ( Costar ) with the microglia then seeded onto culture inserts that had been placed into the same wells as the SY cells . The cell density in these experiments was 1×105 cells per ml of culture medium with an initial microglia to neuronal cell ratio of 4∶1 . Live B . burgdorferi was added to the culture medium in both chambers for a 5-day stimulation , at an MOI of 10∶1 , in relation to the entire cell population . In this way , it was possible to maintain a generally homogeneous exposure of each cell type to the culture medium while still allowing for bidirectional transfer of secreted molecules between the cell types . Supernatants collected from primary glia and/or SY cells co-cultured with B . burgdorferi ( MOI 10∶1 ) or in medium alone for either 24 hours or 5 days were used for quantification of secreted cytokines and chemokines . All of the mammalian cells were seeded at a density of 5×104 cells per 500 µl of culture medium with a glial cell to SY cell ratio of 4∶1 . The 27-cytokine bioplex assays ( Bio-Rad ) were performed according to manufacturer's directions as were the individual antigen-capture ELISAs for IL-6 , IL-8 , TNF , CCL3 , CCL4 , and MCP-1 ( CCL2 ) . Sandwich ELISA capture and detection antibody pairs for human IL-6 , IL-8 and TNF , along with recombinant human IL-6 , IL-8 , TNF and horseradish peroxidase ( HRP ) were from BD Biosciences . We obtained CCL3 and CCL4 ELISA DuoSet kits and the MCP-1 ( CCL2 ) kit from R&D Systems . Supernatants collected from primary glia and/or SY cells co-cultured with B . burgdorferi ( MOI 10∶1 ) or in medium alone for either 24 hours or 5 days were used for quantitative determination of total secreted nitric oxide . The mammalian cells were seeded at a density of 5×104 cells per 500 µl of culture medium with an initial glial cell to SY cell ratio of 4∶1 . The Total Nitric Oxide Assay Kit ( catalogue #917–020 ) from Assay Designs was used , and all analyses were performed according to directions from the manufacturer . Microglia , astrocytes , SY cells or combinations thereof were cultured in the presence of B . burgdorferi ( MOI 10∶1 ) or medium alone for either 24 hours or 5 days . After removal of the supernatant , the cells were harvested using trypsin , washed in phosphate buffered saline ( PBS ) , and fixed for 5–10 minutes in 2% PFA . The fixed cells were permeabilized in PBS/FSG/Triton and blocked with 10% NGS . Apoptosis was evaluated using the Apoptag TUNEL assay kit ( Chemicon/Millipore ) as per manufacturer's instructions and the results were analyzed using a Leica TCS SP2 confocal microscope equipped with 3 lasers . Briefly , 6–18 0 . 2-µm optical slices per image were collected at 512×512 pixel resolution . In order to distinguish SY cells in the co-cultures containing glia , the cells were additionally stained with primary anti-HuD antibody for 1 hour , washed 3 times in PBS and then stained with Alexa 562-labeled secondary antibody for 45 minutes . The To-Pro nuclear stain was combined with the secondary antibody at a concentration equal to 0 . 05 µg/ml . The identities of microglia and astrocytes were additionally confirmed using the above protocol , substituting anti-IBA-1 and anti-GFAP respectively , for the anti-HuD . Cell morphology consistent with apoptosis including cell shrinkage , nuclear condensation and membrane blebbing was assessed along with the fluorescein staining for TUNEL . The number of apoptotic cells counted was divided by the total ( 500 minimum ) number of cells counted . When the assays included co-culture of SY cells with glia , the number of cells that were double- stained for apoptosis and neuron specificity were divided by the total number of cells displaying the neuronal marker stain . Statistical significance was evaluated by One Way analysis of variance ( ANOVA ) followed by Bonferroni , Tukey and Levine's tests . RNA was isolated from approximately 5×106 isolated SY cells or microglia using the RNeasy kit ( Qiagen ) plus DNA-free ( Ambion ) to eliminate DNA contamination . Five hundred nanogram of total RNA was amplified and used to synthesize Cy-labeled cDNA with the Low RNA Input Linear Amplification Kit ( LRILAK , Agilent Technologies ) . Cy3 ( control ) and Cy5 ( experimental ) labeled cDNA were mixed in equimolar quantities and hybridized overnight at 55°C , to Agilent whole-genome microarrays ( 4×44 k format ) . While the samples derived from macaque microglia were hybridized to rhesus macaque arrays ( Agilent # G2519F ) with over 44 , 000 rhesus macaque probes , representing approximately 18 , 000 individual annotated genes , the samples derived from the neuronal cells were hybridized to human genome arrays ( Agilent # G4112F ) with over 41 , 000 oligonucleotides , representing approximately 22 , 000 unique human genes . The slides were scanned on a GenePix 4000B scanner , and data were extracted from the resulting 16-bit TIFF images using GenePix Pro 6 . 1 software . Data were analyzed using Spotfire DecisionSite for Microarray Analysis . Values were log2 transformed and normalized using a Locally Weighted ScatterPlot Smoothing ( LOWESS ) script within S+ ArrayAnalyzer . RNA was isolated from approximately 5×106 isolated microglia or neuronal cells using the RNeasy kit ( Qiagen ) plus DNA-free ( Ambion ) to eliminate DNA contamination . The RNA was reverse-transcribed into DNA using a OneStep RT-PCR kit ( Qiagen ) and the QuantiFast™ SYBR® Green PCR kit ( Qiagen ) was then used for the quantitative real-time ( QRT ) -PCR . All assays were performed according to directions from the manufacturer and using Qiagen Quantitect® primer pairs in a 96-well block Applied Biosystems 7900 HT fast RT PCR System . PCR efficiencies , average fold change and statistical significance were evaluated using REST© software .
In experiments where B . burgdorferi or recombinant L-OspA was co-cultured with isolated rhesus cortex glia , we observed robust expression and release of IL-6 and IL-8 ( Figure 1A ) . We also observed the production of TNF , although at a much lower level , and only by microglial cells and cells in the aggregate cultures , which themselves contained microglia ( Figure 1A insert ) . SY cell production of IL-6 and IL-8 in response to B . burgdorferi and L-OspA were below the limit of detection for the assay used . Cytokine/chemokine expression levels in response to the same stimuli often varied significantly in glial cells obtained from different animals . The patterns of expression , however , were reproducible in experiments where the glial cells were isolated from tissue originating from a single individual animal . As such , the highest response was always that of microglia , followed by that of aggregate cultures and then astrocytes , regardless of the animal from whom the cells had been obtained . With the exception of TNF , whose expression peaked at 24 hours and then declined , levels of cytokine/chemokine expression also increased with the time of stimulation ( Figure 1B ) . As the inherent difficulties in culturing primary neurons [35] , [36] often render their use in experiments impractical , the neuroblastoma cell line SH-SY5Y was employed to assess neuronal responses . We used a three-dimensional ( 3D ) rather than traditional monolayer ( 2D ) culture for the SY cells , as this culture method has been shown to promote a more normal , untransformed phenotype as compared to that of transformed cells grown in 2D [34] , [35] , [37]–[45] . In order to assess endpoint damage to the glia and SY cells responding to co-culture with B . burgdorferi or L-OspA , we employed the terminal deoxynuclease dUTP nick end labeled ( TUNEL ) assay as a tool for visualization of apoptosis . When microglia isolated either alone or in aggregate with astrocyte cells , were co-cultured with both B . burgdorferi and SY for at least 5 days , increases in cellular apoptosis consistently occurred . Apoptosis in glial cells was minimal as compared to the un-stimulated controls and there were no remarkable changes in apoptotic levels with regard to individual animals . SY cells cultured for 5 days with B . burgdorferi alone , or in combination with astrocytes and B . burgdorferi , showed only baseline levels of apoptosis ( Table 1 ) . No significant increase in astrocyte apoptosis was observed whether these cells were incubated with other cell types , L-OspA or B . burgdorferi ( Table 1 ) . Confocal microscopy images of mixed cultures stained for TUNEL and with cell-specific markers indicated that the majority of cells dying in response to B . burgdorferi were SY cells ( Figure 2A ) . In consideration of these findings , we focused our next experiments on SY cells cultured in the presence of microglial cells and B . burgdorferi for 5 days . Significant increases in SY cell apoptosis occurred consistently in cell cultures from each of the 4 animals sampled when both microglia and B . burgdorferi were included in the culture conditions ( Figure 2B ) . In order to determine whether stimulation of the culture medium with IL-6 , IL-8 , TNF or combinations thereof would be sufficient to induce apoptosis in the SY cells , we conducted experiments in which concentrations of each cytokine ( recombinant-human ) were added to the neuronal medium that were comparable to the highest expression values obtained during our in vitro assays with microglia and SY cells . After a 5 day stimulation , we did not find increases in neuronal apoptosis comparable to those observed in the co-culture of SY cells with microglia and B . burgdorferi , or significantly above the baseline levels of SY cells cultured in medium alone ( data not shown ) . In view of the evidence indicating that microglia were the more robust responders to the inflammatory stimuli of B . burgdorferi ( Figure 1 ) , and that molecules other than , or in addition to IL-6 , IL-8 and TNF were required to elicit SY cell apoptosis , we further explored the diversity of mediators produced by microglia in response to B . burgdorferi . As several investigators had reported that the activated glia seen in many CNS pathologies were able to kill neurons by the release of nitric oxide ( NO ) into their surrounding environments [46]–[48] , and knowing that B . burgdorferi could elicit the release of NO from exposed macrophages [49] , we explored this possibility in our models . Using an adaptation of the Greiss reaction , we were not able to detect any significant release of NO into the supernatant of microglia , astrocytes , SY cells or any combinations of the three cell types co-cultured with B . burgdorferi for either 24 hours or for 5 days ( data not shown ) . Using a 27 cytokine Bio-Plex assay to expand on our previous data , we found along with IL-6 and IL-8 , significant B . burgdorferi-induced upregulation in expression of the pro-inflammatory chemokines CCL2 , CCL3 , CCL4 and CCL5 by microglial cells ( Table 2 ) . L-OspA ( 0 . 25 µg/ml ) was observed to elicit a comparable upregulation of cytokines in both microglia and astrocytes , indicating that B . burgdorferi lipoproteins may elicit the lion's share of the spirochetal stimulus . However , when microglia were present in the cultures , alone or with astrocytes ( aggregate cultures ) , it appeared that spirochetes provided , overall , the stronger stimulus . SY cell responses were absent or minimal in comparison to that of the microglia , except for the production of vascular endothelial growth factor ( VEGF ) ( Table 2 ) , which has been increasingly implicated as a contributing factor in neuronal protection and survival [50]–[53] . B . burgdorferi induced significant cytokine/chemokine expression in astrocytes , but expression levels were generally orders of magnitude lower than those of microglia that had been derived from the same tissue ( Table 2 ) . These results , combined with our previous cytokine and apoptosis assays , prompted us to focus on interactions of microglia with SY cells in the presence of B . burgdorferi , and to broaden our exploration of pathways involved in microglial activation and neuronal apoptosis . We used microarray analysis to further investigate how exposure to B . burgdorferi might affect global gene expression in microglial cells . As changes in the expression and activity of multiple genes often work in concert to affect responses to many cellular pathogens , including B . burgdorferi [10] , [54]–[56] , we used Ingenuity Pathways Analysis software to compare transcript levels in 18 , 000 annotated rhesus genes . When microglia were cultured in the presence of B . burgdorferi , as compared to medium alone , five of the ten most altered canonical pathways , as well as the chemokine signaling pathway ( number 19 on the list of 232 affected pathways ) , showed significant upregulation in inflammatory signaling ( Figure 3A ) . Many of the transcript changes that occurred within the triggering receptor expressed on myeloid cells-1 ( TREM1 ) , pattern recognition receptors ( PRR ) , IL-10 , IL-6 and chemokine signaling ( Chem . Sig . ) pathways were proinflammatory and they were repeated in several pathways that included both innate and adaptive immune responses ( Table 3 ) . Interestingly , the pathway that exhibited the most profound changes in regard to inflammatory signaling was TREM1 ( Figure 3B , Table 4 ) . TREM1 cell surface receptors associate with the adaptor molecule DAP12 for signaling and function and have been classified as immune/inflammatory response amplifiers . Although the receptor has thus far not been found to be expressed on microglial cells [55] , [57]–[61] , our array data indicate that it is expressed in rhesus microglia , or a subpopulation thereof , and is also upregulated during infection with B . burgdorferi . A low level of TREM1 expression in rhesus microglia was confirmed by RT-PCR ( data not shown ) . RT-PCR was further used to confirm significant transcript up-regulation in the following three selected molecules: CCL2 ( TREM1 and Chem . Sig . pathways ) , IL-6 ( TREM1 , PRR , IL-10 and IL-6 pathways ) and CCL5 ( PRR and Chem . Sig . pathways ) , ( Table 5 ) . Individual transcript changes for both animals sampled can be accessed online through the supplemental data portion of this report ( Tables S1 and S2 ) . In addition to affecting the environment surrounding neurons exposed to B . burgdorferi , activated microglia may also affect gene expression of neurons themselves . Microarray analysis for the p53 Signaling pathway revealed a microglia-induced shift in the gene expression of SY cells co-cultured with B . burgdorferi from one of cell survival and proliferation to one more in line with cell cycle arrest and apoptosis ( Figure 4A and 4B ) . In cultures containing only SY cells and B . burgdorferi , there were prominent increases in transcript for the cell survival and anti-apoptotic molecules protein kinase B ( AKT1 ) and BCL2-like ( Bcl-XL ) genes , combined with a decrease in transcript for pro-apoptotic damage-regulated autophagy modulator ( DRAM ) ( Table 6-left , Figure 4A ) . This scenario , however , changed dramatically in experiments when microglia were included in the co-culture of SY cells with B . burgdorferi ( Figure 4B , Table 6 , right ) . In order to study microglia-induced gene expression changes in the SY cells , transwell inserts were incorporated into the culture system providing physical separation between the SY cells and the microglial cells , yet allowing for bidirectional transfer of secreted molecules between the cell types . In this model , gene expression for phosphatase and tensin homolog ( PTEN ) , which functions to modulate cell survival and proliferation primarily through its downstream effects on AKT1 , was shown to be significantly increased ( Table 6 , right ) . SY cell anti-apoptotic Bcl-XL expression was observed to completely swing from up to down-regulation in the presence of microglia , while pro-apoptotic BCL2-associated X protein ( Bax ) and phorbol-12-myristate-13-acetate-induced protein 1 ( NOXA-1 ) transcripts were increased . Additionally , the transcript for cyclin-dependent kinase 2 ( CDK2 ) , which typically drives the cell cycle , was down-regulated , and the G1/S cell cycle checkpoint inhibitors , cyclin-dependent kinase inhibitor 1A ( p21 ) and glycogen synthase kinase 3 beta ( GSK3β ) , were both increased ( Figure 4 , Table 6 right ) . The observed changes for individual gene transcript levels may be accessed online in the supplemental data portion of this report ( Tables S3 and S4 ) . RT-PCR was used to confirm transcript changes in selected molecules from co-cultures of the SY neurons with B . burgdorferi both in the presence and absence of microglia ( Table 7 ) . Several investigators have shown that when in the presence of certain stimuli , not only can macrophages/microglia induce apoptosis in neighboring cells through a bystander effect , but that there might be an additional requirement for cell-cell contact between the macrophages/microglia and the affected nearby cells [62]–[64] . To address the question of whether the microglia-secreted mediators in our system were enough to stimulate SY cell apoptosis on their own , we again incorporated transwell inserts into our co-culture models to physically separate the microglia from the SY cells while at the same time , maintaining a generally homogeneous exposure of each cell type to the culture medium . Parallel assays for B . burgdorferi-induced apoptosis were set up where one model followed the same experimental design that was described in Figure 2 , and the second model included the addition of transwell inserts into the co-culture system for the duration of the experiment . In each case , primary microglia or aggregate cell isolates from three separate non-human primates were combined in co-culture for 5 days with SY neurons and B . burgdorferi . Using the original co-culture format , we found an average 7 . 7-fold increase in neuronal apoptosis when SY cells were co-cultured with microglia and B . burgdorferi as compared to the SY cells cultured in medium alone . In contrast , no significant changes in apoptosis levels were observed when the SY cells were co-cultured with B . burgdorferi , with microglia , or with microglia and B . burgdorferi using the transwell inserts ( Figure 5 ) .
Neuroinflammation is thought to be a contributing factor in a number of neurodegenerative disorders including Alzheimer's disease , Parkinson's disease and multiple sclerosis , as well as in Lyme neuroborreliosis [6] , [7] , [31] , [65] . Inflammatory mediator levels are often elevated in these disorders suggesting that they are actively involved in the disease process [2] , [6] , [31] , [66] . Because neurological symptoms do occur in many patients with Lyme disease , and cognitive impairment is often a part of this scenario , it was important to discover which mediators likely caused the effects of B . burgdorferi-induced damage in neurons . We hypothesized that the inflammatory environment generated during a possible in vivo exposure of glial cells to B . burgdorferi could harm neurons through a bystander effect . To address this hypothesis , we used in vitro experiments to demonstrate that with regard to microglia , astrocytes , and neurons , the fundamental triumvirate of cells in the CNS , it was the microglia that most aggressively responded to interaction with B . burgdorferi . In addition to inducing a pronounced and sustained production of cytokines and chemokines in microglial cells , B . burgdorferi also activated important inflammatory signaling pathways in these cells . Together , these responses potentially contributed to creating a reactive environment that was toxic to the SY cells . Interestingly , we also found that in addition to inducing SY cell apoptosis through a bystander effect , there might be a requirement for direct cell-cell contact between microglia and neurons for end-stage damage to occur . In early experiments , we determined that when SY cells were co-cultured in the presence of microglia and B . burgdorferi for at least 5 days ( MOI 10∶1 ) , significant increases in SY , but not glial cell apoptosis occurred . Our experiments also ruled out any B . burgdorferi related upregulation in the production of nitric oxide . While previous reports had indicated that L-OspA would induce significant levels of apoptosis in primary rhesus astrocytes [67] , the L-OspA concentration of 1 µg/ml used in those experiments was 4 times greater than the one used in the current experiments . Using a formula developed by Norgard , et al . [68] we approximated the total amount of outer surface lipoproteins correlating to the number of spirochetes used in our MOI . An L-OspA concentration of 0 . 25 µg/ml provided a quantitatively more realistic representation of lipoproteins present in B . burgdorferi at the MOI used in our studies . We further discovered that although B . burgdorferi did induce a potent expression and secretion of the inflammatory cytokines/chemokines IL-6 , IL-8 and TNF in microglia , additional mediators were required to trigger neuronal cell apoptosis . These results prompted us to broaden our study of potential inflammatory mediators , and at the same time , to explore B . burgdorferi-activated pathways in microglia and SY cells that might be further contributing to the neurotoxicity demonstrated in the apoptosis assays . By expanding our search for the expression of cytokines and chemokines that were potentially relevant to B . burgdorferi-induced neurotoxicity we found that in addition to IL-6 , IL-8 and TNF , the four well known proinflammatory chemokines CCL2 , CCL3 , CCL4 and CCL5 were significantly upregulated in microglial cells co-cultured with B . burgdorferi . Upregulation of CC chemokines has been described both in murine models of neurocysticerosis and multiple sclerosis ( experimental autoimmune encephalomyelitis , EAE ) [69] , [70] . CCL2 in particular has been shown to play a role in the pathogenesis of EAE [71] . Perhaps most interesting were the microarray results showing that each of these molecules , with the exception of CCL3 , was represented in more than one of the microglial signaling pathways that were most affected by B . burgdorferi . Considering the high number of inflammatory genes involved in these pathways and their potential for cross-talk and downstream regulation , we believe that the TREM1 pathway , together with those involving PRR , IL-10 , IL-6 and Chem . Sig . , contribute to B . burgdorferi-induced inflammation in the CNS . Even though some of the remaining affected pathways may have provided checks and balances to our findings , many of the transcript changes that occurred in the pathways that we focused on were proinflammatory and included both innate and adaptive immune response molecules . As such , there seems to be a consistent pattern of inflammatory signaling in microglial cells that is directly associated with the presence of B . burgdorferi , and that might adversely affect nearby neuronal cells . When glial cells were co-cultured with B . burgdorferi in the presence of SY cells the concentration of inflammatory mediators was often reduced as compared to that elicited in the absence of SY cells ( Table 2 ) . Since we saw no evidence of glial cell apoptosis , this decrease is most likely due to the reduced number of glial cells , the main producers of pro-inflammatory mediators that were included in these cultures . There are reports that apoptotic neurons co-cultured with microglia may down-regulate microglial synthesis of pro-inflammatory molecules [32] . Such a mechanism also may have contributed to the observed reduction , at least in the case of microglia/SY cell co-cultures . Using microarray analysis , we also showed that B . burgdorferi-activated microglia could invoke changes in their cellular environment that affected gene expression in proximal SY cells . When SY cells were co-cultured with B . burgdorferi alone , SY gene expression for molecules in the p53 signaling pathway indicated a mode of cell survival and proliferation . This conclusion was founded on the observed upregulation in transcripts for AKT1 and Bcl-XL , paired with decreased gene expression for DRAM . AKT1 , which is activated through the phosphatidylinositol 3-kinase ( PI3K ) pathway , is known to stimulate cell cycle progression ( via p21 phosphorylation and release from CDK2 ) and to play an important role in promoting cell survival through the suppression of apoptosis [72]-[74] . Bcl-XL is likewise a strong promoter of cell survival , but as one of the major anti-apoptotic members of the conserved Bcl-2 family of proteins , it functions to inhibit programmed cell death through the control of mitochondrial membrane permeabilization [75]–[78] . DRAM on the other hand , is a lysosomal protein critical for the ability of p53 to induce either autophagy or programmed cell death [79]–[81] . In parallel experiments where microglia were included in the co-culture of SY cells with B . burgdorferi , but physically separated from the SY cells by transwell inserts , we found a striking change of circumstance . One primary difference observed with the inclusion of microglial cells was increased SY cell gene expression of the tumor suppressor PTEN . This molecule acts as a dual protein and lipid phosphatase that can modulate cell survival , proliferation and apoptosis through its downstream effects on phosphatidylinositol 3-kinase ( PI3K ) and the AKT1 gene [72] , [82] , [83] . Complementing the PTEN regulation of AKT1 was a decrease in transcript for CDK2 combined with transcript increases for the CDK inhibitors p21 and GSK3β . Within the cell cycle machinery , cyclin-dependent kinases ( CDKs ) associate with their respective cyclins to drive a cell through the G1/S checkpoint . CDK2 associates with cyclin E and during events of cell stress or DNA damage , p21 can bind the CDK2/cyclinE complex and induce cell cycle arrest at G1/S . Furthermore , association of the CDK4 with cyclin D1 can also be disrupted by the action of GSK3β , which phosphorylates cyclin D1 , triggering its nuclear export and degradation [84]–[86] . Both of these events , if realized , would indicate a drift toward cell cycle arrest , which was not apparent in the transcript changes for the culture of SY cells with B . burgdorferi , but lacking microglia . Other significant changes in gene expression were found in members of the apoptotic Bcl-2 family . Transcript of the anti-apoptotic Bcl-XL molecule , whose function was described previously , changed from being up-regulated when microglia were not present to being markedly down-regulated in the neuron/microglia/B . burgdorferi co-cultures . At the same time , expression of the pro-apoptotic Bax and NOXA-1 transcripts was found to be increased . Under normal conditions , Bax is constitutively associated with ly-XL . During cellular stress , Bax can be activated by a BH3-only protein such as Bid , Bim or NOXA-1 . It may also be displaced by Bad or Bik molecules which possess Bad-like BH3 domains , but act directly on Bcl-2 or Bcl-XL rather than Bax itself , freeing it from Bcl-XL to translocate from the cytosol and directly bind to the mitochondrial membrane , initiating an apoptotic cascade [75] , [76] , [87]–[89] . AKT1 kinase is responsible for the phosphorylation of Bad , keeping it tied to its support molecule 14-3-3 . In an inactivated state , such as that induced by the up-regulation of PTEN , Bad may become unphosphorylated , disassociate from 14-3-3 and move to displace Bax [75] , [88] . While the results of in vitro experiments may not always mirror in vivo responses , this compilation of neuronal transcript changes in the p53 signaling pathway appears to be predicated on the presence of microglia , and provides a compelling example of the neurotoxic effects that activated microglia may have on neurons in their surroundings . Finally , although we have presented evidence of the ability of microglia to harm SY cells through a bystander effect , our findings indicate that microglia-SY cell proximity , while necessary , may not be sufficient for end-stage SY cell damage/apoptosis to occur . When transwell filter inserts were incorporated into our apoptosis experiments , no significant increases in SY cell death were observed regardless of whether microglia were included in the co-cultures of SY cells with B . burgdorferi or not . This differed significantly from a near 8-fold increase in SY cell apoptosis that occurred when microglia ( from the same animal ) and SY cells were in direct physical contact during their co-culture with B . burgdorferi , indicating that neuronal-microglia contact was required for apoptosis to occur . Several investigators have reported a similar cell-cell contact requirement for apoptosis induced by activated macrophages and other non-neuronal cells [62]–[64] , [90] . Whether their contact with mediators secreted by B . burgdorferi-activated microglia is itself enough to critically damage SY cells , or it simply acts to prime the cells for eventual apoptosis , remains in question . Due to viability constraints of the cell culture medium , we were not able to extend our apoptosis assays beyond 5 days . We therefore cannot exclude the possibility that longer exposure to microglial mediators might have induced apoptosis in SY cells . If this were the case , the cell-cell contact hypothesis would shift from one of “requirement” to one of “influence” . Further , even though steps were taken to more normalize the SY cell-line phenotype , these cells may respond differently than would non-transformed neurons . While experiments with primary neurons would be optimal , they are often not practical . Similar experiments with other cell lines and even primary neurons may be critical to understanding how B . burgdorferi affects neurons of the CNS during neuroborreliosis .
|
Lyme disease , which is transmitted to humans through the bite of a tick , is currently the most frequently reported vector-borne illness in the northern hemisphere . Borrelia burgdorferi is the bacterium that causes Lyme disease and it is known to readily induce inflammation within a variety of infected tissues . Many of the neurological signs and symptoms that may affect patients with Lyme disease have been associated with B . burgdorferi-induced inflammation in the central nervous system ( CNS ) . In this report we investigated which of the primary cell types residing in the CNS might be functioning to create the inflammatory environment that , in addition to helping clear the pathogen , could simultaneously be harming nearby neurons . We report findings that implicate microglia , a macrophage cell type in the CNS , as the key responders to infection with B . burgdorferi . We also present evidence indicating that this organism is not directly toxic to neurons; rather , a bystander effect is generated whereby the inflammatory surroundings created by microglia in response to B . burgdorferi may themselves be toxic to neuronal cells .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/bacterial",
"infections",
"infectious",
"diseases/infectious",
"diseases",
"of",
"the",
"nervous",
"system",
"immunology/innate",
"immunity"
] |
2009
|
Microglia Are Mediators of Borrelia burgdorferi–Induced Apoptosis in SH-SY5Y Neuronal Cells
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Chemerin is an adipokine proposed to link obesity and chronic inflammation of adipose tissue . Genetic factors determining chemerin release from adipose tissue are yet unknown . We conducted a meta-analysis of genome-wide association studies ( GWAS ) for serum chemerin in three independent cohorts from Europe: Sorbs and KORA from Germany and PPP-Botnia from Finland ( total N = 2 , 791 ) . In addition , we measured mRNA expression of genes within the associated loci in peripheral mononuclear cells by micro-arrays , and within adipose tissue by quantitative RT-PCR and performed mRNA expression quantitative trait and expression-chemerin association studies to functionally substantiate our loci . Heritability estimate of circulating chemerin levels was 16 . 2% in the Sorbs cohort . Thirty single nucleotide polymorphisms ( SNPs ) at chromosome 7 within the retinoic acid receptor responder 2 ( RARRES2 ) /Leucine Rich Repeat Containing ( LRRC61 ) locus reached genome-wide significance ( p<5 . 0×10−8 ) in the meta-analysis ( the strongest evidence for association at rs7806429 with p = 7 . 8×10−14 , beta = −0 . 067 , explained variance 2 . 0% ) . All other SNPs within the cluster were in linkage disequilibrium with rs7806429 ( minimum r2 = 0 . 43 in the Sorbs cohort ) . The results of the subgroup analyses of males and females were consistent with the results found in the total cohort . No significant SNP-sex interaction was observed . rs7806429 was associated with mRNA expression of RARRES2 in visceral adipose tissue in women ( p<0 . 05 after adjusting for age and body mass index ) . In conclusion , the present meta-GWAS combined with mRNA expression studies highlights the role of genetic variation in the RARRES2 locus in the regulation of circulating chemerin concentrations .
Chemerin has been extensively studied as an adipokine associated with obesity and related phenotypes [1]–[4] . It is secreted from adipose tissue as an 18-kDa precursor protein which is activated by several extracellular cleavage steps [5]–[7] . The class of proteases responsible for the transformation of pro-chemerin to chemerin also determines pro-inflammatory or anti-inflammatory function of the protein . Interestingly , proteolytic processing is also suggested to be involved in the inactivation of the protein . By binding with the G protein-coupled receptor chemokine-like receptor 1 ( CMKLR1 ) chemerin activates nuclear factor-kB and MAPK pathways [1] , [6] , [7] . Chemerin is highly expressed in white adipose tissue and its expression and secretion increases with adipogenesis [1] , [5] . From a physiological point of view , chemerin was initially reported as a chemo-attractant for several types of immune cells [6] . Because of its role in chemotaxis of dendritic cells and macrophages , this adipokine is proposed to be a critical link between obesity and chronic inflammation of adipose tissue . Serum chemerin concentrations have been shown to be moderately heritable , with about 25% of variation attributed to genetic factors [8] . A recent genome-wide association analysis ( GWAS ) in 523 Mexican-American individuals revealed 7 loci moderately associated with chemerin serum concentrations . However , none of the suggested variants achieved genome-wide significance level and a replication cohort was not available in that study [8] . The single nucleotide polymorphism ( SNP ) showing the strongest evidence of association ( rs347344; p = 1 . 4×10−6 ) was located within epithelial growth factor-like repeats and discoidin I-like domains 3 ( EDIL3 ) playing an important role in angiogenesis , a key step in adipose tissue expansion in obesity [8] . Notably , variants in retinoic acid receptor responder 2 ( RARRES2 ) , the gene encoding chemerin , have been shown to be associated with increased visceral fat mass in non-obese subjects [9] and with increased incidence of the metabolic syndrome [10] . Co-expression network analyses in gluteal and abdominal adipose tissue showed that rs10282458 in the RARRES2/REPIN1 region modulated RARRES2 expression and was associated with body mass index [11] . In summary , none of the previously reported loci has either reached genome-wide significance levels or has been sufficiently replicated . Thus , the heritability of serum chemerin concentration still remains largely unexplained . Therefore , we conducted a meta-analysis of GWAS for serum chemerin in three independent cohorts: the Sorbs ( N = 824 ) and KORA ( N = 1630 ) from Germany and the PPP-Botnia ( N = 337 ) from Finland . To functionally support our GWAS findings , we performed more detailed analyses in the Sorbs cohort comprising interaction , gene expression quantitative trait loci , expression–association and causal analyses of peripheral blood mononuclear cells ( PBMC ) mRNA expression profiles of genes mapping within the associated loci as well as Gene expression analyses in adipose tissue by quantitative RT-PCR in an independent sample .
Serum chemerin concentrations were positively correlated with BMI ( p = 2 . 4×10−10 , beta 0 . 442 ) and % body fat ( p = 7 . 9×10−9 , beta = 0 . 008 ) but not with fat distribution ( Table 1 ) . Furthermore , chemerin levels correlated with elevated fasting insulin ( p = 3 . 5×10−4 , beta = 0 . 071 ) , HOMA-IR ( p = 0 . 001 , beta = 0 . 065 ) and AUCglucose ( p = 0 . 033 , beta = 0 . 145 ) . Interestingly , serum chemerin concentrations are also positively associated with adipocyte fatty acid binding protein ( AFABP ) and progranulin levels but not with vaspin and adiponectin concentrations ( Table 1 ) . Additionally , chemerin levels were negatively correlated with renal function ( p = 7 . 8×10−5 , beta = −0 . 255 ) . The significant degree of relatedness observed in the Sorbs cohort allowed us to estimate the heritability of chemerin concentration , which was 16 . 2% , using a mixed-model approach proposed by Amin et al . and Aulchenko et al . [12] , [13] . No general inflation of meta-analysis statistics was observed ( λ = 0 . 99 ) . Based on the fixed effects model , 30 SNPs on chromosome 7 reached genome-wide significance ( p<5 . 0×10−8 ) in the meta-analysis of Sorbs ( n = 824 ) , KORA ( n = 1630 ) and PPP ( n = 337 , Table 2 ) . All of these SNPs were consistently associated with p<10−3 in both the KORA and the Sorbs cohorts but not in the PPP cohort . However , effect sizes and directions of effects were concordant between all three studies . The strongest evidence for association was observed for rs7806429 at chromosome 7 ( p = 7 . 8×10−14 , beta = −0 . 067 , explained variance 2 . 0% , Table 2 and S1 Figure ) . All other SNPs with p<5 . 0×10−8 were in linkage disequilibrium ( LD ) with rs7806429 ( minimal r2 = 0 . 43 in the Sorbs cohort ) . When adjusting chemerin levels for rs7806429 , these SNPs are no longer significant in the Sorbs cohort ( minimal p-value 0 . 19 ) , i . e . no independent effects of other SNPs at this locus were detected . A regional association plot is provided in Fig . 1 . The obvious gap detectable between positions 149 . 58 Mbp and 149 . 64 Mbp is caused by low imputation efficacy of Affymetrix 500 K SNP panels resulting in post-imputation quality drop-outs in the Sorbs cohort and in the Botnia study . The results of the subgroup analyses in males and females were consistent with data in the total cohort ( S2 Table ) . No significant interaction of rs7806429 and sex was observed ( p = 0 . 83 in the Sorbs cohort ) . Of note , 4 SNPs not in LD with rs7806429 have been identified with p-values<10−6 using the fixed effect model in at least one of the groups “all” , “male” or “female” ( S2 Table ) . We consider these results as being suggestive but requiring further investigations . Thus , two other loci on chromosomes 3 ( lead SNP: rs2594989 in ATG7 - Autophagy Related 7 ) and 15 ( lead SNP: rs8027521 ) should be considered . The first locus showed consistent effects across all studies and subgroups but failed to reach genome-wide significance . The significant effect of the locus on chromosome 15 was primarily due to the strong effect size observed in the Sorbs cohort ( S2 Table ) . We analysed the correlation of our top-hits with published GWAS-hits of the GWAS catalogue ( downloaded on 12th Sept . , 2013 ) . The maximum r2 was 0 . 2 for the hit on chromosome 15 , suggesting there are no pleiotropic effects regarding published GWAS traits [14] . Finally , we performed a Mendelian randomization analysis using our top-SNP as an instrumental variable . We considered different parameters of obesity or the metabolic syndrome as possible causal endpoints of chemerin ( Table 1 ) . Results are presented in S6 Table . Analysis suggests a causal relationship of chemerin and adiponectin while causality regarding all other endpoints was not significant . Replication of top-SNPs was performed in 967 samples of the LIFE Leipzig Heart Study [15] . All top-SNPs were either directly measured or imputed with high quality , i . e . no proxies were required . Replication analysis revealed a clear support of our results at chr . 7 with similar effect sizes as observed for the Sorbs and KORA cohort ( S7 Table ) . In contrast , no additional evidence was generated for the loci at chrs . 3 and 15 .
Chemerin is an adipokine proposed to link obesity and chronic inflammation of adipose tissue . Consistent with previous reports [1] , [2] , serum chemerin concentrations were positively correlated with BMI and % body fat in our study . Furthermore , chemerin levels correlated with elevated fasting insulin , HOMA-IR and AUCglucose . Although serum chemerin levels are heritable , with estimates of 0 . 25 in Mexican-Americans [8] , studies aimed at identification of genetic factors explaining the variability in circulating chemerin levels are lacking . Here , we conducted a meta-analysis of GWAS for serum chemerin in three independent cohorts from Europe ( Germany and Sweden/Finland ) . The heritability of chemerin in the Sorbs cohort was re-estimated to be 16 . 2% which is close to the above mentioned estimate . We found 30 SNPs within the RARRES2-LRRC61 locus on chromosome 7 which reached genome-wide significance levels in the meta-analysis . The strongest evidence for association was observed at rs7806429 , which represents a cluster of SNPs in pairwise LD . The results of the subgroup analyses in males and females were consistent with findings in the total cohort . No significant SNP-sex interaction was observed . Our lead SNP rs7806429 is in LD with rs10278590 ( r2 = 0 . 64 in the Sorbs cohort ) reported to be associated with visceral fat mass in non-obese subjects [9] but not with rs17173608 ( r2 = 0 . 049 in the Sorbs cohort ) reported to be associated with the incidence of metabolic syndrome [10] . Mendelian randomization analysis suggests a causal relationship between chemerin and adiponectin in the Sorbs cohort , which may appear plausible when considering the potential role of chemerin in regulation of adipogenesis [5] . Yet , we see these data with caution since only nominal significance was achieved for the causality test , i . e . results are not robust against multiple testing correction . Noteworthy , despite missing genome-wide significance levels for association with chemerin , the present GWAS revealed additional variants on chrs . 3 ( lead SNP: rs2594989 in ATG7 ) and 15 ( lead SNP: rs8027521 ) definitely deserving further consideration . The first locus showed consistent effects across all studies . It includes the ATG7 which seems to be an appealing candidate for metabolic studies when considering the emerging evidence for the implication of autophagy in the regulation of adipose tissue and beta cell functions [18] . Although the precise mechanism by which autophagy regulates adipogenesis is unknown , it has been shown that Atg7−/− animals have smaller white adipose tissue depots and adipocyte-specific knockdown of Atg7 lead to the development of brown like adipose tissue [19] , [20] . One might therefore hypothesise that any developmental disturbance in autophagy may affect adipose tissue mass and homeostasis . Thus , alteration in autophagy caused by genetic variants in autophagy genes ( e . g . ATG7 ) might result in adverse adipogenesis which consequently might influence expression of adipokines such as chemerin . In addition , we tested the association of SNPs reported by Bozaoglu et al . [8] with chemerin levels in our meta-analysis ( S4 Table ) . Five out of the seven reported SNPs were included in our meta-analysis . For the other two SNPs we analysed adequate proxies , namely rs12534101 for rs11971186 ( r2 = 0 . 99 ) and rs6701545 for rs4446959 ( r2 = 1 . 0 ) . We observed only nominal significance of rs1405069 in the Sorbs cohort and of rs347344 in the PPP cohort , the latter SNP having discordant direction of effect between cohorts . In the meta-analysis , we observed only nominal significance for rs1405069 with concordant direction of effects between cohorts . Hence , we could not convincingly replicate the hits reported by Bozaoglu et al . These inconsistencies between the studies could possibly be attributed to different ethnicities of the studied cohorts ( Mexican-Americans vs . Europeans ) as well as limited sample size of the previous GWAS by Bozaoglu et al which only included 523 individuals [8] . To functionally support the present GWAS findings , we sought to elucidate possible molecular mechanisms underlying the SNP associations with serum chemerin . An eQTL analysis in PBMCs revealed a strong association between rs7806429 and mRNA levels of AL359058 and LRRC61 ( also known as FLJ31392 , HSPC295 , MGC3036 ) . Both transcripts map within the chromosomal region harbouring variants with lowest p-values in the chemerin GWAS , thus suggesting cis-regulation of the expression of corresponding genes . Nevertheless , none of the transcripts correlated with chemerin levels , i . e . the association between rs7806429 and serum chemerin does not seem to be mediated by the mRNA expression of AL359058 or LRRC61 in PBMCs . Unfortunately , we could not analyse possible SNP associations with RARRES2 transcripts since these were not expressed in PBMCs . However , our top-SNP is in linkage disequilibrium with variants showing eQTL effects of RARRES2 in other tissues such as brain and lymphoblastoid cell lines [16] , [17] . In this line , rs7806429 was associated with mRNA expression of RARRES2 in visceral adipose tissue of female participants from the Leipzig cohort . We only detected significant associations in women . Hence , this result may at least in part suggest that the observed SNP-chemerin association could be mediated by the role of the variant in transcription control . It has to be acknowledged though , that serum chemerin was not available in the Leipzig cohort , i . e . no direct evidence for correlation between adipose mRNA expression and serum chemerin could be tested . Nevertheless , consistent correlations of metabolic parameters with both , RARRES2 mRNA expression in the Leipzig cohort as well as serum chemerin in the Sorbs cohort suggests a relationship of RARRES2 expression in fat tissue and circulating chemerin levels which could explain the observed SNP-chemerin associations . Further support comes from Min et al . who employed a whole-genome expression and genotype profiling on abdominal adipose tissue and found that rs10282458 , an eSNP affecting expression of RARRES2 , was associated with BMI [11] . It is noteworthy that rs7806429 , the lead SNP from our study , is in high LD ( r2>0 . 7 ) with rs10282458 , which however , was not included in our meta-analysis due to quality issues . On the other side , we further checked for potential associations in publically available GWAS datasets from GIANT consortium ( http://www . broadinstitute . org/collaboration/giant/index . php/GIANT_consortium ) and found rs7806429 being nominally associated with BMI ( p = 0 . 01; N = 123 , 835 ) . In contrast , no associations were observed for glucose and insulin related traits in the GWAS dataset from MAGIC consortium ( http://www . magicinvestigators . org ) . Moreover , functional consequences of the associated SNPs were predicted using the RegulomeDB [21] . Including all 26 SNPs in strong LD with rs7806429 ( based on r2>0 . 8 in HapMap CEU population ) , we identified 17 variants reaching a score of 1 , suggesting that these polymorphisms might explain an eQTL or affect corresponding transcription factor binding sites . For example , as shown for rs17173617 , the reported eQTL associates with the expression of LRRC61 , RARRES2 and C7orf29 in lymphoblastoid cell types [17] , [22] . Regarding to transcription factor binding sites , it is noteworthy that rs2108854 maps within the FOXP1 [23] , rs3735171 within the AP-4 [23] , [24] or Brn-2 and rs11769348 within the NFE2L2 binding motif [24] . These data strongly suggest causative variants within the chemerin-associated locus and warrant further in-depth functional analyses to elucidate molecular mechanisms underlying the observed associations . Finally , we would like to note that it may not appear surprising that variants associated with circulating chemerin map within a locus including the RARRES2 which itself encodes chemerin . However , history of GWAS showed that biologically plausible genes do not necessarily point towards easily identifiable risk variants . Indeed , our locus was not discovered by the previous GWAS for chemerin [8] . In conclusion , the present meta-analysis of GWAS for serum chemerin levels is the first study to demonstrate robust SNP-associations reaching genome-wide significance and highlights the role of genetic variation in the RARRES2 in the regulation of circulating chemerin concentrations possibly caused by altered gene expression in fat tissue .
Paired samples of visceral ( vis ) and subcutaneous ( sc ) adipose tissue were obtained from 636 Caucasian men ( N = 203 ) and women ( N = 433 ) , undergoing open abdominal surgery . The subjects had a mean age of 50±14 years and a mean BMI of 43 . 9±12 . 9 kg/m2 ( women: 49±13 years and 44 . 5±12 . 3 kg/m2; men: 53±15 years and 42 . 8±14 . 2 kg/m2 ) . All subjects maintained a stable weight ( within 2% of the body weight ) for at least 3 months before surgery . Patients with severe conditions including generalized inflammation or end-stage malignant diseases were excluded from the study . Samples of vis and sc adipose tissue were immediately frozen in liquid nitrogen after explantation . An OGTT was performed after an overnight fast with a 75 g standardized glucose solution ( Glucodex Solution 75 g; Merieux , Montreal , Canada ) . In addition to the above mentioned clinical parameters , abdominal vis and sc fat area were calculated using computed tomography scans at the level of L4-L5 , and percentage body fat was measured by dual-energy X-ray absorptiometry ( DEXA ) . The ethics committee at the Medical Faculty of the University of Leipzig specifically approved this study , and all subjects gave written informed consent before taking part in the study . De novo genotyping of the SNP rs7806429 was done using the TaqMan SNP Genotyping assays according to the manufacturer's protocol ( Applied Biosystems , Inc . , Foster City , CA ) . To validate the reproducibility of genotyping , a random subset ( about 5% ) of the sample was re-genotyped; all genotypes matched initial designated genotypes . No deviations from HWE were observed ( p>0 . 1 ) . Potential functional consequences of the associated SNPs were analysed using the RegulomeDB [21] . Briefly , RegulomeDB is a database that annotates SNPs with known and predicted regulatory elements in the human genome , such as DNAase hypersensitivity , binding sites of transcription factors , and promoter regions . Finally , a Mendelian randomization analysis was performed using our top-associated SNP as an instrumental variable [33] . This analysis aimed at establishing causal links between chemerin and other parameters of obesity and the metabolic syndrome as presented in Table 1 . Replication of top-SNPs was performed in samples of the LIFE Leipzig Heart Study . Top-SNPs to be replicated were retrieved from these data by searching for well-imputed proxies using SNAP ( https://www . broadinstitute . org/mpg/snap/ldsearch . php ) . For a total of 967 non-diabetic individuals , both , chemerin measurements with adequate covariate information and SNP data were available . For replication analysis , we analysed additive models of chemerin-SNP associations adjusting for age , sex and BMI as in the cohorts of the meta-analysis . Taking into account the characteristics of the LIFE LeipzigHeart study population , we also adjusted for fasting status and disease status ( coronary artery disease case/control status ) . Pre-processing of RNA microarray data relied on the intensities of 47 , 323 transcripts derived from Illumina BeadStudio without background correction and normalization measured in 1 , 029 individuals of the Sorbs cohort . Steps for pre-processing comprised 1 . filtering of individuals with atypical low number of expressed genes ( median - 3 interquartile ranges ( IQR ) of the cohort's values ) , 2 . quantile normalization and log2- transformation , 3 . filtering individuals with atypical gene-expression profiles ( Euclidian distance to average expression larger than median +3 IQR ) , 4 . filtering individuals with atypical values of internal quality parameters ( quantified as Mahalanobis distance of quality control probes included on the HT-12 v4 chip by Illumina , individuals having a larger value than median +3 IQR of this measure were excluded ) , 5 . correcting for batch effects on the basis of hybridisation chip numbers using Empirical Bayes estimates [34] , 6 . linear adjustment for age , sex , and lymphocyte and monocyte cell counts . A total of 924 individuals fulfilled all quality criteria . For 898 of these , SNP array data were also available for eQTL analyses . To provide functional evidence for the SNPs with the strongest evidence for association in the meta-analysis , we performed eQTL analyses in PBMCs including all SNPs reaching genome-wide significant association with chemerin serum levels . Moreover , we examined potential correlations between the corresponding regulated transcripts and chemerin levels . Finally , we tested for causal relationships between top-SNPs , gene-expression and chemerin levels by comparing effect sizes ( beta-coefficients ) of SNPs between the two linear models: “chemerin depending on SNP” and “chemerin depending on regulated gene-expression and SNP” . To measure human adipose tissue expression of RARRES2 in the Leipzig cohort , total RNA was isolated from paired subcutaneous and visceral adipose tissue samples using TRIzol ( Life Technologies , Grand Island , NY ) , and 1 µg RNA was reversely transcribed using standard reagents ( Life Technologies ) . RARRES2 mRNA expression was measured by quantitative real-time RT-PCR using TaqMan methodology , and fluorescence was detected on an ABI PRISM 7500 sequence detector according to the manufacturer's instructions ( Applied Biosystems , Darmstadt , Germany ) . Human RARRES2 mRNA expression was calculated relative to the mRNA expression of HPRT and 18S rRNA , determined by a premixed assay on demand ( PE Biosystems , Darmstadt , Germany ) . The specificity of the PCR was further verified by agarose gel electrophoresis .
|
Chemerin is an adipokine proposed to link obesity and chronic inflammation of adipose tissue . In the present study we show that circulating chemerin is a heritable trait . In a meta-analysis of genome-wide association studies ( GWAS ) of 2 , 791 individuals from Germany and Finland , we identified common genetic variants which associate with serum chemerin levels . The variants map within the retinoic acid receptor responder 2 ( RARRES2 ) /Leucine Rich Repeat Containing ( LRRC61 ) at chromosome 7 . To better understand the potential functionality of the identified variants , we also provide insights into the mRNA expression of RARRES2 ( encoding chemerin ) in blood and adipose tissue . Our results highlight the role and function of genetic variation in the RARRES2 locus in the regulation of circulating chemerin concentrations .
|
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"Methods"
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2014
|
Genome Wide Meta-analysis Highlights the Role of Genetic Variation in RARRES2 in the Regulation of Circulating Serum Chemerin
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Proper segregation of chromosomes during meiosis requires the formation and repair of double-strand breaks ( DSBs ) to form crossovers . Repair is biased toward using the homolog as a substrate rather than the sister chromatid . Pch2 is a conserved member of the AAA+-ATPase family of proteins and is implicated in a wide range of meiosis-specific processes including the recombination checkpoint , maturation of the chromosome axis , crossover control , and synapsis . We demonstrate a role for Pch2 in promoting and regulating interhomolog bias and the meiotic recombination checkpoint in response to unprocessed DSBs through the activation of axial proteins Hop1 and Mek1 in budding yeast . We show that Pch2 physically interacts with the putative BRCT repeats in the N-terminal region of Xrs2 , a member of the MRX complex that acts at sites of unprocessed DSBs . Pch2 , Xrs2 , and the ATM ortholog Tel1 function in the same pathway leading to the phosphorylation of Hop1 , independent of Rad17 and the ATR ortholog Mec1 , which respond to the presence of single-stranded DNA . An N-terminal deletion of Xrs2 recapitulates the pch2Δ phenotypes for signaling unresected breaks . We propose that interaction with Xrs2 may enable Pch2 to remodel chromosome structure adjacent to the site of a DSB and thereby promote accessibility of Hop1 to the Tel1 kinase . In addition , Xrs2 , like Pch2 , is required for checkpoint-mediated delay conferred by the failure to synapse chromosomes .
Meiosis is a specialized cell division program to produce haploid gametes . To achieve faithful chromosome segregation during meiosis I ( MI ) , cells utilize meiotic recombination to establish physical connections through the formation of chiasmata or crossing-over at the DNA level between homologous chromosomes [1] . In budding yeast , meiotic recombination is initiated by programmed double-strand breaks ( DSBs ) catalyzed by a topoisomerase II-like enzyme Spo11 [2] . The 5′ ends of DSBs are resected to produce 3′ single-stranded DNA , at which Dmc1 and Rad51 load to mediate strand exchange with a homologous DNA sequence [3] , [4] . Unlike in vegetative cells where sister chromatids are preferred templates for DSB repair , most meiotic programmed DSBs are repaired using homologous non-sister chromatids [5] , [6] , [7] . A subset of DSBs is repaired to form crossovers ( CO ) through a double Holliday junction ( dHJ ) pathway [8] , [9] , [10] . CO formation and distribution is highly regulated during meiosis; each homolog must receive at least one CO to sustain reductional segregation in meiosis I [11] . Interhomolog bias is established and maintained by regulatory proteins associated with chromosome axis structures , including Hop1 and Mek1 . In response to DSBs , the meiotic chromosome axis protein Hop1 is phosphorylated by Tel1/Mec1 ( ATM/ATR homologs ) [12] . Phosphorylated Hop1 promotes dimerization and auto-activation of Mek1 kinase [13] , [14] , [15] , [16] . A Hop1 mutant that is refractory to Tel1/Mec1 phosphorylation fails to activate Hop1-dependent Mek1 phosphorylation and results in the loss of interhomolog bias [12] . Mek1 kinase plays dual roles by promoting interhomolog bias and checkpoint signaling in the presence of recombination intermediates [13] . The presence of unrepaired DSBs is monitored by DNA damage checkpoint proteins Mec1 , Rad17 , Rad24 , Tel1 , and the MRX ( Mre11-Rad50-Xrs2 ) complex [17] . Mutants defective in the repair of meiosis-induced DSBs activate one or more pathways involving these proteins [17] . Different lesions appear to activate different checkpoint pathways . For example , unresected DSBs appear to activate a checkpoint requiring Tel1 ( ATM homolog ) while unrepaired resected breaks activate a Mec1 ( ATR ) pathway [18] , [19] . Pch2 is a member of the AAA+-ATPase family of proteins and is implicated in a number of meiosis-specific processes in budding yeast Saccharomyces cerevisiae , including meiotic recombination , chromosome axis formation , checkpoint signaling , crossover control and interhomolog bias [20] , [21] , [22] , [23] , [24] . Pch2 participates in one branch of a bifurcated pathway that defines the recombination checkpoint: One branch is regulated by Rad17 and Mec1 , likely in response to ssDNA [19] . A second branch is regulated by Pch2 , however , the activating lesion has not been defined [20] . In mouse the Pch2 homolog TRIP13 plays roles in axis morphogenesis and early steps of recombination [25] , [26] , [27] . In Caenorhabditis elegans and Drosophila melanogaster , PCH-2 plays a role in a checkpoint that monitors synapsis and/or axis formation [28] , [29] , [30] . Whether these seemingly disparate roles of Pch2 share mechanisms in common is an open question . Pch2 was originally identified by mutation as a suppressor of the arrest/delay phenotype conferred by the deletion of ZIP1 [31] , which encodes the transverse element of the synaptonemal complex ( SC ) [32] , [33] . Suppression of the zip1Δ delay phenotype by pch2Δ is enigmatic since the zip1Δ delay is also suppressed by deletion of RAD17 [20] . Multiple roles for Zip1 during meiosis are indicated by the pleiotropic phenotypes associated with the deletion mutation [1] , [34] , therefore it is possible that Pch2 might signal more than one lesion during a challenged meiosis . Our data support these key findings: 1 . Pch2 and Rad17 contribute to suppression of intersister recombination through independent pathways with partially overlapping functions . 2 . Pch2 and Tel1 function in the same epistasis pathway to regulate meiotic recombination checkpoint signaling , independent of Rad17 and Mec1 . 3 . Pch2 functions to signal the presence of unresected breaks leading to the phosphorylation of Hop1 . 4 . Pch2 physically interacts with the N-terminal region of Xrs2 containing putative BRCT repeats . Deletion of this non-essential region of Xrs2 leads to a defect in Pch2-dependent checkpoint signaling . 5 . Xrs2 and Pch2 play a role in the synapsis checkpoint while Tel1 does not . These findings link multiple roles of Pch2 in budding yeast to the ATM homolog Tel1 and/or the MRX component Xrs2 . We propose that phosphorylation of the meiotic chromosome axis protein Hop1 depends on two partially redundant pathways: one pathway involving Tel1 , Pch2 and Xrs2 that responds to the presence of unprocessed DSBs and another pathway involving Mec1 and Rad17 that responds to the presence of resected DSB intermediates of homologous recombination .
Deletion of both PCH2 and RAD17 causes a synergistic reduction in spore viability and accelerated meiotic progression compared to either single mutant or wild type . Spore inviability is suppressed in a spo13 mutant background suggesting that programmed DSBs are repaired , most likely using the sister chromatid as a template [20] . These combined phenotypes led us to suggest that Pch2 and Rad17 function in redundant pathways to suppress the use of sister chromatids to repair meiotic programmed DSBs . To test this , we monitored the presence of intersister ( IS ) and interhomolog ( IH ) joint molecules that form as intermediates of meiotic DSB repair at the HIS4LEU2 hot spot in pch2Δ , rad17Δ and pch2Δ rad17Δ at various time points during meiotic progression in a synchronized cell culture using two-dimensional gel electrophoresis ( Figure 1A , 1B ) . To detect maximal levels of these intermediates we used an ndt80Δ mutant background to block the resolution of dHJs to crossover products [8] , [35] . While the ndt80Δ pch2Δ mutant gave ∼10% higher levels of IH-dHJ compared to the ndt80Δ strain , the levels in ndt80Δ rad17Δ and ndt80Δ pch2Δ rad17Δ were reduced by ∼60% and 67% , respectively . By contrast , while the ndt80Δ pch2Δ mutant gave ∼9% lower levels of IS-dHJ compared to the ndt80Δ strain , this species was increased in ndt80Δ rad17Δ and ndt80Δ pch2Δ rad17Δ mutants by ∼13% and 52% , respectively ( based on averages of measurements from two independent time course experiments ) . Together these results suggest that Pch2 and Rad17 have independent and partially overlapping functions in promoting interhomolog bias . In an independent test , we measured DSB levels in the dmc1Δ mutant background where DSBs form and are resected but their repair is blocked [3] . If the process of upholding interhomolog bias is compromised then breaks can be repaired using sister chromatids [7] . We found that steady-state DSB levels were decreased over two-fold in dmc1Δ pch2Δ rad17Δ compared to dmc1Δ pch2Δ and dmc1Δ rad17Δ ( 5 hours after transfer to SPM; Figure 1C and 1D ) . The observed decrease in DSBs in dmc1Δ pch2Δ rad17Δ ( compare t = 3 hours and t = 5 hours ) , but not in sae2Δ pch2Δ rad17Δ where DSBs are not processed [20] , suggests that repair of DSBs occurs using a sister chromatid . These results suggest that Pch2 and Rad17 are required to uphold the barrier to sister chromatid recombination . From the findings above , we reasoned that Pch2 and Rad17 might independently promote phosphorylation of Hop1 in response to DSBs . In wild-type cells , Hop1 was transiently phosphorylated starting at about t = 3 . 5 , as revealed by slow-migrating bands in a western blot using an α-Hop1 antibody ( Figure 2A ) . Slow-moving Hop1 isoforms were abundant in both pch2Δ and rad17Δ single mutants but dramatically reduced in the pch2Δ rad17Δ double mutant . These results suggest that Pch2 and Rad17 function in different pathways leading to Hop1 phosphorylation . We next examined the phosphorylation status of Mek1 using an α-Akt-substrate antibody to the T327 residue in the activation loop [36] . While phosphorylation of the T327 residue was present in the pch2Δ and rad17Δ single mutants , it was completely abolished in the pch2Δ rad17Δ double mutant , similar to the results seen above for Hop1 ( Figure 2B ) . The reduction in Mek1–3HA phosphorylation in rad17Δ was more dramatic than the reduction of Hop1 phosphorylation in the same background . One interpretation of this result is that Rad17 not only regulates signaling upstream of Hop1 but also impacts the Hop1-dependent autophosphorylation of Mek1 . Consistent with this notion , rad17Δ shows aberrant SC formation [37] perhaps indicating aberrantly formed axial elements . Together , these results demonstrate that two independent pathways defined by Pch2 and Rad17 , respectively , regulate the activation status of the meiotic chromosome axis proteins Hop1 and Mek1 . The failure to phosphorylate Hop1 and Mek1 in the absence of both Pch2 and Rad17 may account for the loss of interhomolog bias in the pch2Δ rad17Δ double mutant background . A hallmark of mutants defective in interhomolog bias is the formation of largely inviable spore products due to reduced levels of interhomolog crossovers [1] . Consistent with this pattern , the pch2Δ rad17Δ double mutant gives <0 . 1% viable spores , while each single mutant gives higher levels ( 37 . 1% for rad17Δ and 92 . 2% for pch2Δ; Table 1 ) [20] . Like Rad17 and Pch2 , the ATR/ATM homologs Mec1 and Tel1 have also been shown to play partially redundant roles in meiotic interhomolog recombination by phosphorylating Hop1 [12] . Since RAD17 and MEC1 are in the same epistasis group that mediates checkpoint signaling in the presence of ssDNA [19] , [37] , one possibility is that Pch2 functions with Tel1 in a separate pathway , perhaps in response to unresected DSBs [18] . To test this , we examined spore viability in mutants containing pair-wise combinations of pch2Δ , rad17Δ , tel1Δ and mec1Δ mutations . In the cases where we predicted the two genes would act in the same pathway ( e . g . pch2Δ tel1Δ and rad17Δ mec1Δ ) , there was no decrease in spore viability compared to the single mutants ( Table 1 ) . By contrast , in the cases where we predicted the two genes would function in different pathways we observed a synergistic decrease in spore viability in the double mutants ( 2 . 9% for pch2Δ mec1Δ and <0 . 1% for rad17Δ tel1Δ ) . In a similar line of reasoning , checkpoint activation leads to a delay in MI division and can be triggered by loss of either Pch2 or Rad17 , but not both . We showed previously that MI division kinetics in the pch2Δ rad17Δ double mutant is faster than in wild type , yet is delayed in the two single mutant strains [20] . From a first approximation , the epistasis pattern described above for spore inviability holds true: i ) the MI delay conferred by pch2Δ was suppressed by mec1Δ to give divisions even faster than WT; and ii ) the delay phenotype conferred by rad17Δ was suppressed by tel1Δ ( Figure 3C and 3D ) . Notably , the MI delay in the pch2Δ tel1Δ double mutant was more severe than either single mutant , suggesting that each protein may function in additional pathways that do not involve the other . To further confirm the epistasis relationship observed above , we examined Hop1 phosphorylation in the pch2Δ tel1Δ , rad17Δ mec1Δ , pch2Δ mec1Δ and rad17Δ tel1Δ double mutant combinations . As expected , we observed abundant Hop1 phosphorylation in pch2Δ tel1Δ and rad17Δ mec1Δ , while only a low level of Hop1 phosphorylation was seen in pch2Δ mec1Δ and rad17Δ tel1Δ which showed very low spore viability and fast meiotic progression ( Figure 3E , 3F and Figure 4A ) . Together , these results suggest Pch2 acts together with Tel1 to promote an essential meiotic process , perhaps by ensuring interhomolog bias through Hop1 phosphorylation . Tel1 is required to signal the presence of unprocessed DSBs during meiosis [17] , [18] . Specifically , deletion of TEL1 eliminates the signaling of unresected DSBs to Hop1 [12] . To test if signaling of unprocessed DSBs also requires Pch2 , we examined Hop1 phosphorylation in both pch2Δ and tel1Δ mutants in a sae2Δ mutant background where breaks are unprocessed to give blunt ends . Hop1 was phosphorylated in a sae2Δ mutant but not in sae2Δ pch2Δ or sae2Δ tel1Δ ( Figure 4B ) , as expected if Tel1 and Pch2 are specifically required for unprocessed DSBs signaling . By contrast , Rad17 was not required for Hop1 phosphorylation in the sae2Δ background ( Figure 4C ) , which is also expected since Rad17 is involved in signaling resected DSBs . As a control , we measured Hop1 phosphorylation in the dmc1Δ mutant background where DSBs are resected to give ssDNA . Hop1 phosphorylation was not affected in dmc1Δ pch2Δ and dmc1Δ tel1Δ ( Figure 4D ) . We noticed that Hop1 protein levels were elevated in the dmc1Δ pch2Δ double mutant ( Figure 4D ) compared to the dmc1Δ single mutant . On the other hand , sae2Δ pch2Δ showed no increase in Hop1 levels compared to the sae2Δ single mutant ( Figure 4B ) . We reasoned that this effect of pch2Δ does not relate to the role of Pch2 in promoting Hop1 phosphorylation per se since pch2Δ only affected Hop1 phosphorylation in the sae2Δ background ( where Hop1 levels were not altered ) but not in the dmc1Δ background ( where Hop1 levels were increased ) . The tel1Δ strain did not show such an effect either , again suggesting this aspect of Pch2 function is independent of its role in Tel1 signaling to Hop1 . We speculate that the increase in Hop1 levels ( or reduced Hop1 protein turnover ) shown here by western blotting likely reflects altered Hop1 abundance/distribution shown previously by immunostaining [21] and is related to Pch2′s role in axis organization and CO control . Interestingly , this effect is manifested at a “post resection” stage of DSB repair since increased Hop1 levels were observed in dmc1Δ but not in sae2Δ . CO designation is also thought to occur around this stage of meiotic prophase [38] , [39] . The ATM homolog Tel1 physically interacts with Xrs2 and promotes the phosphorylation of Sae2 and Hop1 [12] , [40] , [41] . We thus tested if Pch2 also interacts with components of the MRX complex using pair-wise bait-prey combinations of Pch2 with Mre11 , Rad50 and Xrs2 for yeast two-hybrid analysis . In this trial , Pch2 interacted with Xrs2 , but not Mre11 or Rad50 ( Figure 5A ) . Mre11 and Rad50 two-hybrid constructs were functional since we detected interaction between Rad50-Mre11 ( Figure 5B ) and Mre11-Xrs2 ( Figure 5C ) . We narrowed the Pch2-binding region of Xrs2 to a 187 amino acid region in the Xrs2 ( 126–313 ) -Gal4AD construct ( Figure 5D – 5F ) . This region contains two putative BRCT repeats , similar to the human ortholog Nbs1 [42] . Point mutations created to abolish FHA domain function present in Xrs2 ( 1–313 ) -Gal4AD did not abolish interaction with LexA-Pch2 [43] ( Figure 5F ) . The first 313 amino acids of Xrs2 are dispensable for the formation of normal levels of DSBs and crossover recombination products yet DSB turnover and MI division are delayed [44] . We created the allele xrs2ΔN-13myc that deleted the first 313 amino acid coding region of XRS2 and found it delayed MI division ( Figure 5G and 5H ) , presumably due to the slow turnover of DSBs as in the pch2Δ mutant [20] , [21] , [45] . We wondered if xrs2ΔN-13myc , like pch2Δ , would suppress the MI delay conferred by rad17Δ ( and vice versa ) . We found this to be the case with MI division timing in xrs2ΔN-13myc rad17Δ occurring earlier than either single mutant ( Figure 5H ) . By contrast , MI division was delayed in xrs2ΔN-13myc pch2Δ ( Figure 5G ) . Spore viability of xrs2ΔN-13myc rad17Δ ( 1 . 4%; Table 1 ) was dramatically decreased compared to xrs2ΔN-13myc and XRS2-13myc rad17Δ ( 89 . 7% and 26 . 5% , respectively ) , while xrs2ΔN-13myc pch2Δ gave only a modest reduction of spore viability compared to XRS2-13myc pch2Δ ( 74 . 5% and 94 . 5% , respectively ) . To test if xrs2ΔN-13myc affects checkpoint signaling in a similar manner to pch2Δ , we examined the effect of this mutation on Hop1 phosphorylation in xrs2ΔN-13myc rad17Δ and xrs2ΔN-13myc pch2Δ double mutants as well as in sae2Δ and dmc1Δ backgrounds . We found Hop1 phosphorylation was greatly reduced in xrs2ΔN-13myc rad17Δ but not in xrs2ΔN-13myc pch2Δ ( Figure 6A ) . Furthermore , xrs2ΔN-13myc only abrogated Hop1 phosphorylation in sae2Δ but not in dmc1Δ backgrounds ( Figure 6B and 6C ) . The absence of Hop1 phosphorylation in sae2Δ xrs2ΔN-13myc was not due to reduced DSB levels ( Figure 6D ) . Notably , as with dmc1Δ pch2Δ , dmc1Δ xrs2ΔN-13myc accumulated more Hop1 protein ( Figure 6C ) . Taken together , these results suggest that the interaction of Pch2 with the N-terminal region of Xrs2 , and perhaps the putative BRCT repeats specifically , is required for Pch2′s role ( s ) in the recombination checkpoint and axis organization during meiosis . Budding yeast pch2Δ was originally isolated in the BR background as a mutation that suppresses the meiotic arrest that occurs in the absence of Zip1 [31] and Pch2 has been thought to be involved in a “synapsis checkpoint” [46] . In SK1 , zip1Δ caused a meiosis I delay that is partially suppressed by pch2Δ [20] . We found that xrs2ΔN-13myc , but not tel1Δ , suppressed the zip1Δ-induced meiotic delay , suggesting that interaction with Xrs2 may also be required for Pch2′s role in the synapsis checkpoint ( Figure 6E ) . In fact MI delays conferred by tel1Δ and zip1Δ are independent since the double mutant exhibits a more severe delay . It is thus possible that Xrs2-Pch2 interaction is required for most , if not all , functions of Pch2; while Pch2 and Tel1 may perform independent functions besides their concerted role in the recombination checkpoint . Deletion of PCH2 has been shown to sensitize strains carrying hypomorphic alleles of spo11 to give lower levels of spore viability [22] , . Through our studies we found that the deletion of TEL1 also gave a modest reduction of spore viability in a spo11-HA/spo11Y135F-HA background ( 50 . 1% versus 68 . 3%; Table 1 ) , but not to the extent of pch2Δ ( 35 . 8% ) . When both Pch2 and Tel1 are absent , spore viability was dramatically reduced in this background ( 3 . 8% ) . Similar effects were also observed in the spo11-HA homozygous mutant background ( Table 1 ) . These data suggest that Pch2 and Tel1 independently influence an essential meiotic process that is sensitive to DSB levels . Identification of this process will require analysis of the pch2Δ tel1Δ spo11-HA/spo11Y135F-HA strain for defects in other meiotic chromosome events including DSB repair , crossover control , chromosome axis morphogenesis and/or synapsis .
We propose that phosphorylation of the meiotic chromosome axis protein Hop1 is regulated by two partially redundant pathways: one pathway requires Tel1 , Pch2 and Xrs2 and responds to the presence of unprocessed DSBs; a second pathway requires Mec1 and Rad17 and responds to the presence of resected DSB intermediates of homologous recombination ( Figure 6F ) . This model is directly analogous to the different roles of Tel1/Xrs2 and Mec1/Rad17 in the DNA damage response during vegetative growth [18] with Pch2 providing a regulatory feature specific to meiotic chromosomes that coordinate the events of meiotic recombination with axis organization . The physical association of Pch2 with Xrs2 suggests a mechanism to promote interhomolog bias near sites of DSBs by bringing the Tel1/ATM kinase near its substrate Hop1 , a component of the chromosome axis . Pch2 might utilize the binding and/or hydrolysis of ATP to promote conformational changes in axis structure that enable the phosphorylation of Hop1 by Tel1 . Alternatively or in addition , Pch2 interaction with Xrs2 might function to stabilize the association of the MRX complex at the chromosome axis analogous to the interaction of Mdc1 protein ( Mediator of DNA damage Checkpoint ) with the BRCT repeats of the mammalian Xrs2 ortholog , Nbs1 [48] . In this case , Mdc1 stabilizes the association of Nbs1 at sites of DNA damage , thus creating a microenvironment to promote phosphorylation of H2AX by ATM [49] . It is not clear if Pch2 interacts with Xrs2 ( Nbs1 ) in other organisms . The mouse ortholog of Pch2 , TRIP13 , is implicated in early recombination steps that follow DSB resection but precede Rad51 focus formation [25] . It is possible that TRIP13-NBS1 interaction could establish a precondition that facilitates a later step of recombination . Indeed in yeast , deletion of PCH2 results in the slow turnover of resected DSBs [20] , [21] , [45] . In Drosophila , NBS is required for DSB repair [50] , but its role in meiotic recombination has not been explored to date . While Xrs2/Nbs1 proteins are conserved from vertebrates to fungi , there is no apparent ortholog in C . elegans . It remains possible that Pch2 plays a role in a recombination checkpoint in C . elegans that has not yet been uncovered experimentally . Pch2 orthologs in worm and fly are implicated in a checkpoint activated by the failure to synapse chromosome and/or by disruptions in axis formation . The synapsis checkpoint functions in these instances even in absence of DSB formation [28] , [30] . Although synapsis is dependent on DSB formation in budding yeast , several examples implicate Pch2 in a synapsis checkpoint that responds to defects in synapsis and/or axis structure in situations where DSBs are efficiently repaired [13] , [51] . Strong evidence in support of a synapsis checkpoint comes from our previous observation that MEK1-GST , an artificially activated form of MEK1 , acts as a genetic enhancer of zip1Δ by causing MI arrest [13] . Since DSBs are efficiently repaired in this situation [13] , this result suggests that synapsis and/or axis defects trigger the arrest , not the persistence of unrepaired DNA breaks . Deletion of PCH2 , but not TEL1 , can bypass this arrest , suggesting an independent role for Pch2 in a synapsis checkpoint ( unpublished data ) . Similarly , deletion of PCH2 suppresses the meiosis I arrest phenotype that is activated by the presence of aberrantly synapsed chromosomes caused by the non-null allele zip1-4LA , which also repairs DNA breaks efficiently [51] . We found here that xrs2ΔN-13myc , similar to pch2Δ , partially suppressed zip1Δ delay , suggesting that Xrs2 , perhaps through association with Pch2 , is required to execute the synapsis checkpoint . By contrast , Tel1 does not seem to be involved in this branch of Pch2′s function . Borner and colleagues argued previously that Pch2 might mediate Mec1/ATR activity with respect to sensing “structure-dependent interchromosome interactions” [21] . It is possible that a Pch2/Xrs2/Mec1 pathway functions in this program . Further understanding of the differential requirements for Xrs2 and Tel1 for Pch2 function in the recombination checkpoint versus the synapsis checkpoint ( and possibly crossover control ) may help to identify common mechanisms shared among synaptic organisms , where pairing and SC formation are not always coupled to recombination [52] .
All strains are derivatives of SK1 except the strain used for yeast two hybrid spot assay is L40 ( MATa trp1 leu2 his3 LYS2::lexA-HIS3 URA3::lexA-lacZ ) [53] . Deletion mutants were generated by PCR-based gene disruption [54] , [55] . All the mec1Δ strains also carried sml1Δ to suppress inviability . MEK1-3HA and XRS2-13myc were made by using pFA6a-3HA-kanMX6 and pFA6a-13myc-kanMX6 modules , respectively [56] . xrs2ΔN-13myc was created by two-step allele replacement . Briefly , a PCR-amplified URA3 was used to replace the region encoding amino acid 1–313 of Xrs2-13myc . Then a PCR-generated fragment containing 385 bp upstream the first coding ATG fused to 375 bp downstream the ATG encoding amino acid 314 of Xrs2 was used to replace the URA3 , resulting in xrs2ΔN-13myc expressing 13myc-tagged Xrs2 ( 314–854 ) under the native promoter of XRS2 . spo11-HA and spo11Y135F-HA are a gift from Scott Keeney and crossed into our strain background . SBY strain numbers are listed in Table 1 . Additional strains used in this study are: strains isogenic to SBY1903 ( MATa/MATα ho::hisG/″ leu2::hisG/″ ura3 ( ΔSma-Pst ) /″ his4-X::LEU2- ( NBam ) -URA3/HIS4::LEU2- ( NBam ) ) except the indicated mutations: SBY3055 ( ntd80Δ ) ; SBY3280 ( ntd80Δ pch2Δ ) ; SBY3277 ( ntd80Δ rad17Δ ) ; SBY3274 ( ntd80Δ pch2Δ rad17Δ ) ; SBY2591 ( dmc1Δ ) ; SBY2597 ( dmc1Δ pch2Δ ) ; SBY2594 ( dmc1Δ rad17Δ ) ; SBY2606 ( dmc1Δ pch2Δ rad17Δ ) ; SBY3800 ( dmc1Δ tel1Δ ) ; SBY2611 ( sae2Δ ) ; SBY2625 ( sae2Δ pch2Δ ) ; SBY3843 ( sae2Δ tel1Δ ) ; SBY2616 ( sae2Δ rad17Δ ) ; SBY4684 ( sae2Δ xrs2ΔN-13myc ) ; SBY3589 ( MEK1-3HA ) ; SBY3595 ( MEK1-3HA pch2Δ ) ; SBY3592 ( MEK1-3HA rad17Δ ) ; SBY3598 ( MEK1-3HA pch2Δ rad17Δ ) ; strains isogenic to SBY4056 ( MATa/MATα ho::hisG/″ lys2/″leu2::hisG/″ ura3Δ::hisG/″ trp1::hisG/″ GAL3/″ ) except the indicated mutations: SBY3560 ( dmc1Δ ) , SBY4517 ( dmc1Δ xrs2ΔN-13myc ) , SBY3644 ( sae2Δ ) , SBY4514 ( sae2Δ xrs2ΔN-13myc ) , SBY4445 ( zip1Δ ) , SBY4451 ( zip1Δ pch2Δ ) , SBY4448 ( zip1Δ tel1Δ ) , SBY4404 ( zip1Δ xrs2ΔN-13myc ) ; tel1Δ , sml1Δ , and ndt80Δ are marked with hphMX; meclΔ is marked with natMX; all other mutations are marked with kanMX . Time courses were conducted by the SPS method [20] . Briefly , cells were patched on YPG plates ( 3% glycerol , 2% bactopeptone , 1% yeast extract , 2% bactoagar , 0 . 01% adenine sulphate , 0 . 004% tryptophan ) for ∼14 hr and then stripped on YPD plates ( 2% glucose , 2% bactopeptone , 1% yeast extract , 2% bactoagar , 0 . 01% adenine sulphate , 0 . 004% tryptophan ) and grown for 2 days . Single colonies were used to inoculate 5 ml YPD ( plus 0 . 002% uracil if ura3 strains were used ) liquid cultures and grown for at least 24 hr before diluted into SPS ( 1% potassium acetate , 0 . 5% yeast extract , 1% bactopeptone , 0 . 17% yeast nitrogen base , 0 . 5% ammonium sulphate , 1 . 02% potassium biphthalate ) ( plus 0 . 002% uracil if ura3 strains were used ) at O . D . 600 = 0 . 16 . SPS cultures were grown for ∼15 . 5 hr , washed with H2O , and then resuspended into SPM ( 1% potassium acetate , 0 . 02% raffinose , 0 . 009% amino acid powder ) at O . D . 600 = 2–3 . Spore viability data were obtained by sporulation on solid SPM media . All procedures were performed at 30°C . DNA extraction , gel electrophoresis and southern blot were performed as previously described [9] . Meiosis I division timing was determined by calculating the percentage of post-MI cells at indicated time points . Briefly , meiotic cultures were fixed in 50% ethanol and stained with DAPI . Cells with more than 2 DAPI-stained nucleus bodies were counted as post-MI cells . 200 cells were counted for each time points . Denaturing whole-cell extracts were prepared as previously described [57] with modifications . Briefly , 1 mL meiotic cultures at indicated time points were spun down and resuspended in 1 mL ice-cold water with 1 mM PMSF , 10 mM sodium fluoride and 10 mM sodium diphosphate . 150 µL ice-cold 2 N NaOH / 8% 2-ME was then added and mixtures were incubated on ice for 10 min . After added 160 µL ice-cold 50% TCA and incubated on ice for 10 min , mixtures were spun for 8 min at 14000 rpm . Pellets were washed by 500 µL ice-cold acetone and spun for 5 min at 14000 rpm . Washed pellets were dried by spinning in the vacufuge for 8 min and then resuspended in 1X SDS sample buffer with PMSF , sodium fluoride and sodium diphosphate . A bath sonicator was used to facilitate resuspension in acetone and sample buffer . Proteins from denaturing whole-cell extracts were detected by Western blotting using α-Hop1 ( S . Roeder ) , α-HA ( Santa Cruz , sc-7392 ) , α-phospho-Akt substrate ( Cell signaling , #9614 ) , and α-Pgk1 ( Invitrogen , A-6457 ) . Immunoprecipitation was performed as previously described [13] except α-HA antibody ( Santa Cruz , sc-7392 ) was used . Mre11 , Rad50 , and Xrs2 yeast two-hybrid plasmids are a gift from S . Keeney [58] . Xrs2 truncation plasmids were constructed by cloning PCR-generating fragments into the same plasmid for full-length Xrs2 ( pACT2-2 ) . Xrs2 ( 1–313 ) -S47A H50A plasmid was created by QuikChange ( Stratagene ) . LexA-Pch2 plasmid was made by cloning PCR amplified intronless PCH2 coding region into pCA1 plasmid . Y2H spot assay was performed by spotting 5 µL O . D . 600 = 1 cultures onto SC-Leu-Trp plates and SC-Leu-Trp-His + 1mM 3AT plates and grown for 3–5 days .
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Sexually reproductive organisms utilize meiosis to produce gametes ( e . g . egg and sperm ) . During meiosis , chromosome numbers reduce to half ( haploid ) and fertilization restores their numbers to a diploid state so that ploidy can be maintained throughout generations . Meiosis involves two successive divisions ( meiosis I and meiosis II ) that follow a single round of DNA replication . In meiosis I homologous chromosomes segregate , whereas in meiosis II sister chromatids segregate . Failure to properly segregate chromosomes leads to the formation of aneuploid gametes , which are a leading cause of birth defects and pregnancy loss in humans . In most organisms , proper chromosome segregation in meiosis I requires meiotic recombination , where the repair of deliberately introduced double-strand breaks ( DSBs ) generates physical connections between homologous chromosomes . Importantly , DSBs must be repaired in a timely fashion and coordinated with the meiotic cycle by the recombination checkpoint . Here we investigated the role of Pch2 , an AAA+-ATPase protein , in regulating chromosome events during meiotic prophase . We found Pch2 functions with Tel1 ( homolog of ATM ) and the MRX component Xrs2 to signal blunt-ended , unprocessed DSB intermediates of meiotic recombination . In addition , physical interaction between Pch2 and Xrs2 appears to play additional roles during meiosis , independent of Tel1 function .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"model",
"organisms",
"genetics",
"biology",
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"biology",
"genetics",
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"genomics"
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2011
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Pch2 Acts through Xrs2 and Tel1/ATM to Modulate Interhomolog Bias and Checkpoint Function during Meiosis
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Scaffold proteins play a crucial role in facilitating signal transduction in eukaryotes by bringing together multiple signaling components . In this study , we performed a systematic analysis of scaffold proteins in signal transduction by integrating protein-protein interaction and kinase-substrate relationship networks . We predicted 212 scaffold proteins that are involved in 605 distinct signaling pathways . The computational prediction was validated using a protein microarray-based approach . The predicted scaffold proteins showed several interesting characteristics , as we expected from the functionality of scaffold proteins . We found that the scaffold proteins are likely to interact with each other , which is consistent with previous finding that scaffold proteins tend to form homodimers and heterodimers . Interestingly , a single scaffold protein can be involved in multiple signaling pathways by interacting with other scaffold protein partners . Furthermore , we propose two possible regulatory mechanisms by which the activity of scaffold proteins is coordinated with their associated pathways through phosphorylation process .
Protein phosphorylation and dephosphorylation is an important means of protein regulation that occur in both prokaryotic and eukaryotic organisms [1–5] . Phosphorylation of a protein may result in a conformational change in its structure , recruitment of binding partners or change of localization , leading to its activation or deactivation [6 , 7] . In the context of a signaling pathway , a relay of phosphorylation events could allow the transmission of extracellular signals to intracellular targets . One well-known example is the RAS-ERK pathway , in which a small G-protein RAS activates MAP3K RAF , which then phosphorylates and activates MAP2K MEK1 ( MAPKK1 ) . MEK1 then phosphorylates and activates MAPK ERK1/2[8] . Biological systems contain a large number of phosphorylation-related signaling pathways . Many of these signaling pathways share common signaling components and are subject to extensive cross-regulation . The emergence of complex signaling networks prompts the question of specificity , and understanding how individual signals are transduced to arrive at specific outputs is of great importance to the biological community . It is believed that the answer may partially lie in the existence of scaffold proteins . Scaffold proteins act as “molecular glue” , linking multiple components in a phosphorylation-dependent signaling pathway together to facilitate signal transduction , and as such play a crucial role in the regulation of signaling cascades [8–13] . The scaffold proteins exert their effects through simple tethering of signaling proteins , properly orienting target proteins , or allosteric assembly of pathway components . They can enhance signaling specificity by sequestering proteins , preventing unwanted cross-influence between proteins in different signaling pathways . They can also increase the signaling efficiency by increasing the local concentration of each signaling component . Thus , the knowledge of scaffold proteins can help improve our understanding of the regulation of subcellular signal transduction [14] . Traditional biochemistry approach to identifying scaffold proteins requires multiple steps [15 , 16] , including 1 ) selection of a candidate as a scaffold protein and the corresponding signaling pathway; 2 ) testing the protein-protein interactions between the scaffold candidate and the protein members of the selected pathway; and 3 ) assessment of the enhanced signaling readout of the signaling pathway in the presence of the scaffold candidate [12] . To date , there is no report on a systematic effort to comprehensively identify scaffold proteins . In this work , by taking advantage of the existing extensive datasets of protein-protein interactions ( PPIs ) and kinase-substrate relationships ( KSRs ) , we developed a statistical approach to predict scaffold proteins . We predicted a large number of potential scaffold proteins , which share many similar characteristics with known scaffold proteins . Interestingly , we discovered that these predicted scaffold proteins are likely to form scaffold complexes and contain more phosphorylation sites than other proteins in human proteome , suggesting that the functionality of the scaffold proteins might be regulated by phosphorylation process .
We first construct a composite network , which includes 55 , 048 protein-protein interactions ( PPIs ) and 1103 kinase-substrate relationship ( KSR ) in human [3 , 5 , 17] . For a given protein pair , we calculated the shortest distances connecting them in the PPI network ( see Methods ) . A distance of 1 indicates that two proteins directly interact with each other , while a distance of 2 indicates that they do not interact directly with each other , but both interact with a third protein ( Fig 1A ) . Among 1 , 103 protein pairs with known KSRs , 24 . 9% of them have a distance of 2 in the PPI network , suggesting that these signaling proteins are likely to interact with a shared protein mediator . In contrast , of the 6 . 4×107 human protein pairs in the PPI network , only 2 . 7% have a distance of 2 ( Fig 1A ) . The shortest distance analysis suggested that protein mediators might be widespread among signaling proteins in the phosphorylation networks . We next examined the network motifs in the composite network , which represent the basic building blocks in a network [18] . The network motif relevant to scaffold proteins is single-input module ( SIM ) , where a single regulator regulates a set of proteins [19] . Here , the single regulator corresponds to a scaffold protein , while the set of proteins are the protein members in a signaling pathway . In our analysis , a SIM is identified if one protein shows PPIs with a set of proteins and the set of proteins form a linear cascade through KSRs ( Fig 1B ) . We observed that the occurrences of the SIMs are significantly enriched as compared to their expected occurrences in the networks , where the PPIs were randomly permutated ( Fig 1B ) . For example , the SIM motif with a cascade length of 5 occurs 47 times; whereas only 2 times is expected in a randomized network ( Fig 1B ) . Both shortest distance and network motif analyses suggest that a scaffold mediator is likely a widely-used mechanism in phosphorylation signaling cascades . In order to predict potential scaffold proteins in phosphorylation signaling cascades , we searched in the composite networks for proteins that show protein-protein interactions with multiple components in KSR networks ( Fig 2 ) . Note that in this work we do not distinguish scaffold proteins and adaptors , which are smaller proteins binding only two signaling proteins [20] . The scaffold proteins in this work are simply defined as the protein hubs that interact with multiple members in a signaling pathway . A stringent requirement was made in predicting potential scaffold proteins by examining whether a given candidate interacts with all components in a particular pathway . Here , the pathway is defined as a set of proteins with linear KSRs . For example , if Kinase A phosphorylates Kinase B , and Kinase B phosphorylates Protein C , we constructed a pathway of A → B → C . Some proteins might interact with subset of proteins in the pathway , such as proteins A and B ( or proteins B and C ) in the pathway . Continuous sub-paths within a long pathway are also considered as separate pathways ( such as A → B and B → C ) . Note that such defined pathways are not necessary to be the same biological pathway as those defined in other databases ( e . g . , KEGG database ) [21] . To assess the statistical significance for predicting scaffold proteins , simulations were performed by permutation of the PPIs , while keeping the interaction degree ( i . e . , number of interacting partners ) for each protein unchanged . For a protein with a PPI degree of n and a targeted signaling pathway with length of l , we calculated in the permutated networks the chance that a protein with the same PPI degree is predicted as a scaffold protein . Using 1000 random PPI data to calculate the false discovery rate and choosing 0 . 01 as the cutoff of false discovery rate , 212 proteins were predicted as scaffold proteins , which are associated with 605 non-redundant phosphorylation pathways . Among the 1 , 103 known KSRs , 359 of them ( 33% ) are associated with at least one predicted scaffold protein . The resulting network is shown in S1 Fig . The predicted scaffold proteins and their associated pathways are listed in S1 Table . We then examined whether these scaffold proteins are chosen simply because of their high interaction degrees . Based on the PPI degree distribution , we found that the peak of the distribution locates around 10 ( S2 Fig ) . This distribution is similar to that of known scaffold proteins . This result indicates the prediction of scaffold proteins is unlikely to be an artifact due to their high PPI degrees; whereas we did observed that proteins with high PPI degrees have high possibilities to be scaffold proteins ( S3 Fig ) . We collected 78 known scaffold proteins for kinase signaling pathways through literature curation ( S2 Table ) . Our prediction recovered 18 of them , yielding a sensitivity of 23% . In contrast , when 212 proteins were selected randomly among the whole human proteome ( ~24 , 000 proteins ) , it is only expected to recover 0 . 69 known scaffold protein . Therefore , our prediction of scaffold proteins is of > 26-fold enrichment ( p<6 . 9×10−21 , hypergeometric distribution ) . To experimentally evaluate the quality of our prediction , we performed kinase reactions on a human proteome array ( HuProt ) , which contains over 17 , 000 full-length human proteins , in order to comprehensively examine the effects of predicted scaffold proteins [3 , 22] . The kinase assays were performed by incubating each array with a purified kinase in the presence or absence of its predicted scaffold protein ( see methods ) . Two newly predicted scaffold proteins , activating transcription factor 2 ( ATF2 ) and peptidylprolylcis/trans isomerase , NIMA-interacting 1 ( PIN1 ) , were selected for validation . Note that these two scaffold proteins do not contain kinase domain so that they themselves will not directly enhance the phosphorylation activity on the substrates . ATF2 is highly conserved in vertebrates and it is comprised of a C-terminal basic leucine zipper ( bZIP ) domain and an N-terminal GCN4 central activation domain-like acidic activation domain . This protein can specifically bind the CRE DNA motif to activate downstream transcription . As a peptidyl-prolylcis/trans isomerase , PIN1 catalyzes the cis/trans isomerization of peptidyl-prolyl peptide bonds . It specifically binds to phosphorylated pS/TP motifs to catalytically regulate the post-phosphorylation conformation of its substrates and has a profound impact on key proteins involved in the regulation of cell growth , genotoxic and other stress responses , the immune response , germ cell development , neuronal differentiation , to name a few . In our dataset , ATF2 was predicted to act as a scaffold protein for kinases of CKII ( CSNK2A1 ) and MAP kinase JNK2 ( MAPK9 ) , while PIN1 was predicted to act as a potential scaffold for CKII ( S3 Table ) . We tested whether the predicted scaffold proteins will enhance the phosphorylation signals on the substrates presented on the HuProt array . In order to determine the activity of the purified kinases , a standard dot blot assay was first performed for CKII and JNK2 , and both were found to have good activity ( S4 Fig ) . Each HuProt array was incubated with the purified kinase in a standard phosphorylation reaction buffer using 33P-γ-ATP as a labeling reagent in the presence or absence of its candidate scaffold protein . To ensure reproducibility , all the kinase reactions were performed in duplicate . Phosphorylation array images were compared side-by-side and each positive hit was identified with the GenePix software and validated by visual inspection . A scaffold protein-dependent substrate was identified with following criteria . First , a true phosphorylated substrate must have a signal intensity greater than 1 . 5 ( see Methods ) . Second , a positive must be reproducible in the duplicate . Third , a true positive should be found phosphorylated only in the presence of the scaffold protein but not in the absence of the scaffold . Using these criteria , 28 scaffold protein-dependent phosphorylation events were discovered between JNK2 and CKII ( S4 Table ) . For example , JNK2 could only phosphorylate FLJ22639 , CENPB , and MRPL18 in the presence of its predicted scaffold protein ATF2 , suggesting that ATF2 facilitates JNK2 phosphorylation of these substrates ( Fig 3 ) . Interestingly , both PIN1 and ATF2 can act as scaffold proteins for the pathway of CKII →C2orf13 . However , PIN2 and ATF2 can also act specifically on pathways of CKII → C3orf37 and CKII → ZNF554 , respectively . In summary , the successful identification of novel scaffold proteins and scaffold-dependent KSRs strengthens our initial predictions . Of the 605 scaffold-mediated phosphorylation pathways , 408 ( 67% ) are associated with only one scaffold protein , suggesting that the signaling pathways are likely to be specifically regulated by a single scaffold protein ( Fig 4A ) . On the other hand , 61% of scaffold proteins are associated with more than one pathway , suggesting that these scaffold proteins can participate in multiple pathways ( Fig 4B ) . Some partially overlapped pathways are involved in different biological processes and can be regulated by different scaffold proteins . For example , one signaling pathway , namely PLK1→WEE1→CDC2→CDC25C , is associated with a scaffold protein PIN1 . The pathway is partially overlapped with the pathway of CDC2→CSNK2A1→AKT1 , which is associated with scaffold protein Tyrosine-protein phosphatase non-receptor type 1 ( PTPN1 ) . Although CDC2 participates in both pathways , the two scaffold proteins might provide specificity to the signaling pathways and prevent possible undesired crosstalk between pathways . To further our understanding of the biological process that these scaffold proteins might be involved , we examined the gene ontology ( GO ) annotation associated with the predicted scaffold proteins . The GO biological process analysis indicates that 106 of the 212 predicted scaffold proteins are associated with the GO term “signal transduction” ( p<1×10−28 , hypergeometric distribution ) , and that 75 of them are annotated to be related to “intracellular signaling cascade” ( p<1×10−32 , hypergeometric distribution ) , both over three-fold enrichment than expected ( Fig 5A ) . Furthermore , 38 of predicted scaffold proteins are associated with the GO term “regulation of phosphorylation , ” and 36 with “protein kinase cascade . ” To gain molecular insights into how these predicted scaffold proteins might function in signaling cascades , we examined the protein domains encoded by these proteins as defined in Pfam [23] . Compared to the expected occurrence of the corresponding domains , we found several enriched protein domains in these predicted scaffold proteins ( Fig 5B ) . Many of them are known to interact with phosphorylation sites and play a role in signaling cascades , including SH2 , SH3 , and PH , suggesting that many predicted scaffold proteins are directly involved in kinase signaling . For example , SH2 domains are known to interact with phosphorylated tyrosine sites and regulate the signaling pathways [24 , 25] . Interestingly , kinase domains are also enriched , such as Pkinase and Pkinase_Tyr , suggesting that some scaffold proteins are kinase themselves . In fact , 18 . 9% ( 40/212 ) of predicted scaffold proteins are kinases , which is consistent to the previous finding that some kinases can act as scaffold proteins [26 , 27] . However , only 8% ( 40/518 ) of kinases in our dataset were predicted as scaffold proteins . Furthermore , we examined the size of the predicted scaffold proteins . Scaffold proteins are generally large proteins because they need to interact with multiple proteins simultaneously , although some known and predicted scaffold proteins are small proteins as they can form large complexes of polymer , such as ISCU [28] . The comparison between predicted scaffold proteins and the human proteome shows the predicted scaffold proteins are significantly larger than that of background ( average 670 residues for scaffold proteins vs . 200 residues for all human proteins ) ( Fig 5C ) . This property is partially due to the higher interaction degree of scaffold proteins . If we compared the protein sizes between scaffold proteins and the proteins with similar interaction degrees , their protein sizes showed no significant difference ( S5 Fig ) . If scaffold proteins are essential for signaling pathways , it is expected that these proteins should be under evolutionary constraint . By comparing the human protein sequences with their mouse counterparts , we calculated the conservation score for each human protein . On average , the predicted scaffold proteins have a very high conservation score of 0 . 90 , while the average conservation score for all human proteins is 0 . 68 ( Fig 5D ) . In fact , 92% of predicted scaffold proteins have conservation scores larger than 0 . 8 , while we only expect that 47% of human proteins have that level of conservation . Finally , we examined whether the predicted scaffold proteins were co-expressed with the proteins in their associated pathways . Based on the gene expression data across 18 different biological conditions [29] , we calculated the gene expression correlation coefficient between scaffold proteins and all members in the pathways , and found that the correlation coefficients were higher than expected from two randomly selected genes ( S6 Fig ) . In summary , the above analyses of gene ontology , protein domains , protein sizes , evolutionary conservation , and co-expression clearly set apart the predicted scaffold proteins from the rest of the human proteome by showing the characteristics for the functionality of the scaffold proteins . Since some scaffold proteins are known to form dimers [30 , 31] , we systematically examined the connectivity among the predicted scaffold proteins in PPI networks ( Fig 6A ) . Among the 212 scaffold proteins , 72 of them ( 33 . 9% ) have homotypic interactions , suggesting that scaffold proteins tend to form homodimers ( Fig 6B ) . In contrast , only 20 . 7% of proteins ( 2423/11696 ) are found to interact with themselves for all proteins in human PPI network . The enrichment for homotypic interactions among scaffold proteins is statistically significant ( p = 4 . 19×10−6 , hypergeometric distribution ) . Because it is also possible that two different scaffold proteins might form a heterodimer , we next examined the heterotypic interactions among the scaffold proteins . Among the 212 scaffold proteins , we identified total 725 PPIs ( Fig 6A ) . As a control , we randomly selected 212 proteins with PPI degrees similar to the 212 scaffold proteins so that the effect of interactions degree was excluded . The expected number of PPIs among the 212 randomly selected proteins was only 145 , suggesting that the scaffold proteins are also likely to form heterodimers ( Fig 6C ) . Interestingly , if we focused on the scaffold proteins associated with the same signaling pathways , we found these scaffold proteins are more likely to have heterotypic interactions . Indeed , 456 pairs of scaffold proteins share the same pathways . Among them , 118 scaffold protein pairs have direct PPIs . As a control , we randomly selected 456 pairs of protein with similar PPI degrees , and calculated the number of pairs with PPIs . We repeated the simulation 10 , 000 times , and the expected number of pairs with direct PPI among 456 pairs of proteins is only 10 ( Fig 6D ) . Our finding suggests that scaffold proteins might form scaffold protein complexes to regulate signaling pathways . For example , scaffold proteins CBL and SHC1 interact with each other; both of them are found to be associated with pathway of CSK→ LYN→STAT5A , a tyrosine kinase medicated pathway that is involved in regulation of the immune response[32] . Similarly , scaffold proteins GRB2 , CBL and PIK3R1 are likely to form a scaffold complex due to the high interaction degree among them . All three proteins are predicted to be the scaffold proteins of pathway of PDGFRA→SRC→ABL1→CRK ( Fig 6E ) , which is mediated by platelet-derived growth factor receptor and play an important role in organ development and tumor progression[33–35] . Interestingly , in this example , the scaffold protein CBL is associated with two pathways by interacting with different scaffold proteins ( SHC1 vs . GRB2 and PIK3R1 ) , suggesting that formation of scaffold protein complexes is a potential mechanism for multiplexing the function of scaffold proteins . Scaffold proteins are traditionally thought to act as “molecular glue” , bringing different protein components into proximity in a static way . It remains elusive whether and how the activity of scaffold proteins is regulated so that the scaffold proteins and the signaling pathways work in concert to respond to the environmental cue . We hypothesize that scaffold proteins within the phosphorylation network are phosphorylated themselves . To test this hypothesis , we first examined the phosphorylation sites on the predicted scaffold proteins . After collecting 70 , 422 known phosphorylation sites obtained from mass spectrometry experiments [3–5] , we mapped these sites on the proteins . We found that the majority ( 98% ) of predicted scaffold proteins carry at least one known phosphorylation site , and that 79% of them contain at least five known phosphorylation sites . In contrast , only 42% of proteins in the entire human proteome contain any known phosphorylation sites , and only 12 . 4% of them contain at least five known phosphorylation sites ( Fig 7A ) . It is worthy to note that the property is not because of the relatively large size of scaffold proteins . If we compared the number of phosphorylation sites between scaffold proteins and the proteins with similar sizes , the scaffold proteins still have significantly more phosphorylation sites ( Fig 7B ) . Furthermore , if we excluded kinases from the scaffold protein list , the same observation was also made ( S7 Fig ) . Given the possibility that scaffold proteins might be regulated through phosphorylation process , we attempted to identify the possible mechanism in which scaffold proteins and their associated pathways cooperate with each other and respond to the environment . We speculate two possible mechanisms for regulation of scaffold proteins via phosphorylation . First , we expect one kinase member in the signaling pathway phosphorylates the scaffold protein and activates it . We term such cases as intrinsic regulation . In fact , 172 cases were found in which a member in the signaling pathways phosphorylates the associated scaffold proteins , while only 20 cases were expected if we randomly selected proteins from entire human proteome as scaffold proteins ( Fig 7C ) . For example , scaffold protein DAPP was predicted to be associated with pathway of LCK→PLCG2 . Interestingly , kinase LCK in the pathway is known to phosphorylate scaffold protein DAPP [36] ( Fig 7E ) . The second possible mechanism is that a scaffold protein is regulated by a kinase ( s ) that is not a member of the scaffold protein-associated signaling pathway , which we term extrinsic regulation . Since the activity of scaffold proteins and their associated pathways are coordinated , we required that the upstream kinases regulating scaffold proteins also regulate one member in the pathway . In fact , we found 39 such cases while only 10 cases were expected ( Fig 7D ) . For example , kinase FYN is known to phosphorylate scaffold protein CAV1 , which is associated with pathway CSK→SRC→CTNNB1 . In the meantime , FYN is also known to phosphorylate CTNNB1 in the pathway ( Fig 7F ) . In summary , our study marks a promising start in identifying the regulatory mechanism of scaffold-mediated protein phosphorylation and further in vivo studies will determine the functional importance of our observations .
Signal transduction by phosphorylation is the most universal and well-studied mechanism that cells employ to mediate signal transduction , although many kinase-substrate relationships remain to be discovered . Kinase substrates have long been recognized using their known consensus sequence motifs , an amino acid sequence uniquely recognized by a particular kinase . However , with the accumulation of experimental data many kinases break this long-held rule and have been found to share similar phosphorylation motifs with other kinases , although they phosphorylate totally different sets of substrates [3] . This phenomenon has plagued the paradigm and one possible explanation is scaffold proteins . Scaffold proteins can facilitate the kinase-substrate interactions and thus , can be employed to specify and stabilize the weak and transient interactions between the members in a signaling pathway . Therefore , identification of scaffold proteins will help us make a better prediction of kinase-substrate relationships and provide us new insights into the molecular mechanisms of signal transduction . The traditional approaches to identifying the scaffold proteins are often tedious and involved in many steps [15 , 16] . Recently , an analysis of MAPK signaling pathways identified 10 scaffold proteins [37] . This study represents the first attempt at a large-scale projection of potential scaffold proteins . Several lines of evidences suggested the high quality of our predictions . First , comparing with other proteins , the predicted scaffold proteins showed many unique properties that are expected for scaffold proteins . For example , they are likely to contain protein domains that are known to interact with signaling proteins . Second , our protein microarray-based validations have provided a first start in validating this method . Twenty-eight protein substrates could be phosphorylated only in the presence of a predicted scaffold protein . Third , many known scaffold proteins were recovered by our predictions even if we used a very stringent cutoff of FDR = 1% . On the other hand , we are also fully aware of the limitation of our prediction . For example , we currently have only 78 known scaffold proteins . The small number will probably introduce bias in our estimation of sensitivity . Furthermore , the PPI and KSR datasets are incomplete . We believe that we will be able to improve our prediction when the known scaffold proteins , PPI and KSR datasets become more complete and accurate . More important , a better prediction could be made if we could obtain the cell type-specific PPI and KSR data in the future , because the signaling pathways are largely cell type specific . There is one caveat of using existing protein-protein interaction ( PPI ) datasets for our prediction . The PPI datasets we used indeed includes both direct interactions , which were generated from yeast-two-hybrid technique , and indirect interactions , which were generated from affinity purification coupled to mass spectrometry . However , the direct interactions dominate our PPI dataset . By comparing MIPS corum database , which includes 1846 human protein complexes , only 5 . 9% ( 3243/55048 ) PPI interactions are from the human protein complexes . Furthermore , among the 1167 pairs of scaffold proteins and associated signaling proteins , only 88 of them ( 7 . 5% ) belong to the same complexes . Therefore , we do not expect our results would be significantly affected by the inclusion of a small portion of interactions from protein complexes . The scaffold proteins predicted in this study may only reflect a small fraction of the entire set of scaffold proteins , because we used a very stringent requirement for our prediction . In our prediction , only the proteins that interact all members in a pathway were considered as the candidates for the scaffold proteins , while some scaffold proteins might only interact with some members in a signaling pathway . Furthermore , we used 1% of FDR as cutoff , which is very stringent . These factors could partially explain the relatively low sensitivity of our prediction . The systematic identification of scaffold proteins provides us the opportunity to examine the design principle of scaffold-medicated signaling pathways . Although several cases have been discovered that scaffold proteins tend to form homodimers or heterodimer [30 , 31] , our study demonstrates that scaffold complexes are a widespread phenomenon . The statistical significance of our observation indicates that formation of complexes is a general rule of scaffold proteins . More importantly , by interacting with different partners , one scaffold protein could be involved in different signaling pathways . Therefore , formation of scaffold complexes provides a means of encoding multiplexed specificity , generating diversity and exerting additional regulatory controls in the complex signaling networks . Despite the large body of work on scaffold proteins , little is known about whether and how scaffold proteins themselves are regulated . A few studies have showed that scaffold proteins could be regulated through phosphorylation . For example , yeast scaffold protein Ste5 was phosphorylated by Fus3 , which is a member of the Ste5-associated signaling pathways [15] . However , the general regulatory mechanisms for scaffold proteins have not been extensively explored . Inspired by our finding that scaffold proteins contain many phosphorylation sites , we propose two possible mechanisms by which the scaffold proteins themselves are also subjected to phosphorylation regulation . If a scaffold protein is activated by a kinase in its associated pathway , the simultaneous activation of the scaffold protein and the associated pathway could be achieved ( i . e . , intrinsic regulation ) . Coordination can also be achieved when a scaffold protein and at least one member in its associated pathways are regulated by a kinase that is not associated with this pathway , and thereby co-activated in a concerted manner ( i . e . , extrinsic regulation ) . In both mechanisms , phosphorylation of the scaffold proteins serves as a reinforcement to ensure proper signals to be passed downstream . Future studies will further dissect the molecular mechanisms underlying the regulation of scaffold proteins . Nonetheless , our findings suggest that such regulatory mechanism might be a design principle of scaffold-mediated signal transduction .
Human protein-protein interaction ( PPI ) data were collected from five databases: DIP ( Database of Interacting Proteins , http://dip . doe-mbi . ucla . edu ) , MIPS ( Mammalian PPI database , http://mips . gsf . de/proj/ppi/ ) , IntAct ( ftp://ftp . ebi . ac . uk/pub/databases/intact/current/ ) , HPRD ( HPRD_Release_7_09012007 , http://www . hprd . org/ ) and BioGRID ( biogrid-all-2 . 0 . 45 . tab , http://www . thebiogrid . org/downloads . php ) . These data were then formatted and reorganized to remove redundancies . In total , we obtained 55 , 048 human PPIs [17] . 1103 experimentally validated kinase substrate relations were collected from literature and the PhosphoELM database ( phosphor . elm . eu . org ) [3] . The PPI distances of a protein pair is defined as the shortest distance of the protein pair in the PPI network , and can be computed using Breadth-First Search ( BFS ) algorithm [38] . We took each protein with PPI information as a root , and defined it as the first level of a tree . We then extended the root to take all its neighbors as the nodes at the second level of the tree . We next took the neighbors of all nodes at second level as the nodes at the third level of the tree , and all nodes that had appeared in previous levels would be deleted in this level . We repeated this procedure till no further level could be added to the tree . This resulted in the PPI distances between root node and all other nodes in the tree being the difference of their levels . For example , the PPI distance between root node ( first level ) and a node at the fourth level is 3 . This allowed us to obtain the shortest distance of each protein pair . We first extracted all pathways from a KSR network . We took each kinase as root , and extended its substrates using a Depth-First Search ( DFS ) algorithm [39] . Each path starting from a root in the tree represents a possible phosphorylation pathway . Here , we require the path must start from a root node , but does not need to end at a leaf node . The minimum length of a pathway was set as 2 . To speed up the program , only KSR with PPI distance of one or two are considered to build the pathways since KSR with PPI distance larger than two don’t share neighbors in PPI network thus it is impossible for them to have a related scaffold protein . We also included the continuous sub-pathways of long pathways because the longer pathways may not have corresponding scaffold proteins , while its continuous substrings do . By doing it this way , we can list all possible pathways and remove any redundancies . For each possible pathway , we checked whether all protein in the pathway had a common interacting partner in PPI network . If so , the common interacting partner is predicted as candidate scaffold protein related to that pathway . FDR is a statistical method to control the false positive rate in predicted result , which is especially useful in multiple-hypothesis testing to correct for multiple comparisons [40] . In practice , FDR can be defined as the expected false positive rate . Supposing there are n independent tests , each test contains mi predicted results with FDR qi ( qi ≤q* for i = 1 , … , n ) , then the integral FDR q satisfies the following formula , q=∑i=1nmi×qi∑i=1nmi≤∑i=1nmi×q*∑i=1nmi=q*×∑i=1nmi∑i=1nmi=q* In our case , each candidate scaffold protein corresponds to one independent test , thus we can control the integral FDR by controlling the FDR of each individual scaffold protein . Suppose l is the pathway length cutoff and SP is a scaffold protein , SP corresponds to N pathways with length ≧l based on real PPI data , and corresponds to M pathways with length ≧l based on random PPI data , then the FDR of SP as well as its related pathways under pathway length cutoff l can be estimated as M/N . The random PPI data was produced by the random shuffle of real human PPI data . We randomly selected two human PPI pairs , such as A-B and C-D , and then exchange their partners to create two new pairs , A-D and B-C . These two pairs will replace A-B and C-D if both of them are not included in real human PPI data . We repeated this procedure as many times as that of the total number of PPI pairs to create the random PPI data and each pair has been shuffled about two times on average . The final random PPI data also contain exactly 55 , 048 PPI pairs . This kind of shuffle breaks the biological relationship between a protein and its PPI partners , but does not change its PPI degree or the number of its PPI partners , thus keeps its major characters of statistics . Based on the shuffled random PPI data , we can compute the pathways related to a candidate scaffold protein . For accuracy , we created 1000 random PPI data and use them to calculate the average length cutoff under false discovery rate of 0 . 01 . We use “scaffold protein” as keyword to search papers in google scholar and pubmed to find all papers containing this keyword . We then manually collected the known scaffold proteins ( S2 Table ) . Proteins for the microarrays were purified , printed , and analyzed as described previously ( Jeong et al , 2012 ) . Kinases and scaffolds ORFs were expressed as GST-fusion proteins in yeast . Cultures ( 50 mL ) were grown at 30°C to OD600 1 . 0–1 . 2 and induced with 2% galactose for 4–6 hours . Harvested cells were lysed with glass beads in lysis buffer ( 100 mMTris-HCl [pH 7 . 4] , 100 mMNaCl , 1 mM EGTA , 0 . 1% 2-mercaptoethanol , 0 . 5 mM PMSF , 0 . 1% Triton X-100 , protease inhibitor cocktail [Roche] , and phosphatase inhibitor cocktails 2 and 3 [Sigma] ) . GST-proteins were bound to glutathione beads ( GE healthcare ) for 40 minutes at 4°C and washed 3 times with Wash Buffer I ( 50 mMTris-HCl [pH 7 . 4] , 500 mMNaCl , 1 mM EGTA , 10% glycerol , 0 . 1% Triton X-100 , 0 . 1% 2-mercaptoethanol , and 0 . 5 mM PMSF ) and 3 times with Wash Buffer II ( 50 mM HEPES [pH 7 . 4] , 100 mMNaCl , 1 mM EGTA , 10% glycerol , 0 . 1% 2-mercaptoethanol , and 0 . 5 mM PMSF ) before 2 30 minute elutions in elution buffer ( 100 mMTris-HCl [pH 8 . 0] , 100 mMNaCl , 10 mM MgCl2 , 30 mM glutathione , and 20% glycerol ) . Eluate was collected and concentrations were determined through BSA standard . Purified kinase activity was assessed using a simple dot blot assay by incubating each kinase with a generic substrate mix in the presence of 32P-γ-ATP . 2 μL purified kinases were mixed with 1 μL substrate mix ( 1:1:1 casein:MBP:Histone H3 100 ng/μL dissolved in TBS ) and 2μL 2 . 5x reaction buffer ( 90 mMTris-HCl , pH 7 . 5 , 180 mMNaCl , 9 mM MgCl2 , 0 . 9 mM MnCl2 , 0 . 9 mM DTT , 9μM cold ATP , 2 . 5 mM EGTA , 20 mM HEPES-KOH , pH 7 . 5 , 0 . 9 mMNaF , 0 . 9 mM Na3VO4 , and 5 . 954E-05 mM32P-γ-ATP [Perkin Elmer; 0 . 2 μL/5 . 6μL reaction mix] ) and incubated at 30°C for 30 minutes . Reactions were quenched by spotting entire mix onto nitrocellulose paper and drying for 15 minutes . Membrane was then washed 3 times for 10 minutes with PBS and dried again for 15 minutes . Blots were exposed to film overnight . In order to assess whether predicted scaffolds could alter kinase substrate specificity , protein microarray assays were performed . In these assays , the protein microarrays were treated with purified , active kinase both in the absence and presence of predicted scaffold . Microarrays were briefly dipped in TBS to remove excess glycerol from printing procedure before blocking in 3 mL of blocking buffer ( 3% BSA in TBST ) for 1 hour . Arrays were washed 3 times in TBST before the addition of 125 uL of kinase buffer containing 3:1 scaffold:kinase in kinase buffer ( 50 mMTris-HCl [pH 7 . 5] , 100 mMNaCl , 10mM MgCl2 , 1 mM MnCl2 , 1 mM DTT , 1 mM EGTA , 25 mM HEPES-KOH [pH 7 . 5] , 1 mM NaVO4 , 1 mMNaF , 0 . 1% NP-40 , 0 . 0000556 mM33P-γ-ATP [Perkin Elmer; 2 μL/array] ) . Arrays were placed in a humidity chamber and incubated for 30 minutes at 30°C . Following the reaction , arrays were quickly immersed in two separate beakers of TBST and washed 3 times in TBST for 10 minutes followed by 3 washes in 0 . 5% SDS for 10 minutes . Arrays were then quickly dunked in water heated to 37°C and dried by centrifugation before being arranged in a standard film cassette and exposed to film ( Kodak BioMax MR ) for 30 days at -80°C . After 30 days , the film was developed and scanned before analysis with GenePix software .
|
Despite their importance in the signaling transduction , there is no systematic effort in identifying and characterizing the scaffold proteins in humans . In this work , we predicted scaffold proteins by integrating the available protein-protein interactions and kinase-substrate relationships . The predicted scaffold proteins showed characteristics for known scaffold proteins , suggesting the fidelity of our prediction . More importantly , the systematic prediction of scaffold proteins provides biological insights in the scaffold-mediated signal transduction . We found that scaffold proteins are likely to form complexes , suggesting that scaffold proteins could participate in diverse signaling pathways through the combinatorial interactions among scaffold proteins . Furthermore , the regulation of scaffold proteins’ activities has not been extensively studied . Our bioinformatics analysis proposed that scaffold proteins themselves might be regulated through phosphorylation process .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Systematic Prediction of Scaffold Proteins Reveals New Design Principles in Scaffold-Mediated Signal Transduction
|
We retrospectively calculated the prevalence and epidemiologic characteristics of Chagas infection in the New York blood donor population over three years utilizing the New York Blood Center's database of the New York metropolitan area donor population . Seventy Trypanosoma cruzi positive donors were identified from among 876 , 614 donors over a 3-year period , giving an adjusted prevalence of 0 . 0083% , with 0 . 0080% in 2007 , 0 . 0073% in 2008 , and 0 . 0097% in 2009 . When filtered only for self-described “Hispanic/Latino” donors , there were 52 Chagas positive donors in that 3-year period ( among 105 , 122 self-described Hispanic donors ) with an adjusted prevalence of 0 . 052% , with 0 . 055% in 2007 , 0 . 047% in 2008 , and 0 . 053% in 2009 . In conclusion , we found a persistent population of patients with Chagas infection in the New York metropolitan area donor population . There was geographic localization of cases which aligned with Latin American immigration clusters .
Chagas Disease is a common and economically devastating disease of Latin America , with an estimated infected population of over 7 million and over 100 million at risk [1] . Despite the significant number of immigrants from Chagas-endemic regions , prevalence data outside of its countries of origin remains limited [2]–[5] . Estimates of prevalence in non native areas are challenging given the asymptomatic nature of chronic Chagas Disease , the lack of familiarity of local physicians with this imported disease [6] , and the often undocumented immigration status of some infected patients . As a result , no large scale seroprevalence studies of immigrant populations have been done . Instead , many studies have followed a model first seen in Chagas endemic populations where the seroprevalence of Chagas infection in blood donors was used as proxy for overall population prevalence . However , donor seroprevalence of Chagas infection has been reported only from a limited set of populations , and epidemiologic associations of the donors are often lacking . We therefore retrospectively calculated Chagas infection seroprevalence and individual epidemiologic characteristics of infected patients in the greater New York blood donor population .
The study was approved by the Institutional Review Board of both the New York Blood Center and Weill Cornell Medical Center . All data were analyzed anonymously . The New York Blood Center's database of the New York metropolitan area donor population was used to calculate the prevalence of Chagas infection in the general donor population . Chagas positivity was defined as a positive enzyme-linked immunosorbent assay ( ELISA ) screen ( using the T . cruzi test system from Ortho Clinical Diagnostics in Raritan , NJ ) with subsequent radioimmunoprecipitation assay ( RIPA ) confirmation ( from Quest Diagnostics in Madison , NJ ) . The data set covered April 2007 to March 2010 . Screening started in April 2007 so 2007 was adjusted to match the March to March 12 month period of other years by assuming the average monthly number of Chagas positive cases in 2007 continued for one more month . Collected variables included Sex , Racial/Ethnic Background , and Home Zip code , which were originally collected on the Blood Center's standard intake questionnaire given to individual donors .
Seventy Trypanosoma cruzi positive unique donors were identified from among 876 , 614 donors over a 3 year period , giving an adjusted prevalence of 0 . 0083% , with 0 . 0080% in 2007 , 0 . 0073% in 2008 , and 0 . 0097% in 2009 . When filtered only for self-described “Hispanic/Latino” donors , there were 52 Chagas positive donors in that 3 year period ( from a sample of 105 , 122 self-described Hispanic donors ) with an adjusted prevalence of 0 . 052% , with 0 . 055% in 2007 , 0 . 047% in 2008 , and 0 . 053% in 2009 . The remaining 18 Chagas positive donors described themselves as either “Black” ( 1 ) or selected no Racial/Ethnic Background ( 17 ) . Age range was 17 to 76 ( median 43 ) and there were slightly more Females ( 54% ) . When mapped by zip code , the Chagas positive donor contact addresses showed a geographical concentration in one New York metropolitan area , with one notable city in that area seeing a cluster of Chagas positivity . Figure 1 shows one such concentration in Eastern Long Island , mapped on to 2000 Census data .
We found a persistent and possibly increasing population of patients with Chagas infection in the New York City Blood Donor population . Intriguingly , Chagas positivity appears to cluster in a limited set of geographic locations of that population . This study expands what was previously known about Chagas prevalence outside its endemic regions , particularly in the United States . Previous studies have described the prevalence of Chagas infection in the donor population of Spain ( 0 . 62% ) [4] , Mexico ( 0 . 75% ) [7] , citing two examples , but the only detailed published U . S . data is from a sample set from 1994–1998 , showing a 0 . 19% prevalence in Los Angeles and 0 . 08% in Miami [5] . The CDC has published more recent data in 2007 but with no detailed description of donor characteristics [8] . We also found geographic clustering of the donor population in areas with high Foreign Born Hispanic immigrant populations . For example , Eastern Long Island is unique in its large ( 50 k+ ) population of native Salvadorans [9] , which may be mirrored by the geographic clustering of the positive donors in that area ( please see Figure 1 ) . Future efforts at identification of Trypanosoma cruzi infected populations may benefit from this donor-population derived “map” of areas of probable increased population Chagas prevalence . This has already been seen in Europe , where two studies , one In Spain and other in Switzerland , targeted high risk immigrant populations with direct screening ( not during blood donation ) and found a much higher seroprevalence than previously expected . They both confirmed , for example , that the Bolivian immigrant population is at particularly high risk for Chagas infection and merits focused outreach [10]–[11] . Additionally , while neither study looked at the economics of such screening , other studies indicate that even broader screening may make economic sense [12] . This study has several limitations . The prevalence in the Hispanic/Latino group may be underestimated due to lack of race self-identification among many donors , as 24% of Chagas positive donors did not indicate race and therefore could not be included in the “Hispanic/Latino” only results despite most studies indicating there are very few non-Hispanics with Chagas . Thus , the Hispanic/Latino prevalence could be as high as 0 . 067% over all three years if all the Chagas positive patients were in fact Latino . In addition , the donor's country of origin was not included in the questionnaire , and the Hispanic/Latino population in the study database was not segregated by place of birth . The Hispanic/Latino population in New York City includes Dominicans and Puerto Ricans ( the largest Foreign born and the largest non Foreign born Hispanic groups in New York City , respectively [9] ) , groups not at high risk of Chagas positivity . Otherwise our data may have better mirrored the overall trend of increasing positivity , as seen in earlier , larger studies [4] . This increase would be consistent with the rise in immigration in the last decade of particular populations ( i . e . rural Mexicans ) with known higher Chagas positivity [13] . Also of note , blood donor populations do not necessarily mirror society as a whole [14] . However , this has been an accepted practice even in areas of highest Chagas seroprevalence given the difficulty of getting blood samples for the population most likely to be exposed to T . cruzi [15] . Finally it is important to note that no clinical follow up was available ( Blood Center protocol is limited to referring them to an infectious disease physician ) , and thus we were unable to ascertain if any of the seropositive Donors were symptomatic . These results indicate further analysis and outreach is warranted . Chagas Disease is an infection with both asymptomatic latency and debilitating sequelae in a substantial minority of infected patients . Identification , monitoring , and possible treatment of infected persons are best done through targeted identification and testing of at risk population groups . Diagnosis of Chagas infection in blood donors captures only a segment of the population infected with imported Chagas Disease . Characterization of high prevalence communities through blood donor seroprevalence suggests that follow up larger scale community-focused screenings of foreign-born populations could be both lifesaving and cost effective .
|
Chagas Disease is a common and economically devastating disease of Latin America , with millions infected and many more at risk of infection . The hallmark of Chagas Disease is a long asymptomatic latent period ( after an often tiny bug bite ) followed by potentially fatal cardiac or gastrointestinal sequelae . Despite the significant number of immigrants from Chagas-endemic regions , prevalence data outside of its countries of origin remains limited . Our study looks at Trypanosoma cruzi infection in one group , blood donors in the New York metropolitan area , as this was a non invasive way to sample a sometimes difficult-to-reach population . We found that Chagas infection is in fact present , particularly in the Hispanic donors , at a consistent level over the three years we studied . We then compared the blood donor locations to a map of foreign born Hispanics in eastern Long Island in New York and found overlapping concentrations . This may mean that there is an opportunity for large scale community-focused screenings of foreign-born populations that could be both lifesaving and cost effective .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"disease",
"mapping",
"infectious",
"diseases",
"public",
"health",
"and",
"epidemiology",
"chagas",
"disease",
"epidemiology",
"infectious",
"disease",
"epidemiology",
"neglected",
"tropical",
"diseases",
"infectious",
"disease",
"control",
"transfusion",
"medicine",
"parasitic",
"diseases",
"hematology"
] |
2012
|
Seroprevalence of Chagas Infection in the Donor Population
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Pneumococcal carriage is both immunising and a pre-requisite for mucosal and systemic disease . Murine models of pneumococcal colonisation show that IL-17A-secreting CD4+ T-cells ( Th-17 cells ) are essential for clearance of pneumococci from the nasopharynx . Pneumococcal-responding IL-17A-secreting CD4+ T-cells have not been described in the adult human lung and it is unknown whether they can be elicited by carriage and protect the lung from pneumococcal infection . We investigated the direct effect of experimental human pneumococcal nasal carriage ( EHPC ) on the frequency and phenotype of cognate CD4+ T-cells in broncho-alveolar lavage and blood using multi-parameter flow cytometry . We then examined whether they could augment ex vivo alveolar macrophage killing of pneumococci using an in vitro assay . We showed that human pneumococcal carriage leads to a 17 . 4-fold ( p = 0 . 007 ) and 8-fold ( p = 0 . 003 ) increase in the frequency of cognate IL-17A+ CD4+ T-cells in BAL and blood , respectively . The phenotype with the largest proportion were TNF+/IL-17A+ co-producing CD4+ memory T-cells ( p<0 . 01 ) ; IFNγ+ CD4+ memory T-cells were not significantly increased following carriage . Pneumococci could stimulate large amounts of IL-17A protein from BAL cells in the absence of carriage but in the presence of cognate CD4+ memory T-cells , IL-17A protein levels were increased by a further 50% . Further to this we then show that alveolar macrophages , which express IL-17A receptors A and C , showed enhanced killing of opsonised pneumococci when stimulated with rhIL-17A ( p = 0 . 013 ) . Killing negatively correlated with RC ( r = −0 . 9 , p = 0 . 017 ) but not RA expression . We conclude that human pneumococcal carriage can increase the proportion of lung IL-17A-secreting CD4+ memory T-cells that may enhance innate cellular immunity against pathogenic challenge . These pathways may be utilised to enhance vaccine efficacy to protect the lung against pneumonia .
Nasopharyngeal colonisation with Streptococcus pneumoniae ( the pneumococcus ) peaks in prevalence at 2–3 years of age [1] and declines thereafter becoming 10% or less in adult-hood and undetectable in the elderly [2] . Perturbations in host defence and/or increased pneumococcal pathogenicity facilitate colonisation and increase the frequency of progression to mucosal diseases such as pneumonia [3] . Pneumonia is the leading cause of hospitalisation of children in the USA [4] . Elderly populations are also highly susceptible to pneumonia [5] . Pneumococcal carriage is critical in transmission and disease but paradoxically it is also essential for the development of adaptive immunity . Pneumococcal nasopharyngeal colonisation leads to the establishment of antigen specific memory CD4+ T-cells [6] , [7] and specific antibody [8] , [9] at systemic and mucosal sites in mice . It is well established in mice that , in concert with specific antibody and innate immunity , pneumococcal-responding interleukin-17+ ( IL-17A+ ) and not interferon-gamma+ ( IFNγ+ ) CD4+ T-cells ( Th-17 cells ) are essential for protection against pneumococcal carriage [6] , [7] but their role in the lung is less clear . Pneumococcal lung infection in mice leads to the significant recruitment of CD4+ T-cells into the lungs [3] , [7] , [10] , [11] . T cells are associated with protection from pneumococcal pneumonia in some models [3] but not others [8] , [12] possibly owing to variation in host genetic background and the murine bacterial challenge model used . In humans , increased rates of pneumococcal carriage in children [13] and clinical cases of pneumonia in adults [14] were associated with a reduction in circulating Th-1 ( IFNγ+ ) CD4+ T-cells . Polymorphisms in the IL-17A gene are associated with increased pneumococcal colonisation [15] and lung infection [16] . IL-17A and IFNγ can be detected in pneumococcal stimulated blood samples [17]–[19] and tonsillar mononuclear cells [20] . T cells with a Th-1 [21] and Th-17 [22] phenotype have been described in the human airway but their specificity for pneumococcus has not been shown and it is unknown whether they are directly elicited by pneumococcal carriage . Many functions are attributed to IL-17A secreted from Th-17 cells [23] . It can enhance neutrophil recruitment and phagocytosis [18] , increase antimicrobial peptide ( beta defensin 2 ) production [24] , iBALT formation [25] , and enhance polymeric Immunoglobulin receptor expression on mucosal airway epithelial cells [26] . Human Th-17 cells persist for longer and are more resistant to apoptosis compared to Th-1 cells [27] , making their increase an attractive goal for vaccinations relying on cellular immunity . Nasopharyngeal pneumococcal carriage mediated alterations in the frequency and phenotype of pneumococcal-responding T-cell response ( s ) in the lung that could impact vaccination strategies to prevent acute lower respiratory tract infections or therapies designed to augment/modulate lung immunity . We have developed an Experimental Human Pneumococcal Carriage ( EHPC ) model to determine whether carriage could enhance cellular immunity to pneumococcus in the lung . We showed that carriage significantly increased lung and blood IL-17A+ CD4+ T-cell responses . Furthermore , rhIL-17A , dependent upon IL-17 receptor expression , can augment alveolar macrophage killing of pneumococci , to increase innate mucosal defences of the lung .
Written informed consent was obtained from healthy adult volunteers to participate in an approved study at the Royal Liverpool and Broadgreen University Hospitals Trust . Approval was obtained from Liverpool Central [REC 11/NW/0011] and Sefton [08/H1001/52] ) NHS Research Ethics Committees . This work built on other EHPC development studies [28] . In contrast to our previous pneumococcal challenge study design [28] we omitted pre-challenge bronchoscopy with lavage from these cohorts to increase our colonisation success rate . Pneumococcal inoculation was done as published on-line [29] . Briefly , volunteers ( cohort details in Table 1 ) were challenged with a single intra-nasal dose of either 23F ( P833 strain a gift from Prof . JN Weiser , University of Pennsylvania ) or 6B ( BHN418 strain a gift from Prof . PW Hermans , University of Nijmegen ) grown in vegitone broth ( Oxoid ) . The inoculation was performed while the volunteer was seated comfortably in a semi-recumbent position . The head was tilted back slightly and 100 µl of the bacterial inoculum was dispensed , using a Gilson pipette ( P100 ) , across the nasal mucosa . Serial dilutions of the inoculum were plated onto blood agar ( Oxoid ) both before and after inoculation to confirm the dose ( Table 1 ) . Intra-nasal colonisation was assessed in nasal washes collected 48 hours , 7 and 14 days later . Sterile isotonic saline ( 5 mls ) was instilled into each naris with the subject seated at 45° to the horizontal . Saline was held in the nasopharynx for 5 seconds , following which the subjects were asked to tip their head gently forward to allow the saline to run out of the nose and be collected into specimen pots . Collected , pooled nasal washes were centrifuged at 3345 g for 10 minutes and the pellet was resuspended in 100 µl of Skim milk tryptone glucose glycerol ( STGG ) medium . An aliquot ( 25 µl ) was plated onto Columbia horse blood agar ( Oxoid ) containing gentamicin ( Sigma ) and incubated at 37°C , 5% CO2 . After 24 hours plates were inspected for the presence of draughtsman-like pneumococcal colonies . Isolated colonies were subsequently sub-cultured to confirm pneumococcal phenotype using Optochin sensitivity , bile solubility tests and for serotype confirmation , latex agglutination kits ( Statens Serum Institute ) were used . A further aliquot was used to perform a serial dilution ( Miles and Misra ) and 3×10 µl drops per dilution were dropped onto blood agar for colony counting to determine the carriage density ( Table 1 ) . Carriage density was calculated by obtaining the average CFU per 100 µl ( of STGG ) and dividing this value by the volume ( ml ) of nasal wash recovered to obtain CFU/ml of nasal wash . Volunteers with a pneumococcal positive nasal wash that was of the same serotype as the original inoculum were defined as having established carriage . These volunteers were subsequently selected for blood and BAL collection and subject data is summarised in Table 1 . We also recruited 9 age-matched healthy adults ( without pneumococcal colonisation ) to act as controls and obtained BAL and blood samples ( Table 1 and presented in [28] ) for comparison in a cross-sectional study . PBMCs were processed by standard methods [28] . Briefly , PBMCs were seeded in 48-well tissue culture plates in RPMI 1640 media with 2 mM L-glutamine ( both Sigma-Aldrich ) and 10% human AB serum ( complete media ) lot 655272 ( Invitrogen , UK ) , prior to stimulation . BAL was obtained and processed as previously described [28] . BAL cells were plated out into standard 24-well tissue culture plates ( Greiner , UK ) to allow macrophages to adhere for 3 hours at 37°C , 5% CO2 . BAL cells were also allowed to adhere to 96-well tissue culture plates ( Greiner , UK ) for an opsono-phagocytic assay , described below . Non-adherent cells were collected from 24-well tissue culture plates , washed and the pellet re-suspended in 1 ml of complete media in 48-well plates ( Greiner , UK ) and incubated at 37°C , 5% CO2 . For Intracellular Cytokine Staining ( ICS ) , PBMCs or BAL cells ( containing 1–2×105 lymphocytes per well ) were stimulated ex vivo for 2 hours with influenza or one of the following pneumococcal antigen preparations: 1 . 0 µg/ml heat-killed 6B cells ( HK-6B ) , 13 µg/ml ( of which 4 . 2 µg/ml is pneumococcal protein ) 6B culture supernatant ( 6B c/s ) , 13 µg/ml vegitone broth ( ‘vehicle’ ) , 0 . 45 µg/ml of heat-inactivated influenza ( Split Virion , Sanofi Pasteur , 2010/11 strains ) or left untreated ( ‘NS’ ) [28] . After 2 hours , 1 µl of Brefeldin A ( BD biosciences ) was added and cells incubated for a further 16 hours before harvesting and staining for the presence of intracellular cytokines . An equal number of BAL cells were also seeded in parallel and at an equal density to that described for ICS . These cells were stimulated for 20 hours with HK-6B , or left untreated as described above . Cells were harvested , pelleted and the supernatant removed and kept at −80°C for cytokine/protein measurements . There were no significant differences in the total number of cells , macrophages ( mean [±SD] 8 . 5±5 . 5×105 vs 8 . 4±7 . 5×105 ) or lymphocytes ( 1 . 8±0 . 25×105 vs 2 . 0±0 . 09×105 ) per well between non-colonised and colonised groups , respectively . Cells were harvested , stained and analysed as previously described [28] . We gated on viable , CD3+CD4+CD45RO+ T-cells ( hereafter described as CD4+ memory T-cells ) and identified individual TNF , IL-17A and/or IFNγ producing cells ( or combinations thereof ) following stimulation ( Figure S1 in the online repository ) . Alveolar macrophage expression of IL-17RA and RC was determined as described elsewhere [30] . BAL cell culture supernatants ( not treated with Brefeldin A ) were analysed using a Th-1 , Th-2 , Th-17 Cytometric Bead Array ( CBA ) kit ( Becton Dickinson , UK ) . IL-22 and Beta-defensin 2 ( BD2 ) were measured , in duplicate , using an anti-human IL-22 ELISA ( R and D Systems , UK ) or an anti-human BD2 ELISA ( Antigenix America Inc , USA ) , respectively . For the CBA , bead populations were acquired on a BD LSR 2 and fcs files analysed against the standard curve using FCAP version 1 . 0 . 1 ( Soft Flow Inc . USA ) . For ELISAs optical density was measured at 450 nm using a Fluostar microplate reader ( BMG Labtech , Germany ) and corrected for background at 540 nm ( IL-22 ) or corrected using empty wells ( BD2 ) . Standard curves were generated using linear regression fit ( IL-22 ) or 4-parameter fit logistic regression ( CBA and BD2 ) and had an r2 value greater than 0 . 97 . An OPKA using pneumococci and human alveolar macrophages was performed with minor modifications [31] . Briefly , D39 Pneumococci ( serotype 2 ) were opsonised in a 1∶16 dilution of intravenous immunoglobulin ( IVIG , Gamunex , Talecris , USA ) in Hanks and incubated at 37°C for 15 mins on a rotating platform . Opsonised D39 ( 20 µl ) , complement ( 10 µl ) and either 20 µl of rhIL-17A ( rhIL-17A , Biolegend 570502 , reconstituted as described below ) or vehicle control ( HBSS [with Ca2+ Mg2+] containing 10% AB serum [lot 655272] ) were added to 1×105 adhered alveolar macrophages ( multiplicity of infection of 1 pneumococcus :100 cells ) in 30 µl of RPMI+10% FCS to give a total reaction volume of 80 µl in a 96-well flat bottom plate ( Greiner , UK ) . Following 2 hours incubation at 37°C , 10 µl of reaction mixture was tilt plated , in triplicate , onto blood agar ( Oxoid ) and incubated at 37°C , 5%CO2 overnight . Colony forming units ( CFUs ) from cell supernatants were counted the following day . Data with a normal distribution ( tested by Shapiro Wilks ) were compared with parametric tests . Data not following a normal distribution were compared with non-parametric tests . OPKA counts were assumed to follow a Poisson distribution . Changes in CFUs over the three rhIL-17A doses were examined using Poisson regression , with the corresponding vehicle counts included as covariates and with adjustment for clustering within participants . Flow cytometric data were analysed using FlowJo software version 7 . 6 ( Treestar Oregon , USA ) . Graph and statistical analysis was performed using GraphPad prism version 5 . 0 ( California , USA ) . Differences were considered significant if p≤0 . 05 .
We recruited and inoculated 54 healthy young adult volunteers in a dose ranging study , with serotype 6B pneumococcus in which 20 volunteers established carriage ( 37% ) . In a 23F dose ranging cohort , 19 healthy adult volunteers were recruited and 2 established carriage ( carriage positive 11% ) . In the 22 volunteers in whom we established carriage , 17 reported no symptoms , 4 reported mild upper respiratory flu-like symptoms and 1 reported abdominal pain and shortness of breath that resolved without therapy . From the cohort of 22 volunteers with experimentally induced carriage , we were able to recruit 12 volunteers ( average age 22 . 5 years ) 36 days later ( range 21–56 days ) for BAL and blood sampling ( 6B n = 10; 23F n = 2 , Table 1 ) . These 12 carriage positive volunteers had been challenged with a mean dose of 66 , 617±45 , 637 CFUs ( range 11 , 166–136 , 667 CFUs ) per naris ( Table 1 ) . The proportion CD4+ memory T-cells positive for TNF , IL-17A or IFNγ were measured in BAL and blood and compared to controls without challenge ( Table 1 ) . BAL cells and PBMCs from carriage negative volunteers were stimulated with pneumococcal antigens ( HK-6B or 6B c/s ) or influenza and cytokine ( TNF , IL-17A or IFNγ ) producing CD4+ memory T-cells were subsequently detected by ICS and flow cytometry . Pneumococcal-responding CD4+ memory T-cells were identified in BAL ( Figure 1A and C and Figure S1 in the online repository ) and PBMCs ( Figure S2 in the online repository ) in the absence of carriage . BAL CD4+ memory T-cells responding to heat-killed pneumococci ( HK-6B ) in the absence of carriage were TNF+ ( 0 . 25±0 . 19% vs media control 0 . 1±0 . 07% p = 0 . 02 , paired T-test ) and IL-17A+ ( 0 . 12±0 . 09% vs media control 0 . 04±0 . 02% , p = 0 . 01 , paired T-test ) but not IFNγ+ , consistent with a Th-17 phenotype ( Figure 1A ) . There was a positive correlation between TNF+ CD4+ memory T-cells and IL-17A+ CD4+ memory T-cells ( Pearson r = 0 . 8; p = 0 . 009 ) in response to HK-6B . Similar observations were made when cells were stimulated ex vivo with concentrated pneumococcal culture supernatant ( 6B c/s ) and compared to vegetone broth ( vehicle ) alone . We could detect pneumococcal-responding IL-17A+ ( 0 . 12±0 . 07 vs vehicle 0 . 06±0 . 05 p = 0 . 004 ) but not TNF+ or IFNγ+ CD4+ memory T-cells , again consistent with a Th-17 phenotype . In contrast , CD4+ memory T-cells responding to influenza stimulation , in the absence of carriage , were detectable in almost all BAL samples ( Figure 1B and Figure S1 in the online repository ) and these cells were TNF+ ( 0 . 33±0 . 21% vs media control 0 . 1±0 . 06% , p = 0 . 006 , paired T-test ) and IFNγ+ ( 0 . 27±0 . 22% vs media control 0 . 07±0 . 05% , p = 0 . 03 , paired T-test ) CD4+ memory T-cells , consistent with a Th-1 phenotype . IL-17A+ CD4+ memory T-cells were not detected in response to influenza ( Figure 1B ) . To corroborate our flow cytometry findings we stimulated BAL cells from colonised and non-colonised volunteers with HK-6B or left them untreated and measured secreted IL-17A ( Figure 4A ) , TNF ( Figure 4B ) , IL-2 , IL-4 , IL-6 , IL-10 , IL-22 and IFNγ ( all Figure S4 in the online repository ) by ELISA . Large quantities of IL-17A were detected in the culture supernatant of HK-6B stimulated BAL cells from non-colonised volunteers ( stimulated mean±SD 14 , 907±10 , 843 vs non-stimulated 2 , 233±3 , 298 pg/ml , p = 0 . 06 , Figure 4A ) . BAL cells from colonised volunteers and stimulated with HK-6B elicited significantly greater quantities of IL-17A protein compared to non-stimulated cultures ( stimulated 22 , 393±10 , 830 vs non-stimulated 2 , 002±3 , 738 pg/ml , p = 0 . 03 ) . HK-6B stimulated BAL cells from colonised volunteers produced 50% more IL-17A protein than HK-6B stimulated BAL cells from non-colonised volunteers but this difference was not statistically significant . IL-17A production did not correlate with the frequency of pneumococcal-responding IL-17A+ CD4+ T-cells in BAL detected by flow cytometry ( r = 0 . 13 , p = 0 . 68 ) . IL-17A production did correlate with the number of alveolar macrophages per well ( r = 0 . 59 , p = 0 . 03 ) indicative of an alternative source of IL-17A , other than CD4+ memory T-cells , in BAL . Comparisons between colonised and non-colonised groups , following pneumococcal stimulation , revealed no significant differences with the exception of TNF ( 198 . 9±476 . 8 vs non-colonised 5 . 8±3 , p = 0 . 05 Mann-Whitney , Figure 4B ) . When corrected for background ( by subtracting data from non-stimulated cells ) the significance of this observation increased ( TNF 195 . 7±476 . 5 vs non-colonised 2 . 67±2 . 45 , p = 0 . 026 Mann Whitney ) . We then hypothesised that the presence of IL-17A ( and TNF ) in stimulated culture supernatant would in turn elicit alveolar macrophage secretion of constitutively expressed BD2 protein [32] after 20 hrs but this was below the limit of detection in all samples ( data not shown ) . Human alveolar macrophages expressed both IL-17 RA ( 7438±1646 mean channel units , n = 5 ) and RC ( 3551±2426 mean channel units n = 6 ) sub-units consistent with our previous observations [30] . We thus used rhIL-17A and a modified OPKA assay to mimic CD4 T cell action and our hypothesis was that rhIL-17A could enhance the anti-pneumococcal response ( independent of serotype ) of human alveolar macrophages . To calculate a percentage increase or decrease compared to vehicle treated cells , CFU averages from each rhIL-17A dose and respective control were divided to obtain a ratio ( Figure 5 and raw data in Table 3 ) . We showed a dose dependent increase in macrophage uptake of pneumococci using 12 . 5 ng/ml , 125 ng/ml or 625 ng/ml concentrations of rhIL-17A ( Figure 5 and Table 3 ) . Macrophage uptake of pneumococci was increased 26% in the presence of 125 ng/ml of rhIL-17A , compared to the 12 . 5 ng/ml dose ( 12 . 5 ng/ml Control: 14 . 2 vs rhIL-17A stimulated 10 . 5 CFU p = 0 . 013 ) . Increasing the rhIL-17A dose to 625 ng/ml further increased pneumococcal uptake to 37% ( 12 . 5 ng/ml Control: 14 . 2 vs rhIL-17A stimulated 8 . 9 CFU p = 0 . 004 ) ( Figure 5 and Table 3 ) . We correlated the OPKA data described above with IL-17 receptor RA and RC mean fluorescence intensity on BAL alveolar macrophages from a sub-set of the same volunteers ( n = 6 ) to determine whether this response was mediated by the IL-17 receptor . Our hypothesis was that increased mean receptor expression positively correlates with increased percentage killing compared to vehicle at the 125 ng/ml dose . There were no significant correlations between OPKA data and expression of RA or combined expression of RA and RC . Contrary to our hypothesis , however , mean expression of RC ( 3551±2426 ) , negatively correlated with killing ( Spearman r = −0 . 9 , p = 0 . 017 ) .
We have shown that pneumococcal-responding IL-17A+ CD4+ memory T-cells are present at very low frequency in the healthy adult lung in the absence of carriage . Further , using a novel experimental human pneumococcal carriage ( EHPC ) model and post carriage BAL collection an episode of pneumococcal carriage resulted in a 17 . 4-fold increase and 8-fold increase in the percentage of IL-17A+ CD4+ memory T-cells in BAL and blood , respectively , compared to non-colonised volunteers . Using human alveolar macrophages as effectors we showed that rhIL-17A increased in vitro killing of S . pneumoniae in an opsonophagocytic killing assay . These are the first data of which we are aware to describe the relation of nasal carriage of a pathogenic organism and lung IL-17A responses in humans and together support a role for effector IL-17A+ CD4+ memory T-cell responses in the defence of the lung against pneumococcal infection in adults . The two major strengths of this study are that we have described human CD4+ T-cell responses in the relevant mucosal site and after a defined period of nasal colonisation . Other investigators have identified and described pneumococcal-responding human CD4+ T-cells in blood [13] , [19] , [20] , [33] , [34] and upper respiratory tract mucosal tissue [13] , [20] , [35] but the initiation and duration of carriage was unknown . The sharp increase in cellular response seen immediately following an episode of carriage in this study , and not seen in similar volunteers challenged with live bacteria but without carriage [28] , strongly supports a lung immunising role of carriage in adults . Although a study of subjects at high risk of pneumococcal disease ( children , elderly ) would be immunologically more relevant , it would clearly be ethically unacceptable in the context of human pathogen challenge . Our data contrast with decreases in antigen-specific responses observed in blood in pneumococcal carriers in UK children [13] or in endemic areas such as the Gambia [33] and in UK patients with pneumonia [14] , probably due to mucosal sequestration . Our data concur , however , with increased IL-17A responses in other studies [17] from an area with a high prevalence of pneumococcal carriage and disease ( i . e . Bangladesh ) compared to Swedish cohorts . The difference between our study and others may be due to differences in the timing of sample collection relative to exposure . Human pneumococcal-responding IL-17A responses have been demonstrated previously in peripheral blood [17] , [18] , [33] and in adenoidal mono-nuclear cells [20] , [35] but in our study we have examined the mucosal compartment where pneumonia becomes established – the lung , which has not been examined before . In a healthy adult population we showed , using flow cytometry , that pneumococcal carriage elicits high frequencies of IL-17A+ and TNF+ but not IFNγ+ cells , within 5 weeks of colonisation . We have used an extensive 7-colour panel that includes CD3+ and CD4+ antibodies that together with IL-17+ detection ensures that it is highly likely that the responses we have identified in this manuscript are derived from putative Th-17 cells rather than innate sources . The IL-17A dominant responses in BAL and blood contrast with studies that described higher IFNγ and lower IL-17A responses in blood [19] , [33] and tonsil [20] from healthy and HIV affected [19] adults in Malawi and Gambia . There are multiple factors , including the tissue site examined , burden of disease and cellular plasticity [36] that may account for higher IFNγ in these studies and these differences between geographical areas of high and low pneumococcal carriage warrant further attention . It is likely , however , that both IL-17A and IFNγ from T-cell effector cells as well as T-regulatory cell populations play important but different roles in protecting the lung against the pneumococcus and pneunomococcal induced pathology [3] , [37]–[40] . Furthermore , we identified increased IL-22 levels in some volunteers that were independent of the IL-17 response suggesting a separate source of IL-22 ( possibly Th-22 cells ) , the diverse functions of which include maintenance of epithelial integrity [41] and remodelling [42] . Murine models of airway inflammation have shown that IL-22 can be pro-inflammatory ( and thus pathological ) in the presence of IL-17A but in the absence of IL-17A can be anti-inflammatory/tissue protective [43] . Determining the correct “balance” of Th-17 , 22 and T-regulatory cells elicited following vaccination may be important for generating adaptive anti-pneumococcal responses that promote resolution and clearance and reduce immunopathology . We have also measured the cytokine response from lung cells stimulated ex vivo with pneumococcus and shown that pneumococcal stimulated BAL cells ( from non-colonised and colonised volunteers ) produce IL-17A in quantities far greater than described in other studies using blood [17]–[19] or lymphoid tissue [20] , [35] . The response from colonised volunteers was 50% greater compared to the non-colonised group who also had high levels of IL-17A following stimulation that is likely to be derived from non-Th-17 sources . This difference may be of relevance in vivo , however , since TNF [44] , which we have shown to be significantly different between colonised and non-colonised groups using flow cytometry ( Figure 2A ) and ELISA ( Figure 4B ) , and IL-22 [45] can both synergise with IL-17A to enhance epithelial derived CXC chemokine production , important for the recruitment and activation of neutrophils to the airway . It has been shown that murine [46] and human [47] alveolar macrophages can also produce IL-17A utilising a TLR-2 dependent mechanism [46] and this may have contributed to the IL-17A signal detected by our ELISA in both groups . An important role for the alveolar macrophage in the early hours of pneumococcal infection has been highlighted previously in murine models [48] and IL-17A from innate sources are likely to be involved [49] . In this study we showed a significant increase in pneumococcal killing by macrophages when exposed to 125 ng/ml of rhIL-17A , a concentration that is in line with previous publications showing an effect of IL-17 in this dose range [18] , [45] , [50]–[52] . This is also consistent with the study by Lu et al . [18] who showed that human neutrophils exposed to 100 ng/ml of rhIL-17A showed significantly increased pneumococcal killing . Our data are also in line with those of Higgins et al . [53] who showed that treatment of murine peritoneal macrophages with 2 . 5–50 ng/ml of rmIL-17A significantly enhanced killing of Bordetella pertussis . IL-17 also acts as a recruitment and survival factor for monocytes and macrophages [54] , respectively , thus promoting macrophage-Th-17 interaction in the small volume of airway lining fluid [55] . Both RA and RC subunits are required for human IL-17A signalling with combined surface receptor density of RA and RC determining the magnitude of the response [56] but we did not find any positive correlations between our OPKA data and receptor expression . In contrast to our hypothesis , we observed a negative correlation between IL-17RC ( but not RA ) expression and macrophage killing activity at the 125 ng/ml dose . The modulation of killing by RC supports our observations of an IL-17-dependent effect in our assay system rather than a contaminant . Furthermore the negative correlation between IL-17RC ( but not RA ) expression and macrophage killing suggests that killing may be mediated by a different IL-17RA heterodimer other than RA:RC . RC may thus play a regulatory role in this process , separate from its pro-inflammatory role within the RA:RC dimer , fine tuning the phagocytic potential of alveolar macrophages and thus susceptibility to infection . There is evidence that IL-17 receptors play regulatory roles during the inflammatory response [30] , [52] . Recent observations have shown that RD expression intensity can differentially regulate p38 mitogen-activated protein kinase and nuclear factor-kappa B pathways and more importantly the control of lung neutrophil recruitment in a CXCL2 dependent manner [52] . Evidence provided here and elsewhere thus suggests that the role of IL-17 receptors is more complex than initially appreciated and may differ depending on the context . The role of IL-17 receptors , other than the classical IL-17RA:RC heterodimer , on alveolar macrophage function in health and disease remains to be clarified and may determine the overall protective effect of Th-17 cells . Taken as a whole , these results have important implications for vaccine design against pneumonia . First , they show that human nasal carriage can boost innate ( alveolar macrophage function ) and adaptive ( TNF+IL-17A+ CD4+ memory T-cells ) cellular lung immunity that may protect the lung from pneumococcal challenge and the establishment of infection in health , without significant recruitment of neutrophils . When these and other protective immunological mechanisms are compromised or the bacterial load overwhelms innate defence mechanisms the responses described in our study may synergise to enhance neutrophil mediated recruitment into the airspace . Second , we have begun to define the phenotypic and kinetic cellular responses elicited by pneumococcal carriage – a natural immunising event , thus providing a bench mark for vaccines that seek to protect against pneumonia .
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Pneumococcal carriage is an important step in the development of cellular and humoral pneumococcal immunity but paradoxically may lead to mucosal diseases such as pneumonia . The frequency of carriage and pneumonia in young healthy adults is very low despite frequent exposures suggesting the presence of appropriate mucosal defences . Lung mucosal immunity against the pneumococcus is poorly described in humans and lags behind recent advances in our understanding of protective cellular responses in mice . We have therefore developed a method to experimentally induce pneumococcal carriage in healthy adults in order to provide a mechanistic insight into the protective responses elicited at the lung surface . We were able to produce carriage in healthy adults and show that – in the absence of respiratory symptoms or local lung inflammation – pneumococcal-responding ( adaptive ) cellular responses are increased to a large extent . We also provide evidence of cellular cross-talk between lung sentinels and the pneumococcal-responding adaptive response that may help prevent lung infection in humans . Manipulation of this response may provide novel therapeutic approaches to prevent pneumonia . Furthermore these tools allow better interpretation of defective responses in at risk individuals such as the elderly .
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2013
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Experimental Human Pneumococcal Carriage Augments IL-17A-dependent T-cell Defence of the Lung
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Akt phosphorylation is a major driver of cell survival , motility , and proliferation in development and disease , causing increased interest in upstream regulators of Akt like mTOR complex 2 ( mTORC2 ) . We used genetic disruption of Rictor to impair mTORC2 activity in mouse mammary epithelia , which decreased Akt phosphorylation , ductal length , secondary branching , cell motility , and cell survival . These effects were recapitulated with a pharmacological dual inhibitor of mTORC1/mTORC2 , but not upon genetic disruption of mTORC1 function via Raptor deletion . Surprisingly , Akt re-activation was not sufficient to rescue cell survival or invasion , and modestly increased branching of mTORC2-impaired mammary epithelial cells ( MECs ) in culture and in vivo . However , another mTORC2 substrate , protein kinase C ( PKC ) -alpha , fully rescued mTORC2-impaired MEC branching , invasion , and survival , as well as branching morphogenesis in vivo . PKC-alpha-mediated signaling through the small GTPase Rac1 was necessary for mTORC2-dependent mammary epithelial development during puberty , revealing a novel role for Rictor/mTORC2 in MEC survival and motility during branching morphogenesis through a PKC-alpha/Rac1-dependent mechanism .
Post-natal mammary epithelial morphogenesis is a complex process during which an extensively branched ductal network develops from a rudimentary epithelial bud [1] . Branching morphogenesis is most active during puberty and is regulated by endocrine hormones and local paracrine interactions with mesenchymal stroma [2] . In response to hormonal and growth factor cues , mammary epithelial cells ( MECs ) within the terminal end buds ( TEBs ) , the club-shapes structures at the distal epithelial tips [1 , 2] , proliferate and collectively invade surrounding stroma . Differentiation of epithelial progenitors in the TEB populates the ducts with mature luminal MECs , and apoptosis canalizes the lumen . TEB bifurcation results from mechanical restraints at the TEB midline , forming new primary ducts . Side-branches sprout laterally from the trailing ducts as proliferative out-pouchings . Primary and side branching reiterates , filling the entire mammary fat pad [1 , 2] . The dynamic processes that occur during puberty in the mammary epithelium are carefully coordinated by many molecular signaling pathways . The intracellular serine/threonine kinase mammalian target of rapamycin ( mTOR ) regulates cellular metabolism , protein and lipid synthesis , cell survival , and cytoskeletal organization , processes that are required for proper mammary morphogenesis . mTOR regulates these processes through phosphorylation of its target substrates , including translation initiation factor 4E ( eIF4E ) -binding protein 1 ( 4E-BP1 ) , p70S6 kinase ( S6K ) , Akt , SGK1 , and protein kinase C-alpha ( PKC-alpha ) [3] . A complex of associated protein co-factors regulates mTOR substrate specificity . As such , mTOR functions in two distinct complexes , each defined by the specific co-factors in complex with mTOR kinase and by their relative sensitivity to rapamycin . The rapamycin-sensitive mTOR complex ( mTORC ) -1 requires the co-factor regulatory-associated protein of mammalian target of rapamycin ( Raptor ) , whereas mTORC2 requires the co-factor rapamycin-insensitive companion of mammalian target of rapamycin ( Rictor ) . Although mTORC2 is relatively insensitive to acute rapamycin treatment , more recent studies determined that prolonged rapamycin treatment can inhibit mTORC2 complex assembly [4–7] . The intracellular serine/threonine kinase Akt is phosphorylated at S473 directly by mTORC2 and is key effector for many of the biological effects initiated by mTORC2 . Akt is also linked to activation of mTORC1 downstream of PI3-kinase , making Akt a point of intersection between mTORC1 , mTORC2 , and their associated effectors [3] . Though mTOR regulates MEC growth in cell lines [8 , 9] and milk protein expression [8 , 10–12] , mTOR-mediated regulation of mammary ductal morphogenesis remains under-investigated . The signaling complexity of mTOR , its pleiotropic functions , and a lack of mTORC2-specific inhibitors present a challenge to dissecting the relative roles of mTORC1 and mTORC2 in mammary development . Given the importance of mTOR in breast cancer progression and treatment , an understanding of mTORC1 and mTORC2 in untransformed MECs is needed . We assessed the impact of tissue-specific Rictor and Raptor ablation on mammary morphogenesis . Rictor loss impaired mTORC2 activity , reduced ductal lengthening and secondary branching , and reduced MEC proliferation and survival in vivo and ex vivo . Surprisingly , genetic disruption of mTORC1 via Raptor ablation resulted in distinct and milder effects on the developing mammary ductal epithelium , revealing non-overlapping roles for mTORC1 and mTORC2 during mammary morphogenesis . Interestingly , we found that mTORC2 controls mammary morphogenesis through downstream effectors PKC-alpha and Rac1 , but not Akt .
To assess the role of Rictor/mTORC2 during mammary morphogenesis in the context of the native mammary microenvironment , we bred MMTV-Cre mice [13] to RictorFL/FL mice [14] , allowing mammary-specific Cre recombinase to disrupt Rictor expression at floxed ( FL ) Rictor alleles . Immunohistochemistry ( IHC ) for Rictor revealed expression in luminal and myoepithelial MECs in Rictor+/+MMTV-Cre ( RictorWT ) mice ( Fig 1A–upper panel ) . Rictor expression was not seen in RictorFL/FLMMTV-Cre ( RictorMGKO ) luminal MECs , and was slightly reduced in the myoepithelium , consistent with luminal but not myoepithelial Cre expression in MMTV-Cre mice . Akt phosphorylation at S473 , the mTORC2 phosphorylation site , was decreased in MECs of RictorMGKO mice versus RictorWT , confirming decreased mTORC2 signaling upon Rictor ablation ( Fig 1A–lower panel ) . Immunofluorescent ( IF ) staining for cytokeratin ( CK ) -8 and CK14 , molecular markers of luminal and myoepithelial MECs , respectively , confirmed that Rictor loss did not affect the relative spatial organization of luminal and myoepithelial MECs ( Fig 1B–upper panel ) , but revealed the presence of apically mis-localized nuclei in RictorMGKO MECs ( yellow arrows ) , versus basally located nuclei and an organized , smooth apical border in RictorWT samples ( white arrows ) . IF for the tight junction ( TJ ) protein Zona Occludens-1 ( ZO-1 ) revealed apical ZO-1 localization in RictorWT samples . However , ZO-1 was aberrantly localized along baso-lateral membranes in RictorMGKO MECs ( Fig 1B–lower panel ) . In contrast , the baso-lateral localization of the adherens junction ( AJ ) protein p120 was relatively unaltered by Rictor loss . These results suggest that Rictor loss disrupts the proper apical distribution of ZO-1 in MECs . The apically mis-localized nuclei apparent in histological mammary sections from 6-week old RictorMGKO female mice contributed to an irregular apical border ( Fig 1C , black arrows ) . Additional structural alterations were seen in TEBs , including sloughing of body cells ( the multi-layered TEB population comprised of mature and progenitor luminal MECs; Fig 1C–lower panel , arrow ) within TEB lumens , and stromal thickening at the neck between maturing ducts and TEBs ( Fig 1C–lower panel , * ) . Morphological alterations were seen throughout whole mounted , hematoxylin-stained RictorMGKO mammary glands ( Figs 1D , arrows , and S1A ) . Because mammary ducts lengthen distally at a predictable rate during puberty , we measured ductal length in mammary glands from 6 week- ( mid-puberty ) and 10 week-old ( late puberty ) mice . Ductal length was significantly reduced in RictorMGKO mammary glands at both time points ( Fig 1E–left panel , and S1B Fig ) . Primary ( Y-shaped ) and side ( T-shaped ) branches were counted in each mammary gland , revealing a significant reduction in T-shaped side branches at 6 and 10 weeks of age in RictorFL/FLMMTV-Cre samples as compared to RictorWT ( Fig 1E–right panel ) . IHC analysis of Ki67 in both ducts and TEBs was used as a relative measure of cellular proliferation in the mammary epithelium ( Figs 1F—upper panel , and S1C–upper panel ) , revealing decreased Ki67+ nuclei in RictorMGKO samples as compared to RictorWT at 6 weeks of age but not at 10 weeks ( Fig 1G—left panel ) . Cell death in ductal MECs or TEBs , measured using TUNEL analysis ( Figs 1F—lower panel , and S1C–lower panel ) , demonstrated a remarkable increase in TUNEL+ MECs in RictorMGKO samples at 6 and 10 weeks of age ( Fig 1G—right panel ) . These results demonstrate that Rictor loss impairs mTORC2 activity , P-Akt , MEC growth , and MEC survival during mammary morphogenesis . Western analysis of whole mammary lysates harvested from 10-week old female mice confirmed decreased P-Akt S473 in RictorMGKO mammary glands , and revealed increased phosphorylation of the mTORC1 effector ribosomal protein S6 ( [15]; Fig 2A ) confirming that Rictor loss decreases mTORC2 activity , but not mTORC1 . To dissect more precisely how Rictor signaling affects mammary morphogenesis , we used primary mammary epithelial cells ( PMECs ) and primary mammary organoids ( PMO’s ) harvested from RictorFL/FL mice . Adenoviral infection of RictorFL/FL PMECs with Ad . Cre significantly reduced Rictor and P-Akt S473 levels relative to cells infected with control Ad . LacZ , and increased P-S6 levels ( Fig 2B ) , similar to the impact of Rictor ablation in vivo . Consistent with structural alterations were seen in our RictorMGKO model in vivo ( e . g . sloughing of body cells in TEBs , irregular ductal tracts , multiple cell layers ) , confocal analysis of Rictor-deficient PMOs stained for E-cadherin revealed multiple cell layers in acinar structures and poor lumen formation relative to control PMOs infected with Ad . LacZ , which formed a well-defined lumen surrounded by a single layer of epithelial cells ( S1D Fig ) . Rictor loss did not significantly impact PMEC proliferation , as measured by bromodeoxyuridine ( BrdU ) incorporation into genomic DNA ( Fig 2C–left panel ) . However , the percentage of TUNEL+ PMEC nuclei was increased >2-fold following Ad . Cre infection ( Fig 2C–right panel ) , consistent with increased cell death in Rictor-null MECs in vivo . Similar results were seen using MCF10A immortalized human MECs , in which Rictor gene targeting with Rictor-specific zinc finger nucleases ( ZFNs ) genetically impaired Rictor expression and decreased P-Akt S473 ( Fig 2D ) , thus validating our findings in a human MEC model . Increased cell death was also seen in MCF10A-RictorZFN cells as compared to parental MCF10A cells , as shown by AnnexinV-FITC binding ( Fig 2E ) . Therefore , Rictor is necessary for mTORC2 signaling and cell survival in human and mouse MECs . We cultured adenovirus transduced RictorFL/FL mammary organoids in three-dimensional ( 3D ) Matrigel to assess collective epithelial morphogenesis ( Fig 2F ) . Mammary organoids accurately model epithelial autonomous molecular events of mammary morphogenesis in a stroma-free environment that preserves the native relationship between luminal and myoepithelial MECs and permits cell-cell and cell-matrix interactions in three dimensions [16] . GFP fluorescence in organoids infected with Ad . GFP or Ad . Cre-IRES-GFP confirmed efficient infection in basal and luminal cells of organoids ( S2A and S2B Fig ) . IF staining for pan-cytokeratin confirmed that organoids were epithelial-derived ( S2C Fig ) . Ad . Cre infection of RictorFL/FL PMECs substantially reduced organoid size and branching ( Fig 2F and 2G ) and reduced Rictor expression levels ( Fig 2H ) . In contrast , Ad . Cre infection of RictorFL/+ PMECs only modestly reduced Rictor expression levels ( Fig 2H ) and did not significantly affect organoid size or the number of branches formed in RictorFL/+ organoids ( Fig 2F and 2G ) . These data suggest that Rictor is necessary for multicellular morphogenesis of the mammary epithelium , faithfully recapitulating ex vivo the consequences of Rictor ablation that are seen in vivo and demonstrating the utility of this model to examine branching mammary gland morphogenesis . Previous studies demonstrated that Rictor knock-down reduces migration of breast cancer cell lines [17–19] . We therefore assessed PMEC invasion and motility through Matrigel-coated transwell filters upon Rictor ablation ex vivo . Fewer RictorFL/FL PMECs invaded through Matrigel when infected with Ad . Cre , as compared to RictorFL/FL PMECs infected with control Ad . LacZ ( Fig 2I ) . Similarly , invasion through Matrigel-coated transwells was profoundly reduced in MCF10A-RictorZFN cells as compared to parental MCF10A cells ( Fig 2J ) . Under these conditions , there were a similar number of viable cells remaining in the upper transwell chamber after 24 hours of culture of both MCF10A and MCF10A-RictorZFN cells ( S2D Fig ) , suggesting that cell death may not be the primary reason underlying the reduced ability of MCF10A-RictorZFN cells to migrate/invade in these transwell assays , but rather that cell invasion , per se , is decreased in the absence of Rictor . Collectively , these data demonstrate that Rictor promotes MEC invasion and migration , two processes necessary for mammary ductal lengthening and branching . Because Rictor loss reduced P-Akt S473 , we tested the hypothesis that Akt phosphorylation by Rictor-regulated mTOR complex 2 is necessary for survival and morphogenesis of MECs . Adenoviral expression of myristoylated Akt1 ( Ad . Aktmyr ) was used to express a membrane-localized ( and thus , constitutively active ) variant of Akt1 . Indeed , expression of this Akt variant in mammary epithelium delays involution and the onset of apoptosis in vivo [20] . Additionally , we repeated experiments using an alternative adenoviral , constitutively active Akt construct , Ad . AktDD . Ad . Aktmyr or Ad . AktDD restored P-Akt S473 in Ad . Cre-infected RictorFL/FL PMECs ( Figs 3A and S3A ) . Surprisingly , RictorFL/FL organoids infected with Ad . Cre + Ad . Aktmyr or Ad . AktDD were morphologically similar to and harbored little to no statistically significant difference in the numbers of branches compared to those infected with Ad . Cre alone ( Figs 3B and S3B ) . Further , size of Rictor-deficient organoids was not fully rescued by expression of Ad . Aktmyr or Ad . AktDD ( Figs 3C and S3C ) . We found that blockade of Akt using the allosteric Akt inhibitor 5J8 blocked Akt phosphorylation at S473 ( Fig 3D ) , reduced the number of branches per organoids , and reduced organoid size by nearly 50% ( Fig 3E ) . These data suggest that while Akt is necessary for mammary branching and growth , restoring Akt function is not sufficient to completely rescue defects caused by loss of Rictor/mTORC2 function . Indeed , expression of Ad . Aktmyr did not reduce the number of Rictor-null PMECs undergoing cell death ( Fig 3F ) , nor did it increase the number Rictor-null PMECs invading through Matrigel-coated transwells ( Fig 3G ) . Taken together , these observations suggest Rictor is necessary for Akt phosphorylation in MECs , but that Akt is not the primary effector of mTORC2 that regulates MEC survival , invasion , and side branching . Thus , while Akt is necessary for proper mammary epithelial morphogenesis , it is not sufficient to compensate for loss of Rictor/mTORC2 function . Previous studies showed that mTORC2 phosphorylates PKC-alpha [21] Consistent with these findings , Rictor loss reduced P-PKC-alpha in PMECs , as well as total PKC-alpha ( Fig 4A ) . We also observed decreased P-PKC-alpha by IF in mammary gland sections from 6 week old RictorMGKO mice , as compared to RictorWT controls ( S4A Fig ) . Adenoviral PKCα expression rescued P-PKC-alpha in Rictor-null PMECs ( Fig 4B ) , rescued branching morphogenesis in Rictor-null organoids ( Fig 4C ) and increased Rictor-null organoid size ( Fig 4D ) . Similar to what was seen in mouse PMECs , P-PKC-alpha and total PKC-alpha were diminished in MCF10A-RictorZFN cells relative to parental MCF10A ( Fig 4E ) . Restoration of PKC-alpha by adenoviral transduction increased P-PKC-alpha in both parental MCF10A and MCF10A-RictorZFN cells ( Fig 4F ) . Rac1 , a small GTPase involved in actin cytoskeletal dynamics , is necessary for migration of many breast cancer cell lines , regulates apical polarity in MECs , and is a downstream effector of mTORC2 signaling . Importantly , Rac1 is also a known effector of PKC-alpha in MECs [22–24] , but the linear relationship between Rictor , PKC-apha , and Rac1 in MECs is currently unknown . We examined Rac1 activation in MCF10A cells using agarose beads conjugated to recombinant p21-activated kinase binding domain ( PBD ) , which specifically binds to active GTP-bound Rac . Western analysis to detect Rac1 in PBD pull-downs revealed decreased Rac-GTP in MCF10A-RictorZFN cells as compared to parental MCF10A ( Fig 4G ) . However , Ad . PKC-alpha increased Rac-GTP in Rictor-null cells , confirming that PKC-alpha activates Rac downstream of Rictor . Additionally , Ad . PKC-alpha increased invasion of MCF10A-RictorZFN cells through Matrigel-coated transwells ( Fig 4H ) , and significantly reduced apoptosis in MCF10A-RictorZFN , as measured by Annexin V-FITC staining ( Fig 4I ) . A pharmacological PKC-alpha inhibitor profoundly decreased invasion of parental MCF10A cells through Matrigel-coated transwells ( Fig 4J ) , providing validation that PKC-alpha is necessary for MEC motility . These data suggest Rictor-mediated PKC-alpha signaling in MECs controls Rac1 activation , branching morphogenesis , cell survival and motility . To confirm the role of Rictor in Rac1 activation in vivo , we examined mammary epithelium in situ for GTP-bound Rac1 using a glutathione-S-transferase ( GST ) -PBD fusion protein as a probe for Rac-GTP . IF detection of GST-PBD binding was decreased in RictorMGKO mammary glands compared to RictorWT ( Figs 5A , 5B , and S4B ) . Importantly , IF detection of GST-PBD binding in WT PMECs was abolished by a pharmacological Rac1 inhibitor ( Figs 5C and S4C ) , confirming the specificity of the assay for detection of Rac1-GTP . In contrast to the abundant Rac1-GTP detected in WT PMECs , RictorFL/FL PMECs infected with Ad . Cre displayed a 10-fold decrease in GST-PBD binding to Rac-GTP relative to Ad . LacZ infected controls ( Figs 5C and S4C ) . Phalloidin staining revealed cortical actin overlapping with GST-PBD binding in Ad . LacZ-infected RictorFL/FL PMECs ( S4D Fig ) . However , Ad . Cre-infected RictorFL/FL PMECs showed increased formation of actin stress fibers , bearing no overlap with GST-PBD . Constitutively active Rac1 ( Ad . caRac1 ) expression ( Fig 5D ) restored GST-PBD binding in Rictor-null PMECs ( Fig 5E ) , suggesting that Rictor is necessary for Rac1-GTP in PMECs . Ad . caRac1 was used to determine if restoration of Rac1-GTP could rescue invasion in Rictor-null MECs . Ad . caRac1 increased invasion 2 . 5-fold over Ad . LacZ in Rictor-null PMECs ( Fig 5F ) . P-Akt S473 was unaffected by caRac1 ( Fig 5D ) , suggesting that while Akt and Rac1 are both effectors of Rictor-dependent signaling , they exist in two separable pathways in MECs . Despite having no impact on P-Akt , Ad . caRac1 decreased cell death in Rictor-null PMECs ( Figs 5G and S4E ) , suggesting that Rictor-dependent Rac1-GTP is necessary for PMEC survival . Ad . caRac1 also rescued branching morphogenesis of Rictor-deficient organoids ( Fig 5H ) . Conversely , Rac inhibition using a pharmacologic Rac1 inhibitor decreased organoid size and branching in WT organoids ( Fig 5I ) , confirming that Rac1 is necessary for mammary epithelial branching morphogenesis and appears to function downstream of mTORC2 . Thus , Rictor is required for Rac1-GTP signaling , and restoration of Rac1 activity rescued branching morphogenesis and survival of Rictor-deficient PMECs . To determine if Rictor/mTORC2-mediated branching morphogenesis and ductal outgrowth are dependent on PKC-alpha/Rac versus Akt in the context of the native mammary gland environment in vivo , we transduced PMEC from RictorFL/FL mice with control Ad . GFP versus Ad . Cre in the presence or absence of Ad . PKC-alpha , Ad . caRac , or Ad . Aktmyr and transplanted them into the cleared inguinal mammary fat pads of 4 week old recipient female mice . We harvested mammary glands from these animals 6 weeks post-transplantation and assessed epithelial architecture and branching morphogenesis in whole-mount preparations . Consistent with data from MMTV-Cre/RictorFL/FL mice , transplanted Rictor-deficient MEC produced structures characterized by shortened ductal outgrowths with fewer branches relative to GFP controls ( Fig 5J and 5K ) . Restored PKC-alpha or Rac activity ( Fig 5J and 5K ) rescued these defects and produced epithelial outgrowth that resembled endogenous epithelium in contralateral controls ( S4F Fig ) . Consistent with our ex vivo organoid culture analyses , restored Akt activity was unable to fully rescue defects produced by loss of Rictor ( Fig 5J and 5K ) . These data suggest that Rictor/mTORC2-dependent mammary epithelial morphogenesis relies primarily upon downstream activation of PKC-alpha and Rac-GTPase . Rapamycin is a pharmacologic inhibitor of mTOR originally thought to preferentially inhibit mTORC1 over mTORC2 . However , sustained rapamycin treatment impairs both mTORC1 and mTORC2 in a cell type-dependent manner [25–28] . Consistent with this idea , acute rapamycin treatment for 1 hour ( 1 h ) decreased P-S6 ( an mTORC1 effector ) but not P-Akt ( an mTORC2 effector ) , whereas sustained rapamycin treatment ( 24 h ) decreased both P-S6 and P-Akt S473 ( Fig 6A ) . Rapamycin treatment for 10 days significantly decreased branching morphogenesis and organoid size in WT organoids ( Fig 6B ) . Although PMEC survival was not affected by acute rapamycin treatment , cell death increased after 24 h with rapamycin treatment ( Fig 6C ) . Proliferation of WT PMECs , as measured by BrdU incorporation , was unaffected by acute ( 30 min ) or sustained ( 24 h ) pre-treatment with rapamycin ( Fig 6D ) . The effects of sustained rapamycin treatment , including reduced MEC survival , branching morphogenesis formation , and diminished organoid size were similar to the effects achieved by Rictor ablation in MECs . Also similar to what was seen with Rictor-deficient MECs , the phenotypic effects of rapamycin treatment were rescued by Ad . PKC-alpha ( Fig 6E ) and Ad . caRac1 ( Fig 6F ) , including rescue of branching morphogenesis and colony size . Ad . caRac1 also rescued rapamycin-mediated inhibition of cell motility ( Figs 6G and S5 ) . Because the mTOR inhibitor rapamycin impairs mTORC1 and mTORC2 , and recapitulates the morphological and molecular effects of Rictor ablation in MECs , these results suggest that Rictor is acting in complex with mTOR to regulate MEC survival , motility , and branching morphogenesis , supporting a role for mTORC2 in the developing mammary gland . However , these findings do not rule out the contribution of mTORC1 to mTOR-mediated mammary morphogenesis . To understand how mTORC1 participates in mammary morphogenesis , we infected PMECs harvested from female RaptorFL/FL mice [29] with Ad . Cre . Western analysis confirmed loss of Raptor and decreased P-S6 in serum-deprived cells ( Fig 7A ) . However , P-Akt S473 was unaffected by Raptor ablation , confirming that genetic ablation of Raptor causes selective inhibition of mTORC1 , while Rictor ablation inhibits mTORC2 . RaptorFL/FL mammary organoids infected with Ad . LacZ formed multi-branched colonies , as expected ( Fig 7B ) . Surprisingly , infection with Ad . Cre did not affect branching morphogenesis in RaptorFL/FL organoids , the number of branches per organoid , or colony size ( Fig 7B ) . Additionally , Raptor ablation had no significant impact on PMEC migration in wound healing assays ( Fig 7C ) . RaptorFL/FLMMTV-Cre ( RaptorMGKO ) mice were used to assess the impact of Raptor ablation on mammary morphogenesis in vivo . IHC detected Raptor and the mTORC1 effector P-S6 in RaptorWT mammary glands at 10 weeks of age but did not detect P-S6 in age-matched RaptorMGKO mice ( Fig 7D ) . Western analysis of whole mammary lysates from 10 week-old mice confirmed loss of Raptor ( S6A Fig ) . Immunofluorescent ( IF ) staining for cytokeratin ( CK ) -8 and CK14 , molecular markers of luminal and myoepithelial MECs , respectively , confirmed that Raptor loss did not affect the relative spatial organization of luminal and myoepithelial MECs ( S6B Fig–upper panel ) . Additionally , no alterations in localization or staining pattern of ZO-1 were observed ( S6B Fig–lower panel ) . Proliferation , as measured by IHC for Ki67 , was significantly decreased in RaptorMGKO ducts in 6-week old mice , but not in TEBs ( S6C Fig ) . By 10 weeks , however , proliferation in ducts had recovered to levels seen in RaptorWT ( Fig 7D ) . TUNEL analysis revealed similar ratios of TUNEL+ MECs in RaptorMGKO and RaptorWT samples harvested from 6 and 10 week old animals ( Fig 7D ) . Consistent with these observations , only mild defects in side branching and ductal length were found in mammary glands from 6-week old RaptorMGKO mice ( Fig 7E ) , and these were resolved by 10 weeks of age . Taken together , these results demonstrate that mTOR uses Rictor to activate PKCα/Rac1-dependent survival , motility , and branching morphogenesis in the mammary epithelium and that Rictor does not rely fully on Akt signaling to promote ductal morphogenesis in the breast .
Postnatal mammary epithelial morphogenesis requires precise coordination of cell proliferation , apoptosis , differentiation , and motility in order to turn rudimentary epithelial buds into an organized , branched ductal network permeating the entire mammary fat pad by the end of puberty [1 , 2] . mTOR is a central regulator of proliferation , apoptosis , differentiation , and motility , integrating numerous upstream signals to generate the desired biological outcome . Therefore , we assessed how mTOR signaling contributes to mammary morphogenesis . We found that pharmacologic mTOR inhibition reduced the size and branching complexity of mammary organoids in culture , phenotypes recapitulated by mTORC2 loss of function via Rictor ablation , but not upon mTORC1 inhibition through Raptor ablation . We also observed a disorganized epithelial architecture and stromal thickening around TEB upon tissue-specific Rictor ablation . The MMTV-Cre model has been reported to be leaky , leading to expression in tissues other than luminal mammary epithelium [30] thus it is possible that some of these defects may be due to loss of Rictor in stromal components . Alternatively , changes in basal epithelium may be a secondary effect of luminal cell misolocalization in the absence of Rictor , or Rictor expression in the luminal compartment may regulate expression and function of mTOR signaling intermediates in the basal cell layer through an indirect , juxtacrine signaling mechanism . We are actively investigating the role of Rictor/mTORC2 in luminal versus basal epithelium in our ongoing research . As our epithelial branching and survival phenotypes were recapitulated in the ex vivo stroma-free organoid culture model , however , it is likely that the effects on stroma are , at least in part , secondary to the loss of Rictor in epithelium . Genetic inhibition of mTORC2 also reduced ductal branching and lengthening in vivo , diminished P-Akt and P-PKC-alpha , and impaired activation of the GTPase Rac1 . Akt restoration only modestly enhanced branching morphogenesis in Rictor-deficient mammary organoids and was not sufficient to rescue cell survival or PMEC invasion through Matrigel . However , Akt inhibition did decrease organoid branching and colony size suggesting that Akt provides a critical signal in growth control , but is not sufficient to drive branching morphogenesis in the absence of Rictor . This is consistent with the data from our analysis of transplanted Rictor-deficient/AktMyr expressing MEC in vivo and with the phenotype of Akt1 deletion , which did not affect mammary epithelial cell differentiation but did impair lactation [31] . Deletion of Akt1 and one allele of Akt2 enhanced this defect [32 , 33] . Moreover , Akt activation did not completely inhibit luminal apoptosis during MCF10A acinar morphogenesis in culture [34] , suggesting that other factors also regulate cell survival during normal mammary epithelial development . In contrast to Akt , restoration of PKC-alpha signaling to Rac1 , or Rac1 activation independently of upstream signals , fully rescued all phenotypes resulting from Rictor loss in culture and in transplanted Rictor-deficient MEC in vivo , suggesting that Rictor-dependent mTORC2 is essential for PKC-alpha-Rac1 signaling to drive mammary morphogenesis . While not directly tested here , at least one additional study has elucidated mechanisms downstream of Rac1 that can control cell survival . One report using lymphoma cells demonstrated direct inhibition of apoptosis through Rac1-stimulated phosphorylation of the Bcl-2 family member , Bad , which occurred in an Akt-independent manner [35] . We observed a modest decrease in cell viability upon prolonged treatment with the Rac1 inhibitor in organoid culture coupled with the decreased branch extension , consistent with previous studies that also reported regulation of branching initiation and extension via PI3K-mediated Akt and Rac1 , respectively [36] . Interestingly , levels of mTORC1 target P-S6 are elevated in MEC upon Rictor loss relative to controls . This could reflect shift of mTOR kinase to complex 1 in the absence of a stable mTORC2 complex . It will be of great interest to track mTOR kinase association with the two complexes over the course of mammary epithelial development to better understand its functions . Activation of the Akt signaling pathway upon mTOR inhibition via a negative feedback loop has been observed in many cell types , including breast cancer cell lines ( Reviewed in [37] ) . In our study , rapamycin preferentially inhibited mTORC1 upon acute treatment ( e . g . reduction in P-S6 without affecting P-Akt-S473 levels ) and as prolonged treatment inhibited both complexes ( e . g . reduction in both P-S6 and P-Akt-S473 ) . These data are consistent with the observation that rapamycin is an effective inhibitor for activity of both complexes in many cell types [5] , including MECs . The differences in response to rapamycin between normal MECs and breast cancer cell lines could be due to differences in insulin-like growth factor receptors ( IGFRs ) , which are expressed at higher levels in cancer cells and mediate feedback to Akt upon mTOR inhibition ( Reviewed in [37–39] ) . Given the known roles of mTORC1 in cell growth , metabolism , and protein and lipid synthesis [3] , it was surprising that Raptor loss produced only a transient delay in ductal lengthening . It is possible that other signaling pathways may compensate for loss of mTORC1 function in Raptor-deficient mammary epithelium , such as RSK-mediated activation of S6 [40]; [41] . However , we observed similar decreases in cellular proliferation in the absence of Raptor and Rictor expression at 6 weeks that recovered by 10 weeks , suggesting that MEC proliferation may rely on both mTORC1 and mTORC2 . Decreased MEC proliferation upon genetic mTORC1 ablation is consistent with other reports of rapamycin-mediated cell growth inhibition in lactating mouse mammary explants , in lactating mice , and in milk-producing HC11 cells . Based on these previous studies , it will be important to determine the effects of Raptor and Rictor ablation on growth , differentiation , and milk production in alveolar mammary epithelium during pregnancy and lactation in vivo . The PI3-kinase ( PI3K ) /mTOR pathway is aberrantly activated up to 60% of clinical breast cancers , facilitating tumor cell growth , survival , metabolism , and invasion [42 , 43] . Moreover , increased PI3K activity in MMTV-Cre/PTENFL/FL mice increases mammary epithelial branching and decreases apoptosis during pubertal development [44] , suggesting that PI3K signaling is important in branching and survival in the breast . This idea is consistent with the phenotype produced by MMTV-Cre-driven Rictor loss , in which loss of a PI3K pathway mediator produces decreased branching and survival . While inhibitors of mTORC1 show limited clinical efficacy as single agents , anti-PI3K agents combined with dual mTORC1/2 inhibitors appear to be more effective [45–48] , underscoring the clinical relevance of mTORC2 in breast cancer . Importantly , these recent clinical observations parallel the data shown here demonstrating that mTORC2 inhibition due to either sustained rapamycin treatment or to Rictor deletion profoundly affected the complex series of events driving mammary morphogenesis , and these mTORC2-dependent processes occur in a manner unique and separable from mTORC1 . Interestingly , preferential targeting of mTORC2 versus mTORC1 reduced breast cancer cell motility and survival in culture and in vivo [18 , 49] , and Rictor knockdown suppressed anchorage-independent growth of MCF7 breast tumor cells [50] . Although at least one report suggests elevated Rictor levels correlate with higher overall and recurrence-free survival [51] , Rictor overexpression was observed in clinical invasive breast cancer specimens relative to normal breast tissue , as well as in lymph node metastases [18] , supporting the clinical relevance of mTORC2 in invasive breast cancer . Given our findings that Rictor/mTORC2 is required in the normal mammary epithelium for PKC-alpha-Rac1 activation which drives MEC survival , motility , and invasion , it will be interesting to determine if the mTORC2-PKC-alpha-Rac signaling axis is used by breast cancer cells to drive metastasis . If so , mTORC2-specific targeting or PKC-alpha inhibition could represent potential therapeutic strategies to limit metastatic spread of breast tumors , and to limit survival of disseminated tumor cells . Although data shown herein are the first demonstration of mTORC2-mediated regulation of normal MEC migration and invasion , several lines of evidence suggest that cancer cells exploit Rictor-dependent signaling pathways to facilitate invasion and metastasis . For example , siRNA-mediated Rictor knockdown inhibited MCF7 and MDA-MB-231 breast cancer cell migration [18 , 49] . Rictor knockdown inhibited transforming growth factor beta ( TGF-beta ) -mediated epithelial-to-mesenchymal transition ( EMT ) in breast cancer lines [52] . In contrast to our findings that untransformed MECs use Rictor to activate PKC-alpha and Rac1-mediated invasion , breast cancer cells used Rictor to drive motility through protein kinase C-zeta ( PKC-zeta; [18] ) , integrin-linked kinase ( ILK; [52] ) and Akt [49] . Although Akt phosphorylation at S473 required Rictor/mTORC2 in primary MECs , restoring Akt function was not sufficient to rescue survival , motility , or branching morphogenesis in the absence of Rictor . Restoration of Rac1 activity , an essential regulator of mammary epithelial branching morphogenesis [16 , 53] and a downstream effector of mTORC2 and PKC-alpha , rescued survival and migration defects induced by genetic mTORC2 inhibition . While not specifically linked to Rictor in breast cancer cells , Rac1-mediated invasion and metastasis of breast cancer cells has been reported previously [54–56] . Together , these data suggest that Rictor/mTORC2-dependent Rac signaling could promote breast cancer invasion , paralleling its function normal MEC branching morphogenesis . It is possible that breast cancer cells can engage multiple pathways ( PKC-zeta , ILK , Akt , Rac , and others ) to regulate tumor cell metastasis , and it is interesting to speculate that Rictor may lie at the intersection of each of these pathways . In summary , our data demonstrate distinct , non-overlapping functions of mTORC1and mTORC2 in post-natal mammary morphogenesis . Whereas Raptor-dependent mTORC1 signaling regulates proliferation , Rictor-dependent mTORC2 is essential for cell survival , cell junctions , motility , and branching morphogenesis . These findings underscore the importance of understanding the distinct roles for mTORC1 and mTORC2 in normal physiology of the breast and in breast cancer in order to intelligently develop and administer mTOR-directed therapies .
All animals were housed under pathogen-free conditions , and experiments were performed in accordance with AAALAC guidelines and with Vanderbilt University Institutional Animal Care and Use Committee approval . RictorFL/FL mice ( C57BL/6 ) were kindly provided by Dr . Mark Magnuson ( Vanderbilt University ) and have been previously described [14] . RaptorFL/FL mice ( [29] , C57BL/6 ) were purchased from the Jackson Laboratories ( Bar Harbor , ME ) . MMTV-Cre mice ( [13] FVB ) were purchased from the Jackson Laboratories . All analyses were performed on age-matched siblings resulting from F1 ( 1:1 , FVB:C57BL/6 ) intercrosses . Primary mammary organoids were generated from freshly collected , partially disaggregated mouse mammary glands using a modification of previously described methods [16] . Primary mouse mammary epithelial cells ( PMECs ) were harvested as described previously [57] . Organoids were immediately embedded in growth factor reduced Matrigel ( BD Bioscience ) at 50 organoids/100 microliters . Once polymerized , Matrigel-embedded cultures were overlain with Growth Media [DMEM:F12 supplemented with 5 micrograms/ml porcine insulin ( Sigma-Aldrich ) , 10 picograms/ml each estrogen and progesterone ( Sigma-Aldrich ) , 5 nanograms/ml human epidermal growth factor ( R&D Systems ) , 100 I . U . /ml penicillin-streptomycin ( Life Technologies ) ] . PMEC were maintained in Growth Media . For some experiments , cells were maintained for 24 hour in Starvation Media [Growth Media supplemented with penicillin-streptomycin only] or treated with Fibroblast-Conditioned Media ( DMEM:F12 supplemented 100 I . U . /ml penicillin-streptomycin cultured with mouse mammary fibroblasts for 48 hours and passed through a 0 . 2 micron filter ) for wound closure migration studies . Rapamycin ( Sigma-Aldrich , 20 nanomolar ) , InSolution Rac1 inhibitor ( Calbiochem/Millipore , 20 micromolar ) , and adenoviral particles ( Ad . Cre , Ad . LacZ , Ad . caRac1 , Ad . Aktmyr , and Ad . PKC-alpha , Vector Biolabs ) were purchased . Freshly collected organoids were incubated with adenoviral particles ( 5 X 108 particle forming units/ml ) with constant rocking for 3–5 hours at 37°C , washed , and embedded in Matrigel . We analyzed 20–30 independent organoids isolated from 5–6 independent mice in 2–3 experiments for each condition . Morphogenesis in organoids was scored by counting the number of branches/organoid in 10 or more organoids/culture condition . For structures that appeared more spherical and less branched ( e . g . cultures treated with Ad . Cre or inhibitors ) , we counted bifrucations and/or small protrusions from ball-shaped structures as branches in order to be as rigorous and conservative in our quantifications as possible . Organoid size was scored using NIH Image J software to quantify pixel area in 10 or more organoids/culture condition . MCF10A and MCF10A RictorZFN were purchased from Sigma-Aldrich and cultured in Growth Medium [DMEM:F12 supplemented with 5% Horse Serum ( Life Technologies ) , 10 μg/ml porcine insulin ( Sigma-Aldrich ) , 20 nanograms/ml human epidermal growth factor ( R&D Systems ) , 10 nanograms/ml cholera toxin ( Sigma-Aldrich ) , 100 micrograms/ml hydrocortisone ( Sigma-Aldrich ) , 100 I . U . /ml penicillin-streptomycin ( Life Technologies ) ] . For some experiments , cells were maintained for 24 hour in Starvation Media [Growth Media without serum or EGF] prior to stimulation and/or analysis . PKC-alpha inhibitor GO6976 ( Sigma-Aldrich , 2 nm ) and adenoviral particles ( Ad . RFP and Ad . PKC-alpha , Vector Biolabs ) were purchased . Cells were incubated with adenoviral particles ( 5 X 108 particle forming units/ml ) for 3–5 hours at 37°C and cells were allowed to recover for 48 hours prior to experimental analysis . Matrigel-emdedded organoids cultured on coverslips were fixed 8 minutes in 1:1 methanol:acetone at -20°C , permeabilized in 0 . 5% Triton-X 100/PBS for 10 min , blocked [130 millimolar NaCl , 7 millimolar Na2HPO4 , 3 . 5 millimolar NaH2PO4 , 7 . 7 millimolar NaN3 , 0 . 1% bovine serum albumin , 0 . 2% Triton-X 100 , 0 . 05% Tween-20] and stained with rabbit anti-pan-cytokeratin ( Santa Cruz Biotechnology , 1:100 ) and AF621-goat anti-rabbit ( 1:100 ) , counterstained with TO-PRO-3 Iodide ( Invitrogen ) , and imaged using the Vanderbilt Cell Imaging Shared Resource Zeiss LSM 510 confocal microscope and LSM Image Browser software . For E-cadherin staining , the primary antibody used was anti-E-cadherin ( BD Transduction Laboratories ) and visualized with anti-mouse Alexa 594 secondary antibody ( Invitrogen , Molecular Probes ) . Confocal images of 3D structures were visualized using an LSM 510 META inverted confocal microscope with a 20X/0 . 75 plan apochromat objective . Cells and tissues were homogenized in ice-cold lysis buffer [50 millimolar Tris pH 7 . 4 , 100 millimolar NaF , 120 millimolar NaCl , 0 . 5% NP-40 , 100 micromolar Na3VO4 , 1X protease inhibitor cocktail ( Roche ) ] , sonicated 10 seconds , and cleared by centrifugation at 4°C , 13 , 000 x g for 5 min . Protein concentration was determined using BCA ( Pierce ) . Proteins were separated by SDS-PAGE , transferred to nitrocellulose membranes , blocked in 3% gelatin in TBS-T [Tris-buffered saline , 0 . 1% Tween-20 ) , incubated in primary antibody overnight and in HRP-conjugated anti-rabbit or anti-mouse for 1 hour , and developed using ECL substrate ( Pierce ) . Antibodies used: alpha-actin ( Sigma-Aldrich; 1:10 , 000 ) ; AKT and S473 P-Akt ( Cell Signaling; 1:2 , 000 and 1:500 , respectively ) ; S6 and P-S6 ( Cell Signaling; 1:1 , 000 ) ; Rictor ( Santa Cruz; 1:250 ) ; Raptor ( Cell Signaling; 1:500 ) ; Rab11 ( Cell Signaling; 1:1 , 000 ) ; PKC-alpha and T638/641 P-PKC-alpha ( Cell Signaling; 1:2 , 000 ) ; Rac ( BD Transduction; 1:200 ) . GST-Pak-PBD effector pulldown assays were performed using reagents from Millipore as per manufacturer’s protocol . Mammary glands were whole-mounted on slides , cleared of adipose , and stained with hematoxylin as described previously [57] . Sections ( 5 micron ) were stained with hematoxylin and eosin . In situ TUNEL analysis was performed on paraffin-embedded sections using the ApopTag kit ( Calbiochem ) . IHC on paraffin-embedded sections was performed as described previously [58] using: Ki67 ( Santa Cruz Biotechnologies ) , P-S6 ( Cell Signaling Technologies ) ; P-Akt S473 ( Cell Signaling Technologies ) ; Rictor ( Santa Cruz ) , E-cadherin ( Transduction Labs ) . Immunodetection was performed using the Vectastain kit ( Vector Laboratories ) , AF488-conjugated anti-rabbit , or AF621-conjugated anti-mouse ( Life Technologies ) , according to the manufacturer’s directions . Methanol-fixed PMECs were probed 1 hour with GST-PBD ( Millipore ) diluted 1:50 in PBS . GST ( lacking PBD ) was used as a negative control . Samples were washed then probed with AF488-conjugated anti-GST ( 1:100 ) , stained with DAPI or AF621-phalloidin , and mounted . MECs ( 105 ) were added to upper chambers of Matrigel-coated transwells in starvation medium and incubated 5 hours to score migration in response to 10% serum-containing medium in the lower chamber . Filters were swabbed and stained with 0 . 1% crystal violet , [59] and cells on the lower surface were counted . For wound closure , 50 , 000 MECs were plated on Matrigel-coated 24 well plates , grown to confluence , serum-starved for 24 hours , and wounded with a P200 pipette tip . Migration in response to mammary fibroblast conditioned medium [60] was scored by measuring the [width of the wound area at 24 hours] ÷ [width of the wound area at 0 hours] as described previously [61] . Mammary gland whole-mounts and transwell filters were imaged with Olympus SZX12 Inverted Microscope . Slides were imaged with Olympus BX60 Stereo Microscope . Organoids , annexin V-FITC-staining , and wound closure assays were imaged with Olympus IX71 Inverted Microscope . All images were acquired by Olympus DP 72 Digital Camera and CellSens software at ambient temperature . All animals were housed under pathogen-free conditions , and experiments were performed in accordance with AAALAC guidelines and with Vanderbilt University Institutional Animal Care and Use Committee approval . The laboratory animal care program of Vanderbilt University ( PHS Assurance #A3227-01 ) has been accredited by AAALAC International since 1967 ( File #000020 ) . The AAALAC Council on Accreditation's most recent review of VU's program was done in 2011 and resulted in "Continued Full Accreditation . ” Isofluorane was used for anesthesia , as well as euthanasia . For human euthanasia , cervical dislocation was used following isofluorane overdose .
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The protein kinase mTOR is frequently activated in breast cancers , where it enhances cancer cell growth , survival , and metastastic spread to distant organs . Thus , mTOR is an attractive , clinically relevant molecular target for drugs designed to treat metastatic breast cancers . However , mTOR exists in two distinct complexes , mTORC1 and mTORC2 , and the relative roles of each complex have not been elucidated . Moreover , as pathways that regulate normal tissue growth and development are often highjacked to promote cancer , understanding mTOR function in normal mammary epithelial development will likely provide insight into its role in tumor progression . In this study , we assessed the role of mTORC1 and mTORC2 complexes in normal mammary epithelial cell branching , survival , and invasion . Interestingly , while mTORC1 was not required for branching , survival and invasion of mammary epithelial cells , mTORC2 was necessary for these processes in both mouse and human models . Furthermore , we found that mTORC2 exerts its effects primarily through downstream activation of a PKC-alpha-Rac1 signaling axis rather than the more well-studied Akt signaling pathway . Our studies identify a novel role for the mTORC2 complex in mammary morphogenesis , including cell survival and motility , which are relevant to breast cancer progression .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
mTOR Directs Breast Morphogenesis through the PKC-alpha-Rac1 Signaling Axis
|
West Nile Virus ( WNV ) , an emerging and re-emerging RNA virus , is the leading source of arboviral encephalitic morbidity and mortality in the United States . WNV infections are acutely controlled by innate immunity in peripheral tissues outside of the central nervous system ( CNS ) but WNV can evade the actions of interferon ( IFN ) to facilitate CNS invasion , causing encephalitis , encephalomyelitis , and death . Recent studies indicate that STimulator of INterferon Gene ( STING ) , canonically known for initiating a type I IFN production and innate immune response to cytosolic DNA , is required for host defense against neurotropic RNA viruses . We evaluated the role of STING in host defense to control WNV infection and pathology in a murine model of infection . When challenged with WNV , STING knock out ( -/- ) mice displayed increased morbidity and mortality compared to wild type ( WT ) mice . Virologic analysis and assessment of STING activation revealed that STING signaling was not required for control of WNV in the spleen nor was WNV sufficient to mediate canonical STING activation in vitro . However , STING-/- mice exhibited a clear trend of increased viral load and virus dissemination in the CNS . We found that STING-/- mice exhibited increased and prolonged neurological signs compared to WT mice . Pathological examination revealed increased lesions , mononuclear cellular infiltration and neuronal death in the CNS of STING-/- mice , with sustained pathology after viral clearance . We found that STING was required in bone marrow derived macrophages for early control of WNV replication and innate immune activation . In vivo , STING-/- mice developed an aberrant T cell response in both the spleen and brain during WNV infection that linked with increased and sustained CNS pathology compared to WT mice . Our findings demonstrate that STING plays a critical role in immune programming for the control of neurotropic WNV infection and CNS disease .
Encephalitic Flavivirus infections , including West Nile virus ( WNV ) , are ongoing or emerging threats to global health [1–4] . In particular , WNV continues to re-emerge in the Americas , causing neuropathology and death in the most severe cases [3 , 5–7] . Since its emergence in the USA in 1999 , annual outbreaks of WNV are impacted with fluctuations in neurovirulence attributed to the circulating strain [4–6 , 8 , 9] . Morbidity and mortality are dramatically increased in years where the circulating strain has enhanced neurovirulence , highlighting the significance of understanding host-pathogen interactions that control neurotropism [5 , 10] . An analysis of CDC reports reveals that of all cases reported between 1999–2014 , 9% of neurovirulent cases result in death , in contrast to 0 . 5% of non-neurovirulent WNV cases . Factors that limit WNV neurovirulence are not well understood but are critical to restrict pathology associated with WNV infections [5] . WNV infection in humans most commonly manifests as an asymptomatic or mild febrile illness known as West Nile Fever ( WNF ) with symptoms that include headache , generalized weakness , rash , fever or myalgia , and in some cases vomiting , diarrhea , joint or eye pain [3 , 5–7 , 11–13] . While most patients displaying WNF generally display symptoms for days to weeks , in some cases persistent symptoms continue to impact quality of life and cognitive abilities rendering a chronic disease outcome to WNV infection [11] . More serious disease occurs if the virus crosses the blood brain barrier and progress to West Nile Neuroinvasive Disease ( WNND ) [7] . WNND disease symptoms include meningitis , encephalitis , myelitis marked with acute flaccid paralysis , gastric complications , tremors and Parkinson-like symptoms [7 , 11 , 14–18] . Patients with WNND can maintain symptoms for weeks to months , with persistent symptoms including chronic fatigue , functional cognitive disorders or neuropsychiatric disabilities and physiological complications , particularly those who exhibited acute flaccid paralysis symptoms during acute infection [7 , 11 , 18] . Currently no therapeutics or vaccines are available for treatment of WNV infection or neuropathogenesis . Thus , there remains a critical need to understand the virus-host interactions of WNV neurovirulence . Both the innate and adaptive immune response are required to clear WNV infection and restrict immune mediated pathology [19] . In humans , infection with WNV typically occurs through subcutaneous inoculation from the bite of an infected mosquito . A parallel form of infection using sub-cutaneous challenge of WNV in a mouse model has been shown to replicate the progression , tissue involvement , and pathology of WNV infection that occurs in humans [19–22] . In the mouse model , viral replication occurs at the subcutaneous site of entry followed by infection of the draining lymph node and splenic infection [19] . These processes first trigger innate immune activation in peripheral tissues outside of the central nervous system ( CNS ) through viral recognition by the RIG-I-like receptors to induce IRF3 activation and the production of types I and III interferon ( IFN ) [23–26] . Innate ( RLR ) immune defenses triggered by RLR signaling and IFN actions serve to restrict the tissue tropism of WNV and are essential for protection against neuroinvasion [19 , 23 , 24 , 27–34] . Type I and III IFN are essential to inform the innate and adaptive immune interface to balance development of effective immunity , protect the blood-brain barrier , and limit immune-related pathology in the CNS [19 , 23 , 24 , 35–39] . In particular , type I IFN-dependent cytokine and chemokine signaling cascades are essential for functional development of the cytotoxic CD8+ T cell response , as well as its regulatory T cell ( Tregs; FoxP3+ CD4+ T cells ) counterpart [24 , 36 , 37 , 39–42] . While CD8+ T cells are required for controlling both peripheral and CNS viral load , CD4+ T cells , specifically Tregs , are essential for preventing symptomatic disease in the CNS [40–43] . The adaptor protein , Stimulator of Interferon Genes ( STING ) , has also been implicated in host defense against WNV [44–46] . STING was first described as an essential defense mechanism against both RNA and DNA viruses [47 , 48] . Since then , STING has been recognized for its role in responding to cytoplasmic DNA and mediating subsequent innate immune activation and IFN production . However its role in the defense against RNA viruses is poorly understood [47–54] . Intriguingly , multiple RNA viruses , including dengue virus , yellow fever virus , hepatitis C virus and coronaviruses , direct viral evasion strategies to disrupt the STING signaling pathway , reflecting a likely role for STING in host defense against RNA viruses [52] . STING was found to be required for host defense during infection with influenza A virus , as well as dengue virus , a closely related flavivirus to WNV [55–57] . Additionally , during infection with related flavivuses including Japanese encephalitis virus ( JEV ) and Zika virus , STING deficiency led to increased neuropathology in vivo and in vitro , suggesting a critical role for STING in CNS defense [58 , 59] . The role for STING in the CNS has been implicated in multiple other neurodegenerative diseases including Aicardi-Goutières syndrome , sterile immune mediated CNS pathology and during chronic CNS diseases [14 , 16 , 60–66] . In this study , we investigated the hypothesis that STING plays a regulatory role in the immune response against WNV , thereby restricting viral neurotropism and neuropathology . We show that STING is essential for host defense against WNV in a mouse in vivo model of infection . Clinical and pathological analyses demonstrate a novel role for STING in conferring CNS defense against WNV in vivo . We found that tonic levels of type I IFN were decreased in STING-/- bone marrow derived macrophages ( BMDM ) and linked with increased susceptibility to WNV infection . Following infection , we observed heightened immune responses in vitro and in vivo concomitant with increased viral load . STING deficiency led to the development of an aberrant adaptive immune response , with decreased activation of CD8+ cells and T regulatory cells ( Tregs ) in the spleen , and decreased CD4+ T cell numbers resulting in an altered CD4/CD8 T cell ratio in the CNS coupled with CNS disease . Our observations imply an essential role for STING within the interface between the innate and adaptive immune responses for effective immune programming in the control of WNV infection and CNS disease .
Previous studies demonstrated that mice defective in STING signaling experienced increased mortality during WNV infection , yet the linkage of STING to immune response programming for defense against WNV has not been defined [46] . Using genetically knocked-out Tmem173 ( STING-/- ) mice [67] , we first performed a survival analysis to confirm the role of STING in host survival during WNV infection ( Fig 1A ) . C57B/6J ( B6 , WT ) and STING-/- mice were infected through subcutaneous virus challenge via foot-pad injection and monitored for 18 days post infection ( dpi ) . Mice were scored daily for morbidity , marked as loss in body weight ( Fig 1B ) and overall increased clinical score ( Fig 1C ) . Consistently , between 8–12 dpi , mice either met euthanasia criteria ( Terminal; T ) or went on to survive ( Survivors; S ) through 18 dpi ( study end-point ) ( Fig 1D ) . Using this model , we confirmed the occurrence of increased susceptibility to WNV infection in the complete absence of STING ( Figs 1A and S1A ) , similar to what was previously described in STINGgt/gt mice [46] . We also observed significantly increased clinical severity scores in the STING-/- mice that persisted until the study-endpoint , when WT mice had returned to a base-line clinical score ( Fig 1B and 1C ) . Additionally , we monitored mice daily for the duration of the experiment until they either met euthanasia criteria or at the study end-point , day 18 post infection . Results from each mouse were analyzed to determine if there were differences in clinical signs between WT and STING-/- mice . Notably , STING-/- mice displayed increased neurological signs of disease , characterized by loss of balance , reduced muscle tone and reflexes predominantly in the pelvic limbs and increased paresis and paralysis , implicating more severe damage to the hind-brain and spinal cord ( Fig 1E and 1F ) . In order to determine if there was a survivor bias in the clinical data , we retrospectively stratified the data into cohorts of mice that met euthanasia criteria ( Terminal; T ) or ones that survived until day 18 post-infection ( Survivors; S ) , the pre-determined study end-point ( Fig 1D , 1G and 1H ) . By doing so , we found that significant differences in body weight loss and clinical scores between WT and STING-/- mice were only observed in the Survivor cohort and not in the Terminal cohort . While there is an essential role for STING in host survival during acute infection ( Figs 1A and S1A ) , these data implicate an additional prolonged requirement for STING in both prevention and recovery from neurological pathology . When we examined CNS pathology , we found that in both WT and STING-/- mice , pathological scores were significantly increased in the spines of the Survivor cohort , with a trend toward increased scores in the brains and spines of the Terminal cohort ( Fig 1D , 1I and 1J ) . Intriguingly , while STING-/- Terminal mice displayed increased CNS pathology , WT mice that met Terminal criteria had unexpectedly low clinical scores , suggesting that they met euthanasia criteria for reasons independent of severe encephalitis . During necropsy , we observed that the gastro-intestinal ( GI ) tract of Terminal mice exhibited gross distension or other aberrant phenotypes including stool compaction , disintegration and in some cases severe reduction in size or collapse of the GI tract ( S1B Fig ) . Pathologic analysis confirmed that Terminal mice display increased GI pathology that included microbiome overgrowth and neuronal degeneration and loss in the myenteric ganglia , particularly in STING-/- ( S1C and S1D Fig ) . Previous studies have indicated that GI manifestations during WNV infections exist in both mice and humans , and are positively correlated to increased neurotropism and mortality [15–17 , 22] . This outcome may imply that WT mice are meeting euthanasia criteria following WNV infection due to severe GI disease rather than severe CNS involvement as previously thought . Further , these results demonstrate that STING plays a systemic role in host defense against WNV , with increased frequency of mortality and pathology occurring in the CNS and GI tract in STING-/ mice . Together , these results show an essential role for STING in host survival and neuropathological defense in the CNS during WNV infection . To determine if STING is required for viral control in the CNS , we challenged mice with WNV via footpad injection and examined tissue viral load at 4 dpi ( peak of peripheral viremia ) and 8 dpi ( peak of detectible virus in the CNS ) ( Fig 2A ) . Viral titer of macrodissected brains and extracted spinal cords were examined by plaque assay individually for each mouse in the cohort ( Fig 2A ) . As expected , virus was not detected at 4 dpi in the CNS but by 8 dpi virus was clearly detected in different CNS regions . Virus was not consistently found in the CNS of all mice nor in every tissue examined . There was however , a consistent trend toward increased numbers of infected mice with detectible virus in the CNS as well as increased viral titers in the CNS of STING-/- mice compared to WT . To determine if there was detectible virus in the brains of Terminal vs Survivor mice , tissues from retrospectively sorted mice utilized for pathological analysis ( Fig 1I and 1J ) were immunostained for the presence of WNV antigen ( Fig 2B ) . WNV foci were found in the brains of WT and STING-/- Terminal mice but were not apparent in WT or STING-/- Survivors , suggesting that either the virus had cleared or that surviving mice did not have CNS infection . Neuronal death was assessed by TUNEL stain in both WT and STING-/ Survivors . Here we observed enhanced neuronal apoptotic death in the STING-/- cohort , suggesting STING may have a direct or indirect role in neuronal defense in the CNS ( Fig 2C ) . In order to determine if STING is required for neuronal defense against WNV , primary cortical neurons were isolated and cultured , followed by infection with WNV to determine viral growth kinetics under conditions of single and multi-step growth ( Fig 2D ) . Surprisingly , no difference was detected between in WNV replication in WT and STING-/- primary cortical neurons ( Fig 2D ) . To determine if the actions of STING might be restricted to the CNS for WNV protection , we performed an intracranial virus inoculation bypassing the role of the peripheral immune response and physical barriers such as the blood-brain barrier to directly infect the brain with WNV ( Fig 2E ) . At 4 dpi , there was no difference in CNS viral load found in WT vs STING-/- mice nor was viral load different between STING-/- and WT mice . Taken together , our observations imply that the role of STING is not limited to mediating viral control in the CNS . It is possible that STING is therefore required in the development of a protective immune response in the periphery such that in the absence of STING the immune response is aberrantly programmed , leading to CNS immunopathology . Given that STING deficiency was associated with enhanced mortality ( see Fig 1 ) without a significant increase in CNS viral burden ( Fig 2 ) , we considered that STING deficiency could result in defective antiviral innate immune signaling and lead to loss of viral control in the periphery , thereby leading to enhanced morbidity and mortality . We first tested the role of STING in BMDMs , as macrophages are a tropic cell and key modulator of peripheral viral control during WNV infection ( Fig 3A ) [19] . As expected , WNV levels were significantly increased by 24 and 48 hours post inoculation ( hpi ) . Unexpectedly however , STING-/- BMDM had increased innate immune and inflammatory gene expression , including enhanced level of type I IFN expression during WNV infection ( Fig 3B ) . We then examined the spleens of infected mice to determine if there was an overall loss of viral control manifested as increased viral load over WT . As expected , virus was detected at 4 dpi in both WT and STING-/- . Surprisingly however , there was no difference in 4 dpi viral titers between WT and STING-/- , nor was there a sustained virologic response in STING-/- mice ( Fig 3C ) . These data indicate that peripheral loss of viral control does not occur in the absence of STING ( Fig 3C ) . Similarly , viral RNA was detected equally in spleens of infected WT and STING-/- mice at 4 dpi , but the virus was largely cleared from the spleen by 8 dpi ( Fig 3D ) . In the CNS however , we observed a trend toward increased viral RNA and innate immune gene expression at 8 dpi in WNV-infected STING-/- mice , similar to that observed in BMDM ( Fig 3A and 3D ) . These data were unexpected as we initially predicted that STING deficiency would reduce innate immune activation based on the known role of STING signaling in IFN induction . These data demonstrate that innate immune activation and the inflammatory response are exacerbated in both in vitro and in vivo STING deficient models , possibly culminating in enhanced immunopathology in STING-/- mice . The canonical STING sensing pathway is dependent on upstream recognition of DNA danger- or pathogen-associated molecular patterns ( DAMP , PAMP ) such as DNA viruses , cell-free or mitochondrial DNA , by cyclic GMP-AMP synthase ( cGAS ) . In mammals , cGAS binding to dsDNA activates its synthase activity to produce a cyclic di-nucleotide , cGAMP ( cyclic guanosine monophosphate-adenosine monophosphate ) , which binds to STING , initiating downstream activation of STING by phosphorylation , STING relocalization from diffuse cytosolic to punctate pattern , and subsequent induction of innate immune signaling and IFN production [47 , 48 , 53 , 68 , 69] . During RNA virus infections however , the role for STING defense has not been well-characterized . To evaluate the activation of STING during WNV infection , we utilized a recently described telomerase reverse transcriptase human foreskin fibroblasts ( HFF ) model to assess activation of endogenous STING by phosphorylation and relocalization from the cytoplasm to the perinuclear space during WNV infection [70] . Transfection of interferon-stimulated DNA ( ISD; calf-thymus DNA ) into HFFs initiated re-localization of STING as previously reported by 3hpi [48 , 70] . Intriguingly however , STING was not relocalized in WNV infected cells ( Fig 4A ) . It is possible that the kinetics of STING activation are different from ISD activation of STING as compared to WNV infection , so we performed a time course experiment to detect STING activation by phosphorylation status [71] , assessing a range of 1–24 hpi at MOI = 1 ( Fig 4B ) . Similar to what was observed by IFA , STING phosphorylation was not observed at any time point during WNV infection , although phosphorylated STAT1 and WNV protein was detected at 24 hpi , suggesting virus replication and innate immune signaling were occurring normally ( Fig 4B ) . To determine if activation was dependent on viral load , we infected HFF with a MOI = 1 and MOI = 10 of WNV , but also observed no STING activation as measured by phosphorylation ( Fig 4C ) . These data suggest that STING is not canonically activated during WNV infection in HFF cultures and reveals a potential non-canonical role for STING in host defense during infection with WNV . In order to determine if there was a systemic change in the innate immune profile in STING-/- mice , we examined the cytokine and chemokine profile in the serum of WT and STING-/- mice at the peak of peripheral viremia ( 4 dpi ) and CNS viral burden ( 8 dpi ) . We found that mock infected STING-/- mice had an increased basal production of multiple cytokines and chemokines at 4 dpi . We also observed significant increases in IL33 , IL4 , IL6 , IL15 , MCSF , Gro-alpha , while at 4 dpi IP-10 ( CXCL10 ) was decreased in STING-/- compared to WT mice ( S2 Fig ) . While these cytokines have multiple roles in immune modulation , a common role among them is in activation and recruitment of T cells . These data suggest that STING is required for regulation of immune cytokine and chemokines that program immune cell trafficking and actions during WNV , as has been shown for STING in cancer immunity and autoimmune signaling [53] . To determine if STING is required for proper programming of the T cell response during WNV infection , we examined splenic T cells from WT and STING-/- mice at 8 dpi , a time point when the adaptive immune response is established in WT mice [24] . We observed a reduction in the frequency of CD8+ T cells , along with a trend toward decreased numbers of T cells in the spleens of STING-/- mice compared to WT during WNV infection ( Fig 5B ) . Additionally , within the CD8+ T cell subset ( Fig 5C ) , there was a significant decrease in frequency of activated ( CD44+ ) and CXCR3+ T cells , and we observed a consistent trend of decrease in the frequency of WNV-specific CD8+ T cells in the spleens of STING-/- mice compared to WT , suggesting that STING is required for optimal anti-WNV CD8+ T cell responses . We also observed a significant increase in the frequency of CD4+ T cells in STING-/- mice ( Fig 5B ) , with a corresponding trend toward increased absolute cell numbers . While we observed a trend toward differences in the absolute number of most cell populations examined between WT and STING-/- mice , we found that significant differences most typically occurred in cell frequencies , suggesting that the balance of T cells subsets may be skewed in the absence of STING . In particular , we found skewing within the T regulatory cell ( FoxP3+ ) populations ( Fig 5E–5G ) , with significant deficits in Ki67+ , CD44+ and CD73+ Tregs , CD44 and CD73+ Tregs . These data suggest that STING is required for modulating T cell responses and T cell frequencies during WNV infection that lead to a protective rather than pathogenic outcome . Because of the heightened innate immune profile and aberrant programming of the T cell responses in spleens of STING-/- mice , we examined the CNS-specific T cell profile across mouse lines . Histological analyses revealed trends toward increases in CNS immune cellularity , both in the form of perivascular and parenchymal mononuclear infiltrate , suggesting the CNS pathology may be immune-mediated ( Fig 6A ) . We then performed a CD3 IHC stain in the brains of Survivors , we found increased clusters of CD3 infiltrate in the hind and mid-brain regions ( Fig 6B ) co-localized with robust lesions . In serial slices of the same tissues , we did not observe WNV staining by IHC in STING-/- Survivors ( Fig 2 ) , however we did observe continued gliosis , suggesting that a potential immunopathology may occur in the brain of STING-/- mice infected with WNV . Previous studies indicated that cellular infiltrate in the brain is predominantly comprised of CD3+ T cells during WNV infection [72] . Therefore , we characterized T cell responses of WT and STING-/- mice in the CNS on 4 dpi to examine baseline differences at 8 dpi when WNV and leukocytes are both present in the CNS ( Fig 6J ) . Lymphocyte and T cell responses in both mock and WNV-infected mice were comparable at 4 dpi , indicating that there was no gross difference in the CNS between WT and STING-/- mice ( Fig 6C and 6D ) . By 8 dpi however , we found statistically significant decreases in the frequency and numbers of CD4+ T cells in STING -/- mice ( Fig 6F ) . Although there was no difference in the total numbers of CD8+ T cells , there was a statistically significant increase in the frequency of CD8+ T cells in the CNS of STING-/- mice , likely due to overall trend of decreased numbers of lymphocytes in the brain ( Fig 6C–6E ) . By 8 dpi , these changes resulted in a significantly decreased CD4/CD8 ratio of T cells , indicating an imbalanced T cell response to WNV in the CNS of STING-/- mice ( Fig 6I ) . Of cells that made it to the brain by 8 dpi , no differences were found in the absolute number of activated ( CD44+ ) or WNV-specific ( NS4b Tetramer+ ) CD8+ T cells ( Fig 6G and 6H ) , FoxP3+CD25+CD4+ T cells ( Fig 6K ) in the brain . These data suggest that STING is not essential for recruitment of WNV-specific cytotoxic T cells in the CNS , however it may be required for balancing the cytotoxic vs immunosuppressive adaptive response . Furthermore , it is also possible that the enhanced recruitment of cells to the CNS is in response to damage caused by the virus , aberrant immune signaling , or both . This outcome would suggest that STING plays an essential role in modulating the balance between immunopathogenic and immunoprotective response in the CNS during WNV infection . The increase in clinical disease and pathological damage observed in the STING-/- versus WT mice , particularly in Survivors , could be due to an aberrant immune response resulting in CNS damage after initial viral insult . We found that CNS pathology in WT mice is largely restricted to the cortex and meninges , while STING-/- mice display increased pathology in the cerebellum and hind/mid brain regions in addition to the cortex and meninges ( Fig 7A and 7C ) . These data correlate with the increased CD3 staining observed by IHC in STING-/- mice ( Fig 6B ) , also noted as the same brain regions where WNV is often detected by IHC ( Fig 2B ) . These observations suggest that STING plays a role in directing or maintaining the T cell response to specific loci within the CNS or that initial viral infection led to increased recruitment of a localized adaptive immune response that resulted in immunopathology . Furthermore , pathology in the spine was more diffuse , suggesting that STING has a widespread protective role in the CNS during WNV infection ( Fig 7B and 7D ) . These observations led us to investigate if there was a localized polarization of microglia or infiltrating macrophages in CNS regions toward an M1 or M2 phenotype ( Fig 7E ) . Microglia have the highest levels of STING ( Tmem173 ) expression observed in any cell within the adult mouse [73 , 74] and it is possible that in the absence of STING , microglia are aberrantly polarized , enhancing immune-mediated pathology . To examine this possibility , we assessed the expression of M1 ( CXC1 and IL6 ) and M2 ( Pparg , Arg1 , Chil3 and Retnla 1 ) associated genes by RT-qPCR in different regions of the CNS . In WT mice , we found that CXCl1 ( marking an M1 phenotype ) was present in the brain stem by day 8 post infection , and Retnla 1 expression ( marking an M2 phenotype ) occurred in both the mock and 4 dpi tissues within the brain stem and sub-cortex ( containing the thalamus ) regions of the brain ( Fig 7E ) . This profile suggests that CNS homeostasis includes a localized M2 phenotype that is induced to a M1 phenotype in WT mice following WNV infection . In STING-/- mice however , we found a widespread increase in the M1 response gene expression ( marked by CXCL1 and IL6 ) with the highest expression observed in the brain stem and spinal cord . Simultaneously , there was also a corresponding increase in Pparg and Chil3 ( marking the M2 phenotype ) , with no clear difference in Arg1 expression and an overall trend toward decreased expression of Retnla . These observations reveal a widespread increase in both M1 associated genes , with altered regulation of the M2 associated genes in STING-/- mice , potentially resulting in aberrant balance of the M1 and M2 polarization in the CNS . To determine where in the CNS STING is actually localized and if this tissue localization overlaps with the location of the cellular infiltrate noted histopathologically or with expression of innate immune genes , we utilized the Allen Brain Institute database to search for STING ( Tmem173 ) localization in the mouse brain [75] . Within the brain , STING expression is found within the olfactory bulb , thalamus/midbrain , brainstem and cerebellum , as well as low levels throughout the cortex , overlapping areas that are affected most severely by WNV infection ( S3 ) [14 , 75] . These regions of brain affected correlate with the clinical signs we observed including loss of balance , tremors , and loss of motor function ( Figs 1E and 7C–7E ) . Furthermore , these areas of STING expression overlap with the brain regions where altered regulation of M1 or M2 gene expression were most readily observed , implicating a role for STING in polarization of either or both microglia and macrophages in the CNS . Cumulatively , these data suggest that STING has an essential role in maintaining immune response homeostasis and immune programming in initial defense against WNV infection . Without STING , immunopathology occurs , leading to exacerbated CNS disease and clinical sequelae .
Recent years have seen a marked increase in the global health threat presented by emerging and re-emerging encephalitic viruses , particularly those with increased neurotropism and neuropathology such as WNV [1 , 3 , 10 , 76 , 77] . Previous studies indicated an important role for STING in host survival during WNV infection [46] , however it is unclear what role STING plays in conferring host defense against RNA viruses [52 , 54] . Here , we demonstrate that STING is essential to prevent host morbidity and mortality during WNV infection where it plays a role in immune homeostasis and programming . However , STING is not canonically activated in vitro upon infection with WNV , revealing a novel function for STING during infection with RNA viruses . Furthermore , we show that STING is essential for host neuropathological defense against WNV through regulation of the innate-adaptive immune interface in vivo . We found that STING deficient mice exhibit increased mortality and morbidity including increased and sustained neurological clinical signs , particularly in mice that survive infection ( Fig 1 ) . These data were corroborated by pathological analysis , which also revealed distinct differences in CNS pathology . Intriguingly , there seems to be a stratification in clinical and pathological findings between the STING-/- mice that meet euthanasia criteria and those that go on to survive . Survivorship bias has been previously reported in the WNV model , with these data further implicating this bias as a critical factor to consider when performing time course vs . end-point experiments [78] . Unexpectedly , these studies also revealed that there was minimal CNS pathology in WT mice that met euthanasia criteria . It is typically assumed that mice meeting euthanasia criteria do so because of neuroinvasion and subsequent encephalitis . Our data instead indicates that both WT and STING-/- Terminal mice have severe gross GI abnormalities , with corroborating abnormalities by histopathology , which may be the proximate cause of morbundity and meeting euthanasia criteria ( S1 ) . GI complications during WNV have been previously described , however further study is necessary to understand the implications of GI pathology on WNV induced morbidity and mortality [15–17 , 79] . Recently it has been shown that during WNV infection causes delayed GI transit , dependent on infiltrating antiviral CD8+ T cells [80] . Furthermore , both in this model and in a lung model where STING exhibits a gain-of-function mutation , T cell-dependent chronic tissue damage occurs , supporting our findings that STING may play a broad and significant role in communicating between the innate and adaptive immune responses [80 , 81] . Together , these data demonstrate an essential neuroprotective role for STING during WNV infection , potentially through a cellular mediated mechanism instead of the canonical interferon antiviral function typically attributed to STING . WNV typically is cleared through development of an innate immune response and effective T cell immunity [19] . To prevent progression to neuroinvasion , both the innate and adaptive immune response are critical to control WNV viremia and prevent viral induced pathology [19–21 , 24 , 82 , 83] . Because the known function of STING is to initiate a type I IFN response to both PAMPs and DAMPs , we anticipated that the type I IFN response would be diminished both in vivo and in vitro explaining the increased viral loads . Surprisingly , we actually observed an increased inflammatory and antiviral innate immune response in STING-/- mice in the CNS during WNV infection . This same increase in the cytokine-chemokine response was also observed in BMDM ( Fig 3 ) and in serum of infected mice ( Fig 5 ) . These outcomes were highly unexpected as the most commonly described role for STING is known as initiating a type I IFN response [46–48 , 53 , 54] . In particular , STING was shown previously to facilitate the actions of the ELF4 transcription factor to promote type I IFN expression from WNV-infected cells wherein loss of STING associated with reduced IFN and ISG expression ( 49 ) . While we observed significant increases in IFN and ISG expression in BMDM lacking STING , it is likely that STING imparts cell type-specific actions for regulation of innate immune signaling , similar to other pathogen recognition receptors that govern innate immune signaling against WNV , likely explaining this discrepancy between studies [19] . It is also important to note that our studies employed STING-/- mice produced through classical gene targeting approach [48] while the previous study used STINGgt/gt mutant mice produced from N-ethyl-N-Nitrosourea mutagenesis and encoding a T596A point mutation of STING [84] , highlighting that genetic differences between mouse lines might impact findings . Importantly , both mouse lines exhibit increased susceptibility to lethal WNV infection , and together reveal expanded roles for STING in immune regulation during WNV infection . Our data also suggest that STING has a role in controlling WNV replication and tropism , as we found increased viral loads in BMDM , as well as a trend toward increased viral load in the CNS , particularly in the hindbrain regions , but not in the spleens of infected mice lacking STING ( Figs 2 and 3 ) . The trend toward increased virus in the CNS of STING-/- mice could either suggest increased susceptibility of the virus in the CNS , delayed clearance of the virus after entering the CNS , or possibly a combination of the two . Variation observed within strains could be the result of harvesting mice at set time points instead of following them until a determination if they would survive or meet euthanasia criteria , highlighting the potential import of survivorship bias within this model . It does not appear that the requirement for STING in viral control is restricted to neurons or the CNS , as no difference was observed in the viral load of STING-/- primary cortical neurons or intracranial infection ( Fig 2 ) . This outcome suggests that while there is a peripheral requirement for STING in conferring CNS protection , it is not due to complete inhibition of viral control in the periphery . Intriguingly , base-line expression of type I IFN and ISGs were significantly reduced in STING-/- BMDM compared to WT , but not other inflammatory genes ( Fig 3 ) . It is possible that this reduction in baseline IFN allows WNV to establish an earlier and more robust infection , that is later controlled by the RIG-I dependent antiviral response [23 , 34] . However , we favor that STING plays a role in innate immune homeostasis , as in its absence the control of the inflammatory response is lost ( Figs 4 and 7 ) , thus leading to immune-mediated pathology . This function for STING may explain why we had a trend but not significant increase in viral load in the CNS; it is possible that virus is able to establish a stronger infection in the CNS earlier on but is cleared through an exacerbated innate inflammatory and antiviral response in the absence of STING . Alternatively , it is possible that in the absence of STING clearance of the virus takes longer due to an ineffective immune response . Following either of these events subsequent T cell recruitment is likely , but in a manner that leads to enhanced immunopathology and lack of recovery from clinical illness . In addition to its role in mounting a type I IFN response to PAMPs and DAMPs , recent studies demonstrated an essential role for STING in developing antitumor T cell responses [53] . These studies suggested that dead and dying cells are phagocytosed by dendritic cells , which requires STING to present antigen and produce a type I IFN signaling cascade that informs and develops the adaptive immune response . This outcome could also implicate a requirement for STING in microglial-dependent phagocytosis of dead and dying cells , with subsequent STING-dependent polarization and release of soluble factors that effectively recruit and maintain a protective cellular response in the CNS . Upon examining the CNS of infected mice , particularly in STING-/- with ongoing signs , we observed increases in mononuclear cellular infiltrate , implicating possible immunopathology . Previous studies have shown that there is an essential requirement for both CD8+ and CD4+FoxP3+ ( Treg ) T cells to control WNV and prevent immunopathology [42 , 43 , 72] . CD8+ T cells in particular are essential for WNV clearance , however without an adequate Treg response or appropriate balance of CD4+ and CD8+ T cells an uncontrolled cytotoxic T cell response could result in immune mediated pathology . Examining the programming of the adaptive immune response in spleens ( Fig 5 ) we found that expression of Ki67 , CD44 and CD73 in splenic FoxP3+CD4+ Tregs were impaired , implicating a role for STING in the proliferation , activation and suppressive potential of Tregs . Upon examining the brains of mice at baseline ( 4 dpi ) and following infection ( 8 dpi ) , we observed no differences at baseline between WT and STING-/- mice , however the total CD4+ T cells and the CD4/CD8 ratio was significantly decreased in STING-/- mice , suggesting that there is a defective recruitment or maintenance of T cells in the brain ( Fig 6 ) . These data in combination with enhanced CNS pathology suggest that the cytotoxic effect of CD8+ T cells may not be controlled adequately in the absence of STING . It is also possible that increases in cellular response within the CNS recruit an enhanced protective cellular response as a result of viral damage or aberrant immune signaling . Consistent with this either of these options , we found that in STING-/- survivors there were large clusters of CD3+ cells ( Fig 6 ) as well as other cellular infiltrate ( Fig 7 ) in the same vicinity as we observed increased pathology and where STING is localized in the brain ( Fig 7 ) . Recently , a noncanonical STING-dependent signaling pathway was described where multiple cell types initiated an innate immune response following IL1b release in response to mitochondrial DNA release in the cytoplasm [70] . Furthermore , this STING-induced response to IL-1b was essential for the control of dengue virus infection , a flavivirus related to WNV [70] and that this response is linked with protection against WNV neurovirulence in vivo [70 , 83] . Thus , it is intriguing to speculate that noncanonical STING activation in response to proinflammatory cytokine signaling serves to direct immune programming that protects against viral neuroinvasion and CNS pathology during WNV infection . In summary , our study reveals that that STING is required for immune response programming to restrict WNV infection and neuropathogenesis .
All animal experiments were approved by the University of Washington Institutional Animal Care and Use Committee ( IACUC ) guidelines as per protocol #4158–05 and follow the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . Invasive infections and manipulations were performed under anesthesia and every effort was made to limit suffering . C57BL/6J ( WT ) and Tmem173-/- ( STING-/- ) mice were genotyped and bred under specific pathogen-free conditions in the animal facility at the University of Washington . STING-/- mice were gifted by the Stetson lab , who generated them as previously described [67] followed by speed congenics to bring them to a 99 . 4% C57BL/6J background . Additional C57BL/6J ( WT ) mice were purchased from Jackson Laboratories , Bar Harbor , ME . Both male and female mice , ages 8–11 weeks were represented in both the control and infected groups . Mice for primary cortical neurons ( WT and STING-/- ) were set up as timed breeders and embryos were harvested . Mice were monitored daily and assigned a clinical score to describe overall well-being and signs of hind-limb dysfunction ( paresis ) . Clinical scores ( CS ) of ( 0 ) without clinical signs , or ( 1–6 ) dependent on severity of clinical signs presented . CS = 1: ruffled fur , lethargic; no paresis; CS = 2: very mild to mild paresis ( in 1 or more hind limbs with minimal gait disturbance or limb-dysfunction ) ; CS = 3: frank paresis involving at least one hind limb and/or eye conjunctivitis; CS = 4: severe paresis and/or paresis in both hind-limbs; CS = 5: true paralysis; CS = 6: moribund . Additionally , mice were observed daily for the presence or absence of various specific signs . Each mouse was scored as either exhibiting the clinical sign ( YES = 1 ) or not , ( NO = 0 ) . Each sign was monitored through the duration of the experiment and the results were graphed as the average daily score/mouse . Results of clinical signs monitored represent the entire population until they reached euthanasia criteria , at which point the remaining mice continued to be scored until day 18 post infection or study end point . Clinical signs monitored daily include: Lethargy ( L ) , Ruffled fur/decreased of grooming , Hunched , Paresis/Paralysis ( any degree of severity ) , Tremors , Abdominal ( Ab ) distension/GI distress , Loss of Balance , Increased Reflex/Tone in limbs ( fore and/or hind ) and tail , Decreased Reflex/Tone in limbs and tail . The clinical scoring system incorporated signs based off of predicted involvement of different anatomical regions within the CNS and was created using modifications of various previously described scoring systems for experimental autoimmune encephalomyelitis [86–89] . Similar neuroanatomic regions were examined pathologically in an attempt to correlate clinical and neurological phenotype of disease . Subcutaneously-infected mice were monitored for 18 days post infection ( dpi ) . Euthanasia criteria was determined as a clinical score ≥ 5 for 2 or more consecutive days , or 20% loss in body weight . A clinical score of 6 ( moribund ) or respiratory distress resulted in immediate euthanasia . Mice meeting euthanasia criteria were identified as Terminal ( T ) and were euthanized by CO2 asphyxiation followed by cervical dislocation . Mice who did not meet euthanasia criteria were monitored until end point ( 18 dpi ) were identified as Survivors ( S ) . All remaining S mice were euthanized at the end of study ( 18 dpi ) as described above . Mice used for morbidity and mortality analysis were necropsied when meeting euthanasia ( T ) criteria , or study end ( S ) . After euthanasia by CO2 , a complete necropsy was performed and tissues were collected and immersion fixed in 10% neutral buffered formalin [90] . The head was removed and skull cap lifted , leaving the brain within the skull cavity during fixation . The spine was fixed in situ in order to preserve the mesenteric ganglia . Histological preparation hematoxylin and eosin ( H&E ) and immunohistochemical ( IHC ) staining was performed by the UW Histology and Imaging Core ( HIC ) and the Vanderbilt University Medical Center Translational Pathology Shared Resource ( TPSR ) . Primary pathological analysis was performed on the CNS ( brain and spine ) and gastrointestinal ( GI ) tract by a board-certified veterinary pathologist ( PMT ) ( Supplemental methods Table 1 ) . In the brain , the following changes were scored on a subjective 0–4 scale of increasing severity: perivascular inflammation , parenchymal inflammation , hemorrhage , neuronal necrosis , and meningitis . In the spinal cord the presence ( 1 ) or absence ( 0 ) of mononuclear inflammation was documented from 5 different sections of the spine ( C1-C5 , C6-T2 , T3-L3 , L4-S2 , S3 ) for a maximum score of 5 per mouse . For the enteric nervous system ( ENS ) , the degree of mononuclear cells present in the myenteric ganglia , extent to the changes and any secondary GI lesions such as dilation or mucosal change were scores on a on a subjective 0–4 scale of increasing severity . IHC staining of WNV ( VRL W1015 ) and CD3+ T cells ( MCA1477 AbD Serotec ) were performed by the UW Histology Core . VeroWHO ( European Collection of Authenticated Cell Cultures; ECACC ) cells were cultured in Dulbecco’s modified Eagle medium ( DMEM ) supplemented with 10% FBS , 1mM sodium pyruvate , 2mM L-glutamine , antibiotic/antimycotic solution and non-essential amino acids ( complete DMEM; cDMEM ) and split using 0 . 25% Trypsin following PBS wash . HFF cells were kindly gifted from Stetson Lab and were grown in cDMEM . Cells were split using 0 . 05% trypsin following PBS wash . Bone marrow was collected from STING-/- and WT mice and frozen in 10% DMSO/90% FBS . To generate bone marrow derived macrophages ( BMDM ) , bone marrow stocks were thawed , washed and resuspended in cDMEM containing [50 μM] BME and [40ng/mL] murine MCSF ( mMCSF ) . Cells were cultured for 7 days in non-TC coated plates , then scraped , washed with PBS and seeded at 1E6 cells/well in 12-well TC coated plates with cDMEM+BME+mMCSF . Cells were infected or transfected the next day . WNV-TX biological isolates ( 2002 ) were utilized for in vivo work , while WNV-TX ic ( infectious clone ) stocks were utilized for cell culture ( in vitro ) studies . Working stocks were propagated in Vero-E6 ( American Type Culture Collection; ATCC ) and titered by standard plaque assay on VeroWHO and BHK21 ( American Type Culture Collection; ATCC ) cells as previously described [24] . Single-use aliquots from the same viral stock lot were prepared and utilized for all experiments described here . Age and sex-matched 8–11 week old mice were anesthetized by isofluorane and inoculated subcutaneously ( s . c . ) in the right rear footpad with 100 PFU WNV-TX 2002 ( WNV-TX ) diluted in 40 uL PBS , administered via 1mL insulin needle . Mice were monitored daily for clinical score and loss of body weight . Euthanasia criteria was determined as a clinical score ≥ 5 for 2 or more consecutive days , or 20% loss in body weight . A clinical score of 6 ( moribund ) or significant respiratory distress resulted in immediate euthanasia . Mice were anesthetized with ketamine/xylazine , the top of the head was cleaned with EtOH , and the mouse was then restrained manually on a solid surface . The site of injection was approximately halfway between the eye and ear , and just off the midline , in the medial posterior region of the top of the skull . The injection was done with a 29G needle using a Hamilton syringe into the cerebral cortex . Following infection , mice were monitored for revival from anesthesia and monitored daily for clinical score and loss of body weight . Euthanasia criteria was determined as a clinical score ≥ 5 for 2 or more consecutive days , or 20% loss in body weight . A clinical score of 6 ( moribund ) or significant respiratory distress resulted in immediate euthanasia . To determine the viral load from in vivo tissue samples , mice were terminally anesthetized using ketamine/xylazine mixture followed by cardiac perfusion with 30–40 mL PBS . Kidney ( s ) and spleen were collected whole; brains were harvested and macrodissected into four anatomical regions , including the cerebellum , cortex , sub-cortex , and brainstem [91]; spinal cords were collected by perfusion with PBS . Tissues were harvested into 1 mL PBS on ice in Percelly’s tubes with ceramic beads . Following harvest , tissues were homogenized ( Percellys 24 ) 5500/1x 20s/5 min and centrifuged at 4°C/5 min/10k rpm . Supernatant was collected and analyzed by plaque assay on Vero-WHO cells ( 0 . 5% agarose overlay , 3% Neutral red counter stain after five days post inoculation; plaques counted 10-15h post staining ) . Cells were inoculated with WNV in serum-free media and the inoculum left for 1hr rocking at 37°C . Inoculum was removed , cells washed 1x and media replaced with cDMEM . At the indicated time-points , supernatant was collected for virologic and cytokine analysis; cells were treated with RIPA buffer for WB analysis ( or ) with RLT for total cellular RNA isolation . Primary cerebral cortical neuron cultures were generated from E15 WT and STING-/- embryos as previously described [92] and maintained in serum free Neurobasal-A medium ( Life Technologies 21103–049 ) with B27 supplement ( Gibco 17504–044 ) . Neuron cultures were used for virologic experiments after 7 days in vitro . Cortical neuron cultures were infected at MOI 0 . 001 with WNV-TX [32] . Multistep growth curve experiments were performed as described [93] and quantified via plaque assay using BHK21-15 cells . Mice were euthanized in an isoflurane chamber followed by cardiac perfusion with 30–40 mL PBS . Tissues were harvested; right kidney and spleen were collected whole; brains were harvested and macrodissected into four anatomical regions , including the cerebellum , cortex , sub-cortex , and brainstem [91]; spinal cords were collected via PBS perfusion . Tissues were harvested into 1 mL RNALater and stored at 4°C for a minimum of 1 week to stabilize the RNA . Tissues were removed from RNALater solution and transferred to 1 mL TRIreagent in Percelly’s tubes with ceramic beads at RT . Following harvest , tissues were homogenized in a Percelly’s homogenizer ( 5500/1x20s/5 min ) followed by centrifugation ( 4°C/10k rpm/5min ) . RNA isolated with the Ribopure kit from TRIreagent using per manufacturer’s instructions . cDNA was generated from 350 ng RNA using iSCRIPT kits with random primers per manufacturer’s instructions . Cellular and viral gene analysis was assessed by SYBR Green RT-qPCR using an ABI Viia7 and analyzed as the linear fold change ( 2^-dCT ) over a housekeeping gene ( GAPDH ) from WT mock infected sample or mouse ( Table 2 ) . Cells were harvested in RLT and total cellular RNA isolated for RT-qPCR analysis using Qishredders and the Quiagen RNeasy kit per the manufacturer instructions . cDNA was generated from 100 ng total RNA using the iSCRIPT kit per manufacturer instructions using their provided oligo ( dT ) and random primers . Cellular and viral genes were analyzed by SYBR Green RT-qPCR using an ABI ViiA7 . Primers for BMDM experiments described above . Protein extracts from cells were prepared in RIPA buffer . 7–15 ng protein lysate was analyzed by 4–20% gradient SDS-polyacrylamide gel electrophoresis by immunoblotting , using 5% BSA blocking buffer and nitrocellulose membranes . The following antibodies were utilized: WNV NS3 ( R&D BAF2907 ) , Actin ( C4; EMD MAB1501 ) , STAT1 ( CST 9172P ) , STING ( CST D2PZF ) , pSTAT1 ( Y701; CST 58D6 ) , pSTING ( CST D7C3S ) . 8E4 ( or ) 8x10^4 HFF cells were seeded onto glass coverslips in a 24-well plate . The following day , cells were infected with WNV at MOI = 1 or transfected with calf-thymus DNA ( ctDNA; ISD ) ( Thermo Fisher , Waltham , MA , USA ) at 3ug/ml final concentration using Lipofectamine 3000 and following the manufacturer’s protocol . 24h after WNV infection or 3h after ctDNA transfection cells were fixed with 4% paraformaldehyde for 15min at room temperature ( RT ) . Cells were permeabilized with 0 . 1% Triton X-100 for 5min at RT . After blocking the cells for 30min with 3% BSA in PBS , immunofluorescent staining was performed overnight at 4°C with the following primary antibodies: rabbit-anti-STING ( 1:100 , gifted by Glen Barber ) , mouse-anti-dsRNA ( J2 , 1:800 , Scicons , Budapest , Hungary ) . Nuclei were counterstained with 4' , 6-Diamidino-2-Phenylindole , Dihydrochloride ( DAPI , Thermo Fisher ) . Fluorophore coupled secondary antibodies ( Thermo Fisher ) were applied for 1h at RT . After washing with PBS samples were mounted onto glass slides using ProLong Gold ( Thermo Fisher ) . Images were acquired with a Nikon Eclipse Ti confocal microscope equipped with a 60x oil immersion objective using the Nikon confocal software . Insets were captured with 4x enlargement of 600x images . Images were merged and processed using the Nikon confocal analysis software ( Nikon , Melville , NY , USA ) . Mice were euthanized by isoflurane and perfused with 30-40mL PBS to ensure systemic removal of blood and residual intravascular leukocytes . Spleens were homogenized and single cell suspensions were treated with ACK lysis buffer to clear any remaining red blood cells , washed and resuspended in FACS buffer ( 1X PBS , 0 . 5% FBS ) . Cells were plated at 1E6 cells/well and stained for surface markers 15 minutes on ice . Cells were then fixed , permeabilized ( Foxp3 Fixation/Permeabilization Concentrate and Diluent , Ebioscience ) and stained intracellularly with antibodies for 30 minutes on ice . Flow cytometry was performed on a BD LSRII machine using BD FACSDiva software . Analysis was performed using FlowJo software . The following directly conjugated antibodies were used: B515-Foxp3 , B710-CXCR3 , G575-Ki67 , G610-CTA-4 , G666-CD127 , G780-KLRG1 , R660-NS4b Tet , R710-CD45 , R780-CD44 , UV395-CD8 , UV730-CD3 , V450-CD73 , V610-CD4 , V655-CD25 , V510-live/dead . Cells were counted by hemocytometer using trypan blue exclusion . Brains were harvested into RPMI and mechanically suspended using a 70uM strainer . Each brain suspension was added to hypertonic Percoll to create a 30% Percoll solution , vortexed then centrifuged at 1250 rpm for 30 minutes at 4°C . Following centrifugation , the supernatant was aspirated and cell pellet treated with ACK lysis buffer to remove any residual red blood cells . Cells were then washed and filtered through a 70um nylon mesh to remove residual debris and resuspended in FACS buffer . Cells were counted using beads during FACS analysis . Cells were plated at 1E6 cells/well and stained for surface markers 15 minutes on ice . Cells were then fixed and extracellularly stained with antibodies for 30 minutes on ice . Flow cytometry was performed on a BD LSRII machine using BD FACSDiva software . Analysis was performed using FlowJo software . The following directly conjugated antibodies were used for Fig 7C–7I: FITC-CD19 , PerCP-Cy5 . 5-CD103 , PE-CD3e , PE-Cy7-CD4 , APC-WNV Tetramer ( NS4b ) , BV421-CD8a , BV510-CD45 . 2 , BV786-CD44 ( or ) Fig 7K: V510-live/dead , R710-CD45 , UV730-CD3 , UV395-CD8 , V610-CD4 , V655-CD25 , B515-Foxp3 .
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In recent years , outbreaks of emerging and re-emerging neuroinvasive West Nile virus ( WNV ) infection have brought about a critical need to understand host factors that restrict neuropathology and disease . WNV infection in humans typically is either asymptomatic or results in a mild febrile illness , but in some cases virus spreads to the central nervous ( CNS ) causing a more severe form of neuropathological disease . Previous studies established that both innate and adaptive immune responses are essential for controlling WNV disease and restricting the virus from the CNS . In this study , we examined the role of Stimulator of Interferon Genes ( STING ) in conferring host defense during WNV infection in a murine model . Our studies revealed that STING is essential for restricting pathology in the CNS during WNV infection . Further , STING is required for effective programming of the innate and adaptive immune response to WNV . In the absence of STING , aberrant immune development leads to ineffective viral clearance and immunopathology in the CNS . These studies uncover a critical and previously unidentified role for STING in the restriction of WNV that may have broader implications for a role in conferring host defense against RNA viruses .
|
[
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"Methods"
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2019
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STING is required for host defense against neuropathological West Nile virus infection
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Recent genome-wide association ( GWA ) studies described 95 loci controlling serum lipid levels . These common variants explain ∼25% of the heritability of the phenotypes . To date , no unbiased screen for gene–environment interactions for circulating lipids has been reported . We screened for variants that modify the relationship between known epidemiological risk factors and circulating lipid levels in a meta-analysis of genome-wide association ( GWA ) data from 18 population-based cohorts with European ancestry ( maximum N = 32 , 225 ) . We collected 8 further cohorts ( N = 17 , 102 ) for replication , and rs6448771 on 4p15 demonstrated genome-wide significant interaction with waist-to-hip-ratio ( WHR ) on total cholesterol ( TC ) with a combined P-value of 4 . 79×10−9 . There were two potential candidate genes in the region , PCDH7 and CCKAR , with differential expression levels for rs6448771 genotypes in adipose tissue . The effect of WHR on TC was strongest for individuals carrying two copies of G allele , for whom a one standard deviation ( sd ) difference in WHR corresponds to 0 . 19 sd difference in TC concentration , while for A allele homozygous the difference was 0 . 12 sd . Our findings may open up possibilities for targeted intervention strategies for people characterized by specific genomic profiles . However , more refined measures of both body-fat distribution and metabolic measures are needed to understand how their joint dynamics are modified by the newly found locus .
Serum lipids are important determinants of cardiovascular disease and related morbidity [1] . The heritability of circulating lipid levels is estimated to be 40%–60% and recent genome-wide association ( GWA ) studies implicated a total of 95 loci associated with serum high-density lipoprotein cholesterol ( HDL-C ) , low-density lipoprotein cholesterol ( LDL-C ) , total cholesterol ( TC ) , and triglyceride ( TG ) levels [2] . Currently identified common variants explain 10%–12% of the total variation in lipid levels , corresponding to ∼25% of the trait heritability [2] . Epidemiological risk factors , such as alcohol consumption , smoking , physical activity , diet and body composition are known to affect lipid levels [3]–[5] . These risk factors also show moderate to high heritabilities , and over 120 loci with genome-wide significant association have been identified ( http://www . genome . gov/26525384 ) . To better understand the biological processes modifying lipid levels , several twin studies [6]–[8] and candidate gene studies [9]–[14] have tested for interactions between genes and epidemiological risk factors . Interactions between genes and modifiable risk factors might help us develop new lifestyle interventions targeted to susceptible individuals based on their genetic information . The effects of genetic loci and risk factors have been studied widely separately , but to date no GWA studies for interactions on lipids have been reported .
We conducted a genome-wide screen for interactions between 2 . 5 million genetic markers and sex , lifestyle factors ( smoking and alcohol consumption ) , and body composition ( BMI and WHR ) in association to serum lipid levels ( TC , TG , HDL-C , and LDL-C ) in 18 population-based cohorts ( max N = 32 , 225; Table S1A , Text S1 ) . We defined interaction as a departure from a linear statistical model allowing for the additive main effects of both the SNP and the epidemiological risk factor . 18 SNPs with suggestive interactions for at least one of the trait – epidemiological factor combinations ( P-value for the interaction <10−6 ) in stage 1 analyses were taken forward to stage 2 analysis in eight additional cohorts ( max N = 14 , 889; Table S1B , Text S1 ) . In inverse variance meta-analyses combining the results from stage 1 and stage 2 ( Table S2 ) , the interaction between rs6448771 in chromosome 4p15 and WHR on TC ( Figure 1 ) was statistically genome-wide significant ( stage 1 and 2 combined P = 9 . 08×10−9 ) . This interaction was tested in stage 3 in two further cohorts ( N = 7 , 813; Table S1C , Text S1 ) , which showed an effect to the same direction . After combining results from all three stages ( total N = 43 , 903 ) , the P-value for interaction was 4 . 79×10−9 . The association between WHR and TC was strongest for individuals carrying two G alleles of rs6448771 , for whom a one standard deviation ( sd ) difference in WHR corresponds to 0 . 19 sd difference ( confidence interval 0 . 13–0 . 25 ) in TC concentration , while for individuals homozygous for the A allele the difference was 0 . 12 sd ( confidence interval 0 . 09–0 . 16 ) ( Table S3A , Figure S1 ) . The effect corresponds to 0 . 5% and 0 . 2% of the total variance explained in a cohort of young individuals ( YFS , mean age = 37 . 6 ) and an old cohort ( HBCS , mean age = 61 . 49 ) , respectively . Additionally , when looking at the effect of the SNP on TC in WHR tertiles , the estimates differed in a way that the estimated SNP effect is higher for the individuals with higher WHR ( Table S3B ) . The SNP did not have a direct effect on either TC or WHR ( P = 0 . 46 and P = 0 . 51 , respectively , Figure 1 ) . The SNP rs6448771 is located 249 kb downstream of the protocadherin 7 ( PCDH7 ) gene . Since the polymorphisms associated with complex phenotypes often influence gene expression , we examined whether individuals carrying different genotypes of rs6448771 have variation in their transcript profiles . As WHR reflects adipose tissue function , we selected 54 individuals from Finnish dyslipidemic families with available fat biopsies and GWA data . We used linear regression to find genes that were differentially expressed in adipose tissue depending on the rs6448771 genotype . We found two potential candidate genes with nominally significant cis-eQTL effects , PCDH7 ( P = 0 . 027 , distance from the rs6448771 250 kb ) and CCKAR ( P = 0 . 017 , distance from the SNP 4 . 9 Mb ) . The region with CCKAR has previously been linked with obesity [15] . Additionally , using Ingenuity software ( IPA ) , we conducted a pathway analysis for genes with eQTL P-value<0 . 01 ( both trans- and cis-eQTLs ) . Among other diverse IPA-defined biological functions , there was an eQTL association enrichment among genes belonging to the ‘degradation of phosphatidylcholine’ ( 3 genes out of 6 , P = 6 . 64×10−5 , Benjamini-Hochberg corrected P = 0 . 0138 ) and ‘degradation of phosphatidic acid’ ( 4 genes out of 8 , P = 4 . 71×10−4 , B-H corrected P = 0 . 0349 ) functions , which are members of broader defined IPA categories “Lipid Metabolism” and “Carbohydrate Metabolism” . These pathways were up-regulated in individuals carrying the G allele of rs6448771 , possibly indicating a role for rs6448771 in lipid and carbohydrate metabolism . The associated SNP also shows evidence for interactions with WHR on LDL-C ( effect estimate for the interaction = 0 . 03 , P = 0 . 0016 ) and HDL-C ( effect estimate = 0 . 02 , P = 0 . 029 ) in our stage 1 meta-analysis and after adjusting for TC no residual interaction effect on LDL-C and a little on HDL-C remains ( P = 0 . 834 and P = 0 . 131 respectively ) when testing in data subset . Therefore we tested the SNP – WHR interaction also on a range of lipoprotein subclasses measured using NMR metabonomics platform [16] available in two cohorts ( NFBC1966 , N = 4624 mean age = 31 . 0; YFS , N = 1889 , mean age = 37 . 6 ) . The results show that the SNP has a positive interaction effect on large HDL particle concentration ( combined effect for the interaction = 0 . 538 , P = 0 . 0186 ) and a negative effect on large very-low-density lipoprotein ( VLDL ) particles ( combined effect = −0 . 466 , P = 0 . 0291 ) and total triglycerides ( combined effect = −0 . 454 , P = 0 . 0343 ) ( Figure 2 ) .
Our genome-wide scan for interactions between SNP markers and traditional epidemiological risk factors in population-based random samples found a genome-wide significant locus , rs6448771 , modifying the relationship between WHR and TC . The effect of WHR is estimated to be 64% stronger for individuals carrying two copies of the G allele than for individuals carrying two A alleles . The interaction explains around half a percent of the TC variance that is in par with the main effects of the strongest previously identified TC SNPs individually . This SNP also shows similar interaction effects on a cascade of more detailed lipid fractions suggesting broad involvement in lipid metabolism , which was also suggested by our eQTL association enrichment analysis with adipose tissue expression data . The eQTL analysis pointed towards two potential candidate genes in the region . The first one of these was protocadherin 7 ( PCDH7 ) gene , which produces a protein that is thought to function in cell-cell recognition and adhesion . The other candidate gene , cholecystokinin A receptor ( CCKAR ) regulates satiety and release of beta-endorphin and dopamine in the central and peripheral nervous system . It has been previously shown that rats with no expressed CCKARs developed obesity , hyperglycemia and type 2 diabetes [17] . To test whether our eQTL finding was adipose tissue specific , we ran the eQTL analysis for PCDH7 and CCKAR in another dataset with genome wide expression data from blood leukocytes ( N = 518 ) available . CCKAR could not be tested due to its negligible expression in blood leukocytes , and no association was found for the PCDH7 ( P-value = 0 . 284 ) gene most likely indicating an adipose tissue specific eQTL for PCDH7 as a function of rs6448771 . One interesting aspect of this study , given our large sample size , is that only one signal achieved genome-wide significance , where previously published lipid GWA studies have found close to a hundred . Although power to detect interaction is typically lower than for main effects , especially for rare exposures and SNPs , several of the exposures considered here ( such as WHR , BMI , and gender ) were common and available for a large proportion of the study sample . This suggests that the contribution of two-way G×E interactions to lipid levels , at least for the risk factors we examined , is rather small , or that our current measures of risk factors may not be robust enough for identifying interactions . More specific measures of both phenotypes and interacting risk factors would give better statistical power in future screens of G×E interactions . Our findings allow us to draw several conclusions . First , to our knowledge , this is the first time an interaction between a genetic loci and a risk factor has been identified in a genome-wide scan using a stringent statistical threshold for genome-wide significance . Second , in our samples , rs6448771 modified the relationship between WHR and TC , but was not associated with either WHR or TC alone . This observation suggests that genome-wide screens for interactions may be complementary to the current large-scale GWAS efforts for finding main effects . Third , in addition to careful harmonization of both risk factor data and phenotypes , large sample sizes are needed to identify interactions . In our study , 43 , 903 samples were combined to robustly identify the interaction . Our data , however , suggest that the contribution of G×E interaction using current phenotypes appears limited . Finally , from clinical point of view , the interaction may open up possibilities for targeted intervention strategies for people characterized by specific genomic profiles but more refined measures of both body-fat distribution and metabolic measures are needed to understand how their joint dynamics are modified by the newly found locus .
18 studies , with a combined sample size of over 30 , 000 individuals , participated in the discovery phase of this analysis; 8 studies were available for replication with over 14 , 000 individuals . In the discovery stage , only population-based cohorts not ascertained on the basis of phenotype , with a wide variety of well-defined epidemiological measures available , were included . In the replication datasets , the NTR cohort was selected on the basis of low risk for depression and the Genmets samples were selected for metabolic syndrome . In further replication of rs6448771 , the EPIC cases were ascertained by BMI . Descriptive statistics for these populations are detailed in Table S1A ( discovery ) , S1B ( replication ) and S1C ( further replication ) . Brief descriptions of the cohorts are provided in the Text S1 section “Short descriptions of the cohorts” . Individuals were excluded from analysis if they were not of European descent or were receiving lipid-lowering medication at the time of sampling . TC , HDL-C , and TG concentrations were measured from serum or plasma extracted from whole blood , typically using standard enzymatic methods . LDL-C was either directly measured or estimated using the Friedewald Equation ( LDL-C = TC – HDL-C – 0 . 45×TG for individuals with TG≤4 . 52 mmol/l , samples with TG level higher than 4 . 52 were discarded in the calculation of LDL-C ) [18] . Covariates and epidemiological risk factors were ascertained at the same time that blood was drawn for lipid measurements . BMI was defined as weight in kilograms divided by the square of height in meters . Waist circumference was measured at the mid-point between the lower border of the ribs and the iliac crest; hip circumference was measured at the widest point over the buttocks . Waist-to-hip ratio was defined as the ratio of waist and hip circumferences . Alcohol consumption and smoking habits were determined via interviews and/or questionnaires . Both behaviors were coded as dichotomous ( abbreviations: ALC for drinker/abstainer and SMO for current smoker/current non-smoker ) and semi-quantitative traits . Semi-quantitative alcohol usage ( ALCq ) was based on daily consumption in grams ( 0: 0 g/day; 1: >0 and ≤10 g/day; 2: >10 and ≤20 g/day; 3: >20 and ≤40 g/day; 4: >40 g/day ) . Semi-quantitative smoking ( SMOq ) was assessed based on the number of cigarettes per day ( 0: 0 cigarettes/day; 1: >0 and ≤10 cigarettes/day; 2: >10 and ≤20 cigarettes/day; 3: >20 and ≤30 cigarettes/day; 4: >30 cigarettes/day ) . Affymetrix , Illumina or Perlegen arrays were used for genotyping in the discovery cohorts . Each study filtered both individuals and SNPs to ensure robustness for genetic analysis . After quality control , these data were used to impute genotypes for approximately 2 . 5 million autosomal SNPs based on the LD patterns observed in the HapMap 2 CEU samples . Imputed genotypes were coded as dosages , fractional values between 0 and 2 reflecting the estimated number of copies of a given allele for a given SNP for each individual . Cohort specific details concerning quality control filters , imputation reference sets and imputation software are described in Table S4 . Replication cohorts utilized genome-wide imputed data , as described above , where available . Details on the genotyping methods implemented in the replication samples are described in Table S4 . Proton NMR spectroscopy was used to measure lipid , lipoprotein subclass and particle concentrations in native serum samples . NMR methods have been previously described in detail [16] , [19] . Serum concentrations of total triglycerides ( TG ) , total cholesterol ( TC ) together with LDL-C and HDL-C were determined . In addition , total lipid and particle concentrations in 14 lipoprotein subclasses were measured . The measurements of these subclasses have been validated against high-performance liquid chromatography [20] . The subclasses were as follows: chylomicrons and largest VLDL particles ( particle diameters from approx 75 nm upwards ) , five different VLDL subclasses: very large VLDL ( average particle diameter 64 . 0 nm ) , large VLDL ( 53 . 6 nm ) , medium-size VLDL ( 44 . 5 nm ) , small VLDL ( 36 . 8 nm ) , and very small VLDL ( 31 . 3 nm ) ; intermediate-density lipoprotein ( IDL ) ( 28 . 6 nm ) ; three LDL subclasses: large LDL ( 25 . 5 nm ) , medium-size LDL ( 23 . 0 nm ) , and small LDL ( 18 . 7 nm ) ; and four HDL subclasses: very large HDL ( 14 . 3 nm ) , large HDL ( 12 . 1 nm ) , medium size HDL ( 10 . 9 nm ) , and small HDL ( 8 . 7 nm ) . Triglyceride concentrations were natural log transformed prior to analysis . BMI and WHR were transformed to normality using inverse-normal transformation of ranks . For analyses where sex was the epidemiological variable of interest , the phenotypes were defined as the rank-inverse normal transformed residuals resulting from the regression of the lipid measurement on age and age2 . For the other analyses , the phenotypes were defined as the inverse normal transformed residuals resulting from the regression of the lipid measurement on age , age2 , and sex . Associations between the transformed residuals and epidemiological risk factors/SNPs were tested using linear regression models under the assumption of an additive ( allelic trend ) model of genotypic effect . The models regressed phenotypes on epidemiological factor , SNP , and epidemiological factor×SNP termsand tested if the effect for E×SNP was 0 using 1 df Wald tests . In family-based cohorts , linear mixed modeling was implemented to control for relatedness among samples [21] . Analysis software used by the individual cohorts is described in Table S1A and S1B . The interaction terms from the regression analyses were meta-analyzed using inverse variance weighted fixed-effects models [22] . Prior to meta-analysis , genomic control correction factors ( λGC ) [23] , calculated from all imputed SNPs , were applied on a per-study basis to correct for residual bias possibly caused by population sub-structure . Meta-analyses were performed by two independent analysts using METAL ( http://www . sph . umich . edu/csg/abecasis/Metal/index . html ) and the R [24] package MetABEL ( part of the GenABEL suite , http://www . genabel . org/ ) . All results were concordant , reflecting a robust analysis . Results were selected for in silico replication if the meta-analysis P-value was less than 10−6 . Results passing the threshold of suggestive genome-wide association ( P-value ≤5×10−7 ) were selected for further replication by direct genotyping . The commonly accepted genome wide level of significance ( 5×10−8 ) reflects the estimated testing burden of one million independent SNPs in samples of European ancestry [25] . To address the multiple testing arising from testing interactions with multiple risk factors , we set the genome wide significance threshold to 5×10−8/3 = 1 . 67×10−8 corresponding to three principal components explaining 97 . 8% of the total variation of the risk factors ( Table S5 ) .
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Circulating serum lipids contribute greatly to the global health by affecting the risk for cardiovascular diseases . Serum lipid levels are partly inherited , and already 95 loci affecting high- and low-density lipoprotein cholesterol , total cholesterol , and triglycerides have been found . Serum lipids are also known to be affected by multiple epidemiological risk factors like body composition , lifestyle , and sex . It has been hypothesized that there are loci modifying the effects between risk factors and serum lipids , but to date only candidate gene studies for interactions have been reported . We conducted a genome-wide screen with meta-analysis approach to identify loci having interactions with epidemiological risk factors on serum lipids with over 30 , 000 population-based samples . When combining results from our initial datasets and 8 additional replication cohorts ( maximum N = 17 , 102 ) , we found a genome-wide significant locus in chromosome 4p15 with a joint P-value of 4 . 79×10−9 modifying the effect of waist-to-hip ratio on total cholesterol . In the area surrounding this genetic variant , there were two genes having association between the genotypes and the gene expression in adipose tissue , and we also found enrichment of association in genes belonging to lipid metabolism related functions .
|
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2011
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A Genome-Wide Screen for Interactions Reveals a New Locus on 4p15 Modifying the Effect of Waist-to-Hip Ratio on Total Cholesterol
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A rich body of empirically grounded theory has developed about food webs—the networks of feeding relationships among species within habitats . However , detailed food-web data and analyses are lacking for ancient ecosystems , largely because of the low resolution of taxa coupled with uncertain and incomplete information about feeding interactions . These impediments appear insurmountable for most fossil assemblages; however , a few assemblages with excellent soft-body preservation across trophic levels are candidates for food-web data compilation and topological analysis . Here we present plausible , detailed food webs for the Chengjiang and Burgess Shale assemblages from the Cambrian Period . Analyses of degree distributions and other structural network properties , including sensitivity analyses of the effects of uncertainty associated with Cambrian diet designations , suggest that these early Paleozoic communities share remarkably similar topology with modern food webs . Observed regularities reflect a systematic dependence of structure on the numbers of taxa and links in a web . Most aspects of Cambrian food-web structure are well-characterized by a simple “niche model , ” which was developed for modern food webs and takes into account this scale dependence . However , a few aspects of topology differ between the ancient and recent webs: longer path lengths between species and more species in feeding loops in the earlier Chengjiang web , and higher variability in the number of links per species for both Cambrian webs . Our results are relatively insensitive to the exclusion of low-certainty or random links . The many similarities between Cambrian and recent food webs point toward surprisingly strong and enduring constraints on the organization of complex feeding interactions among metazoan species . The few differences could reflect a transition to more strongly integrated and constrained trophic organization within ecosystems following the rapid diversification of species , body plans , and trophic roles during the Cambrian radiation . More research is needed to explore the generality of food-web structure through deep time and across habitats , especially to investigate potential mechanisms that could give rise to similar structure , as well as any differences .
Perhaps the most fundamental property of life is its ability to use energy and materials to maintain and reproduce itself , in turn providing energy and materials to support more life . This generation and consumption of biomass enabled the evolution of biological diversity and concomitant trophic structure among early metazoan ecosystems as documented in Cambrian fossil assemblages of the early Paleozoic [1–3] . Whereas virtually all phylum-level body plans first appeared and rapidly diversified by the Middle Cambrian [4 , 5] , several researchers have suggested that shifts in dominant taxa with different functional forms across the Phanerozoic reflect fundamental differences in trophic structure between ancient and more recent ecosystems . For example , dominant marine fauna shifted from trilobites and inarticulate brachiopods in the Cambrian , to articulate brachiopods , bryozoans , and stalked echinoderms in the post-Cambrian Paleozoic , to molluscs in the post-Palaeozoic [6–8] . The ratio of motile to nonmotile animal genera across the Phanerozoic suggests that the prevalence of taxa with different trophic roles was relatively stable at different levels over four long intervals interspersed by rapid transition periods , with higher proportions of nonmotile genera in the early Paleozoic compared with the Cenozoic [9] . More specific to this study , it has been suggested that Cambrian marine communities may have lacked secondary and higher-order predators that are present in modern ecosystems [1] . In contrast , other researchers have hypothesized that modern trophic structure including higher-order predators may have emerged in the Early Cambrian , as diversification of phytoplankton created new opportunities for the evolution and diversification of zooplankton and larger invertebrates , driving rapid expansion of ecological interactions , particularly those related to feeding [10] . We explored whether trophic organization as characterized by food-web structure appears to have undergone substantial change from the early to the recent Phanerozoic . We present well-resolved data on trophic interactions within Cambrian assemblages and analyze these data in the context of current food-web data and theory , and with regard to uncertainty in diet designations . Recent food-web data from diverse aquatic and terrestrial habitats [11–14] have supported the development of simple models that formalize and successfully predict food-web structure [15–18] , comparative analyses of empirical food-web structure [19–25] , and models that explain variability in basic food-web properties such as connectance [26] . Extending such approaches to ancient ecosystems has been hampered by assumptions that the fossil record is either too incomplete or lacking in evidence of trophic interactions to generate detailed , species-level food-web data of comparable resolution to modern webs [27–29] . However , increasingly comprehensive taxonomic and autecological analyses of select fossil assemblages with excellent soft-body preservation across a wide range of taxa present new opportunities for compiling well-resolved ancient food-web data . Such data , coupled with careful assessments of uncertainty , can provide the basis for quantitative paleo food-web analyses that extend beyond prior guild-based probabilistic approaches [29] , particularly for analyses that are not dependent on abundance information or fine-scale temporal or spatial resolution . We compiled detailed information on taxa and the likely trophic relationships among them for the Lower Cambrian Chengjiang Shale ( ∼520 million years ago ( Ma ) ) of eastern Yunnan Province , China , and the Middle Cambrian Burgess Shale ( 505 Ma ) of British Columbia , Canada . Each of these “Conservation Lagerstätten” contains extensive soft-bodied preservation of benthic and nektonic marine invertebrates and some early chordates in the Chengjiang . Preservation is exquisite , allowing details of soft-part anatomy to be examined to the level of individual setae on polychaete annelids and gill filaments on arthropods . The morphology of the Cambrian taxa suggests that they occupy multiple trophic levels , and the record contains many types of evidence to infer consumer–resource relationships [10] . Although systematic preservational biases do exist in each assemblage [30] , these are not obviously different than methodological biases affecting recent food-web data . Modern datasets , collected by different researchers for various purposes , always exclude some taxa—particularly those that are cryptic , rare , or small—and most webs have uneven resolution of included taxa , with a tendency towards aggregation of lower trophic level taxa [31] . The observation of shared patterns and trends in network structure across modern webs [15–21] suggests that many aspects of food-web structure produce a strong enough signal to rise above some degree of noise , incompleteness , and bias in the data . These types of studies , and the one presented here , seek to elucidate and compare the basic architecture , or “the most universal , high-level , persistent elements of organization” [32] of ecological trophic networks . The Cambrian data are necessarily cumulative over time and space , because fine temporal and spatial resolution is not achievable . However , both assemblages were deposited in relatively brief stratigraphic intervals and represent species that could potentially interact due to their likely coexistence within particular pelagic and benthic marine habitats . Modern food-web data range from cumulative [11 , 33] to finely resolved in time ( e . g . , seasonal webs ) [34] and/or space ( e . g . , patch-scale webs ) [35] . The generally implicit assumption underlying cumulative food-web data is that the set of species in question coexist within a habitat , and representatives of those species have the opportunity over some span of ecological time to interact directly . To the degree possible , such webs provide a cumulative documentation of who eats whom among species within a habitat over multiple years , including interactions that are low frequency or represent a small proportion of consumption . Such cumulative webs are used widely for comparative research to look at whether there are regularities in food-web structure [15–26 , 31] . More narrowly defined webs at finer temporal or spatial scales or that use strict evidence standards ( e . g . , identifying trophic interactions via gut contents only ) have been useful for characterizing how such constraints influence perceived structure within habitats [35 , 36] but are generally not used to look for cross-system regularities in network structure . The Cambrian web data assembled here appear comparable to modern cumulative web data and are thus appropriate for analysis of cross-system regularities , even given the somewhat broader temporal scales of the Cambrian data . Two recent studies used a network modeling approach to explore the potential for secondary extinctions in response to perturbations in ancient food webs [28 , 29] . The second study incorporated an empirical component by generating sets of possible Permian and Triassic food webs from “metanetwork” data based on relatively coarse assignments of trophic interactions among guilds of species [29] . Specific food webs were stochastically drawn from those metanetworks using a range of link distributions observed in modern food webs [21 , 29] . In contrast , the present study uses more highly resolved diet designations coupled with explicit quantification of uncertainty associated with those designations and makes no assumption that central aspects of food-web structure such as link distributions are similar between ancient and modern webs . Instead , we test the validity of such assumptions for early Phanerozoic communities , with an explicit focus on key methodological issues including whether the Cambrian food-web data presented have comparable levels of resolution to modern food-web data , and whether the observed Cambrian network structure and associated comparisons with recent web structure are sensitive to uncertainty and possible errors in the data .
Based on available taxonomic and trophic information ( Tables S1–S5 ) , we assembled a food web with 85 taxa for the Chengjiang Shale and one with 142 taxa for the Burgess Shale ( Figure 1 and Tables S6 and S7 ) . We refer to these as “original-species webs . ” All but five taxa in each original-species web were identified to the species level ( Tables S6 and S7 ) . When species with identical consumers and resources within each original-species web are aggregated into trophic species [37] , the resulting “trophic-species webs” have 33 taxa ( Chengjiang ) and 48 taxa ( Burgess ) ( Figure 1 , Table 1 , and Tables S8–S10 ) . Trophic species are used in comparative food-web studies to reduce methodological and statistical variation due to uneven resolution [37] and insufficient sampling [38] of taxa within and among food webs , and to focus comparative analyses on functionally distinct components of food webs [15] . Following convention [15–26] , we focused our analyses on trophic-species webs . The number of taxa in a food web , denoted by S , is a simple measure of diversity sometimes referred to as species richness . Connectance , denoted by C , is a simple measure of food-web complexity calculated as L/S2 [11] , which quantifies the proportion of possible feeding links L among S taxa that are actually realized . C = 0 . 091 and 0 . 108 for the Chengjiang and Burgess Shale trophic-species food webs , respectively ( Table 1 ) . Subsequently , we refer to these trophic-species food webs as the Chengjiang web and the Burgess web , or collectively as the Cambrian webs . The diversity , complexity , and resolution of the Cambrian webs fall within what is observed for modern webs ( Table 1 ) [31] . The number of taxa in modern trophic-species webs used in recent comparative analyses ranges from ∼25 to 170 [31 , 39] . The eight modern webs used in this study were selected to represent similar S to the Cambrian webs , with a range of 25–50 taxa . The connectance of the Cambrian webs falls within the range of those webs ( 0 . 071–0 . 315 ) and is similar to mean C across larger sets of modern webs ( ∼0 . 10–0 . 15 ) [39] . Mean trophic levels ( TLs ) for the Chengjiang and Burgess webs ( 2 . 84 and 2 . 72 , respectively ) fall in the middle of the range for the eight modern webs ( 1 . 95–3 . 20 ) ( Table 1 ) . With over 90% of taxa identified to the species level , the Cambrian original-species webs have higher taxonomic resolution than many modern webs . The Cambrian trophic-species webs have 39% ( Chengjiang ) and 34% ( Burgess ) the number of taxa in the original-species webs , which is comparable to aggregation levels for similarly well-resolved modern webs [31] . Poorly resolved original-species webs with coarse taxonomic or trophic categories undergo less trophic-species aggregation , because similar taxa are already grouped . Each trophic link in the Cambrian webs represents a hypothesis about a feeding relationship based on one or more lines of evidence ( Tables S1 , S2 , S6 , and S7 ) . Based on the quality of the evidence ( see Materials and Methods section Determination of trophic roles of Cambrian taxa for examples ) , we assigned a certainty level of 1 ( possible ) , 2 ( probable ) , or 3 ( certain ) to each link . Of 559 links among 85 taxa in the original-species Chengjiang web , 4 . 7% are considered certain , 28 . 3% probable , and 67 . 1% possible . Of 771 links among 142 taxa in the original-species Burgess web , <1% are considered certain , 53 . 2% probable , and 46 . 7% possible . When the original taxa are aggregated into trophic species , certainty is calculated as the average of the certainty of the aggregated links ( Table S10 ) . As a result , fewer links are “low-certainty , ” which we defined as <1 . 5 for our analyses . Of 99 and 249 links in the trophic-species versions of the Chengjiang and Burgess webs , 59 . 6% and 37 . 3% are low certainty , compared with 67 . 0% and 46 . 7% of links in the original-species webs . We tested for systematic bias in the distribution of uncertain links , one source of likely errors in the Cambrian food-web data , by conducting a sensitivity analysis to explore whether the exclusion of low-certainty links and comparable numbers of random links affects Cambrian food-web structure . As increasing proportions of low-certainty or random links were removed , S , C , and L/S declined slowly ( Figure 2 ) . Differences in responses to random versus low-certainty link removals were generally small , particularly at less extreme removal levels . In both webs , removal of 30% of total links resulted in the loss of ∼2–3 species , a drop in C of ∼0 . 02 , and a drop in L/S of <1 ( Chengjiang ) and ∼1 ( Burgess ) . Most of the 17 other topological properties , described in more detail in the Materials and Methods section Network structure properties , also changed relatively little ( i . e . , percent change < ±20% ) with the removal of up to 30% of links , regardless of the web or type of link removal ( Figure 3 ) . It was not until extensive removals of more than a third of total links that more than half of the properties exhibited changes greater than ±20% . We feel that it is unlikely that all or even most of the low-certainty links are incorrect; therefore , we suggest that the salient feature is the relative insensitivity of Cambrian network structure to removal of up to ∼30% of total links , equivalent to ∼80% of low-certainty links in the Burgess web and ∼50% of such links in the Chengjiang web .
The abundance distributions of taxa in marine ecosystems across the Phanerozoic suggest that after the end-Permian extinction , the occurrence of complex species assemblages increased , i . e . , assemblages with elevated diversities of mobile and infaunal taxa compared to simpler assemblages dominated by sessile epifaunal suspension feeders [56] . From this point of view , the Chengjiang and Burgess assemblages appear to be early examples of complex metazoan communities . While the occurrence of such complex multi-trophic level communities may have significantly increased in the post-Paleozoic , our results suggest that most aspects of their basic trophic structure were largely in place by the early Paleozoic , despite major changes in the relative dominance of various trophic habits and functional roles [6–9] . The results presented here and elsewhere [15–19 , 31] suggest that food-web structure , the way that species are organized in terms of their feeding interactions , appears largely independent of the particular identities , morphologies , trophic habits , evolutionary histories , and environmental contexts of species in ecosystems . The question of why food webs across habitats and deep time appear to share a similar architecture that scales systematically with the numbers of species and links in the webs remains open , and may relate to thermodynamic , dynamical stability , and/or evolutionary constraints . The patterns and potential mechanisms of both similarities and differences deserve further research . The notion that “familiar types of community structure” emerged in the Cambrian was suggested in the mid twentieth century by the eminent ecologist G . Evelyn Hutchinson [57] . Our research indicates that quantitative analysis of the structure of trophic interactions throughout the Phanerozoic is possible and can help in the study of important macroevolutionary questions [28 , 29] , although it requires thoughtful treatment of data scale-dependence , uncertainty , resolution , and incompleteness . For example , carefully selected datasets could indicate whether mass extinctions break patterns of incumbency in trophic complexity and force the construction of new community structures , and whether those new structures converge on the apparently conserved patterns of species interactions suggested by the current and related analyses . However , the strong , scale-dependent similarity of most aspects of network structure between Cambrian and modern webs suggests that such changes over deep time may be subtle and may not exceed variation already documented across modern webs . Convincing demonstration of significant and systematic changes will require careful compilation and analysis of high-quality datasets . Nevertheless , quantitative analysis of ancient food webs promises to open novel areas of research that synthesize studies of structure , function , dynamics , and constraint at the intersection of ecology , evolution , and thermodynamics .
Data relating to species and feeding links are found in Tables S1–S10 . We compiled lists of 138 taxa for the Chengjiang Shale [58] and 171 taxa for the Burgess Shale [59] ( Tables S1–S3 ) . In addition to species found explicitly in the fossil record , we assumed that five taxa variously used in many modern estuarine and marine food-web datasets [34 , 60] were present in both Cambrian ecosystems [61 , 62]: phytoplankton , bacterioplankton , suspended organic matter , benthic detritus , and zooplankton . We refer generically to parasite , predator , herbivore , and detritivore taxa as “consumers , ” and anything that they feed on as “resources . ” We present information regarding the ecology of Cambrian taxa in Tables S1 and S2 in the columns labeled “Trophic Role , ” “Position , ” and “Evidence for Trophic Role . ” This information was used to assign feeding links among taxa and to assign certainty levels to those links ( Tables S6 and S7 ) . Each feeding link was assigned a certainty level of 1 ( possible ) , 2 ( probable ) , or 3 ( certain ) , based on a subjective estimate of the strength of the lines of evidence . We discuss some general aspects and give a few specific examples of determination of trophic roles and certainty levels here . Direct evidence for food preferences is preserved in a few taxa and is described in original species descriptions as cited ( Tables S1 and S2 ) . For example , some fossils of the priapulid worm Ottoia prolifica have preserved gut contents , allowing assignation of a link to Haplophrentis carinatus with the highest level certainty ( certainty = 3 ) . However , in the majority of cases , characterization of feeding strategies is inferential . In some cases , the assignment of trophic roles was based on examination of particular morphologic attributes , particularly mouthparts , limb morphology and such predatory features as large eyes . Thus , trilobite feeding roles were assigned based on knowledge of the appendages and the structure of the hypostome , with certainty values of 1 and occasionally 2 . We assumed that most predators could feed upon smaller prey available in the same habitat zone . In other cases , particularly for detritus feeding for infaunal forms , the inference is based on eliminating other possibilities , such as predation , based on an absence of apparent predatory features such as grasping spines . For the groups algae , porifera , cnidaria , ctenophora , and brachiopoda , trophic roles are based on knowledge of the characteristics of descendents of the same clade and an assumption of phylogenetic conservation or modern analogs where strongly plausible . Those inferences , assigned certainty levels of 1 or 2 , are relevant for only five taxa in the trophic-species versions of each Cambrian web , and account for 11% and 3% of the links in the Chengjiang and Burgess webs , respectively . As mentioned , algae are assumed to have been photosynthetic based on analogy to modern algae . It has been argued persuasively that herbivory was unlikely in early Palaeozoic ecosystems [63] , and despite the abundance of algae , the only possible herbivore we recognized in the Burgess Shale was Wiwaxia . Modern sponges , although likely polyphyletic , feed on bacteria and dissolved organic matter . Based on the phylogenetic similarity to extant clades of Chengjiang and Burgess Shale sponges , it seems safe to assume a similar ecology ( certainty = 2 ) . Similarly , modern cnidarians and ctenophores are micro-carnivores , and the morphology of the fossil clades provides no basis for arguing that this was not also true of their Cambrian predecessors . Modern brachiopods are largely suspension feeders , and while some may be selective in the categories of food they remove from the water column [64] , the modern clades date to the Cambrian , suggesting this ecology is a synapomorphy for the clade ( certainty = 2 ) . For the onycophorans Aysheaia and Hallucigenia , we accepted previous claims that they fed on sponges based on the ecological associations , but since this may not indicate a trophic relationship , we assigned these links a certainty value of 1 . Inferring the trophic roles of the annelids is more difficult , but is based on head morphology and the presence or absence of a sediment-filled gut . The last is frankly a tricky criterion . In the past the presence of a sediment-filled gut has been taken as evidence of deposit feeding [59] and its absence as evidence of a more selective food gathering . However the validity of this inference is unclear [62 , 65] . Arthropods form the bulk of both faunas , both numerically and taxonomically . Fortunately , arthropod limb morphology is often a very useful indicator of trophic role , although it is not always unambiguous . As noted in Tables S1 and S2 , we used gut morphology [62]; appendage morphology , particularly the presence of diagnostic structures such as spines or grasping appendages; and mouthpart morphology such as that of Anomalocaris or the hypostome of trilobites . Brachiocaris evidently lacks eyes , was epibenthic , and has apparent claws on the anterior appendages; from this we infer that it was a scavenger or feeding on sessile animals . The trophic role of the trilobites was based upon the analysis of Fortey and Owen [66] with the assistance of Hughes [67] . For many of the predatory arthropods , we assumed they could feed on smaller arthropods , unless highly specialized appendages were present ( certainty = 1 ) . For the larger arthropods , we also assumed that they would be feeding on the younger forms of other large arthropods . Thus Sancticaris is shown as feeding on Anomalocaris and Laggania , not because it was likely able to eat the larger adult forms , but because it could feed on the young . The trophic relationships of Tuzoia and Hurdia are sparser than for Anomalocaris , Laggania , and some of the other large arthropods because their morphology , particularly their feeding attributes , is more poorly known , although they are currently under study . Priapulid worms are found in both assemblages . The predatory feeding role of some can be inferred from the preservation of grasping spines in the introvert ( Ottoia ) , while a sediment-filled gut suggests deposit feeding ( e . g . , Acosmia ) , although as noted above this is a problematic assignment . The predatory role of the chaetognaths has recently been discussed [68] , but the function of some of the vetulicolia and the problematica is less certain . Here we have relied on morphology and habitat and as noted sometimes on the elimination of other possibilities . Thus the morphology and semi-sessile habitat of Vetulocystis suggests a filter-feeding mode of life [69] . Finally , as indicated in Tables S1 and S2 by cells left blank , there are several taxa for which the data are too imprecise to allow determination of trophic role and/or position . To create Cambrian food-web datasets , we excluded taxa with critically incomplete trophic information , as well as links to those taxa ( Tables S4 and S5 ) . Similar to modern food webs , the remaining species and trophic link information for each biota comprises an ancient food web characterized by a single connected network where every animal taxon has at least one food chain leading to a basal taxon and each basal taxon has at least one consumer ( Tables S6 and S7 ) . Trophic-species versions of the two original-species Cambrian webs were generated by aggregating taxa within each web that share the same set of consumers and resources ( Tables S8–S10 and Figure 1 ) . Certainty levels for links between trophic species were calculated by averaging across certainty levels for all associated aggregated links , resulting in fractional certainty levels in some cases ( Table S10 ) . Trophic-species versions of the two Cambrian food webs ( Figure 1 and Tables S8–S10 ) were compared to eight modern trophic-species food webs from a variety of habitats ( two each marine , estuary , lake/pond , and terrestrial; Table 1 ) . We limited analysis to webs with 20 < S < 60 because the two Cambrian webs have S within this range , and there are known scale-dependence issues with analysis of very small webs ( i . e . , S < 20 ) [11] and in the use of models to analyze larger webs , which tend to have larger model errors [18] . This represents a strong test for assessing the similarity of Cambrian to modern food-web structure , as the inclusion of smaller or larger webs would increase variability seen in modern web structure , making it more likely that Cambrian structure would fall within modern web variability . For each food web , we generated cumulative degree distributions for all links , the links from consumers ( vulnerability ) , and the links to resources ( generality ) . In addition , 17 network structure properties [15 , 18 , 21] were calculated: Top , Int , and Bas , the fraction of species that are top ( without consumers ) , intermediate ( with both consumers and resources ) , or basal ( without resources ) ; Can , Herb , Omn , and Loop , the fraction of species that are cannibals , herbivores ( feeding only on basal species ) , omnivores ( species that consume two or more species with different trophic levels ) , or found in loops ( food chains that contain the same species twice , apart from cannibalism ) ; ChLen , ChSD , and ChNum , the mean length , standard deviation of length , and log number of food chains; TL , the mean trophic level of all species computed using the short-weighted trophic level algorithm [18 , 45]; MaxSim , the mean of the maximum trophic similarity of each species [15 , 18]; VulSD , GenSD , and LinkSD , the normalized standard deviations of vulnerability , generality , and total links , which measure relative variation in the number of consumers , resources , and consumers plus resources across species [15 , 18]; and Path , the mean shortest food-chain length between all pairs of species , and Clust , the mean clustering coefficient , the probability that two species linked to the same species are also linked [21 , 70] . We sometimes refer to these 17 metrics as “single-number properties , ” as they quantify different aspects of structure with single numbers , unlike degree distributions . While a few of these properties have analytic forms derivable from generality distributions at the limit of S >> 1 and C << 1 [17] , most of them depend on details of food-web structure not captured by link distributions [18] . Although some of these properties are clearly not independent , there is still information to be gained in reporting them separately . For example , Top , Int , and Bas sum to 1 , so there are only two degrees of freedom among three properties . While this means that knowing one of the properties places a constraint on the sum of the other two , it does not determine how species are divided among the other two categories . A thorough investigation of the correlation structure among commonly reported food-web properties would be a useful topic for future research . To quantify the effects of excluding low-certainty or random links on our understanding of the network structure of the Chengjiang and Burgess Shale trophic-species food webs , we conducted a link-removal analysis . We removed the numbers of links that correspond to removal of 10% , 25% , 50% , 75% , and 100% of low-certainty links ( i . e . , certainty < 1 . 5 ) in each web . This corresponds to 6 , 15 , 30 , 44 , and 59 links in the Chengjiang web , and 9 , 23 , 47 , 70 , and 93 links in the Burgess web . Removals were first done targeting low–certainty links , and in a separate analysis , targeting the same number of random links . One hundred random draws of eligible links were conducted for the 10% , 25% , 50% , and 75% low–certainty link removals ( there is only one way to remove 100% of low-certainty links ) and at all five levels for random link removals . After each link removal , consumers without resources , consumers without a chain to a basal taxon , and disconnected taxa were removed along with their links , so that the resulting web is a single connected component with an ecologically tenable topology . To look at overall trends in Cambrian network structure in response to link removal , property values were averaged across the 100 webs resulting from a particular level of link removal . We examined the response of S , C , and L/S to link removals and the percent change in the value of the 17 other properties for the two webs and the two different types of link removal for each level of removal with reference to the original property value . To compare empirical food-web structure with structure produced by the niche model , described in detail elsewhere [15 , 18] , we generated ten sets of 1 , 000 niche-model webs with the same S and C as the ten empirical food webs . For the 17 network structure properties for each of the 1 , 000 niche model webs , we calculated ME to determine whether the value of a property in an empirical food web differs significantly from the model's distribution of values for that property . A property's ME is calculated as the normalized difference between the model's median property value and the empirical value . Depending on whether the empirical property is higher or lower than the model's median property , the difference is respectively normalized by ( i . e . , divided by ) the difference between the model's median property value and the property value at the upper or lower bound of the central 95% of the 1 , 000 model values of the property . If the property distribution is one-tailed , the difference is normalized by the difference between the median value and the value at the upper or lower 95% boundary of the distribution . An ME whose absolute value is greater than 1 indicates that the empirical property value does not fall within the most likely 95% of model property values , which we consider indicates that the empirical value significantly differs from the model prediction . This procedure [18] makes no assumptions about the shape of the model's distribution of properties , eliminating statistical errors associated with assumptions of normal distributions of model property values [15 , 20 , 31 , 71] . We checked for confounding scale-dependence in model errors by testing for significant relationships between mean niche model ME ( Table 2 ) and S , L/S , or C across the eight modern webs . No significant linear relationships were found , reinforcing our decision to limit analysis to the selected modern webs ( linear regression: n = 8: ME = 0 . 0014S + 0 . 0705 , r2 = 0 . 011 , p = 0 . 802; ME = −0 . 019L/S + 0 . 241 , r2 = 0 . 165 , p = 0 . 317; ME = −0 . 5127C + 0 . 2188 , r2 = 0 . 127 , p = 0 . 386 ) . We also checked for the adequacy of n = 1 , 000 for niche model analyses by running five additional sets of n = 1 , 000 niche model webs corresponding to the Chengjiang and Burgess webs , and found that mean niche model values showed little sensitivity to different runs of 1 , 000 , as reflected by low coefficients of variation ( <3 . 6% in all cases; <1% in 27 out of 34 cases ) ( Table S14 ) . We also ran basic niche model analyses ( n = 1 , 000 ) on two additional modern food webs with known biases likely to be poorly fit by the niche model . One , a parasite-free version of the Ythan estuary web ( “Ythan” ) with an overemphasis on birds as top predators [72] , is known to be poorly described by the niche model in terms of both single-number properties [15] and degree distribution [17] . The other , a source web of the herbivores , parasitoids , predators , and pathogens associated with the shrub Scotch broom ( “Broom” ) [73] , represents a particular subset of a broader food web , and has a broad-scale link distribution that differs from the single-scale link distributions typical of most food webs [21] . In addition , while it has been shown previously that a null model that distributes links randomly does a very poor job of predicting empirical food-web structure [15] , we considered an alternate , more plausible null model that we refer to as the “random-beta model . ” This model distributes links using a beta distribution , reproducing one of the central constraints of the niche model [15] and its variants [16 , 17] , but it does not include any other constraints ( e . g . , near-hierarchical feeding , diet contiguity ) . We generated two sets of 1 , 000 random-beta model networks with the same S and C as the two Cambrian webs , and calculated niche model means and MEs for each of the 17 properties for the two webs . To quantify the effects of excluding low-certainty or random links on our understanding of how the network structure of the Cambrian webs compares to modern web structure , we used the sets of webs generated by systematically removing uncertain or random links to conduct a comparative niche model analysis . For each of the reduced webs ( i . e . , 100 webs for each level and type of link removal in the two Cambrian webs , with the exception of a single web resulting from 100% removal of low-certainty links in each web ) , a set of 200 niche model webs at the appropriate S and C was generated , and MEs were calculated for each of the 17 properties . MEs for a particular property were then averaged across the 100 webs generated for each level and type of link removal . In addition to niche model analyses focused on the 17 single-number properties , we calculated the mean cumulative link distributions ( all links , vulnerability , generality ) and 95% CIs for separate sets of 500 niche model webs for the two Cambrian webs .
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Food webs , which depict the networks of feeding interactions among co-occurring species , display many regularities in their structure . For example , the distributions of links to prey and links from predators , the percentages of omnivores and herbivores , and the mean trophic level of species change systematically with the number of taxa and feeding links in a web . Such “scale-dependent” regularities are formalized by network models based on a few simple link distribution rules that successfully predict the network structure of complex food webs from a variety of habitats . To explore how long such regularities may have persisted , we compiled and analyzed detailed food-web data for two ancient fossil assemblages from the early Paleozoic , when rapid diversification of multicellular species , body plans , and trophic roles occurred . Our analyses show that for most aspects of network structure , the Early Cambrian Chengjiang Shale and Middle Cambrian Burgess Shale food webs are very similar to modern webs . This suggests that there are strong and enduring constraints on the organization of feeding interactions in ecosystems . However , a few differences , particularly in the Chengjiang Shale web , suggest that some aspects of network structure were still in flux during early phases of de novo ecosystem construction .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"ecology",
"computational",
"biology",
"evolutionary",
"biology"
] |
2008
|
Compilation and Network Analyses of Cambrian Food Webs
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Generation of skeletal muscles with forms adapted to their function is essential for normal movement . Muscle shape is patterned by the coordinated polarity of collectively migrating myoblasts . Constitutive inactivation of the protocadherin gene Fat1 uncoupled individual myoblast polarity within chains , altering the shape of selective groups of muscles in the shoulder and face . These shape abnormalities were followed by early onset regionalised muscle defects in adult Fat1-deficient mice . Tissue-specific ablation of Fat1 driven by Pax3-cre reproduced muscle shape defects in limb but not face muscles , indicating a cell-autonomous contribution of Fat1 in migrating muscle precursors . Strikingly , the topography of muscle abnormalities caused by Fat1 loss-of-function resembles that of human patients with facioscapulohumeral dystrophy ( FSHD ) . FAT1 lies near the critical locus involved in causing FSHD , and Fat1 mutant mice also show retinal vasculopathy , mimicking another symptom of FSHD , and showed abnormal inner ear patterning , predictive of deafness , reminiscent of another burden of FSHD . Muscle-specific reduction of FAT1 expression and promoter silencing was observed in foetal FSHD1 cases . CGH array-based studies identified deletion polymorphisms within a putative regulatory enhancer of FAT1 , predictive of tissue-specific depletion of FAT1 expression , which preferentially segregate with FSHD . Our study identifies FAT1 as a critical determinant of muscle form , misregulation of which associates with FSHD .
Developmental genetics has provided considerable insight into the regulatory networks controlling overall skeletal muscle development . Perturbation of these common mechanisms is associated with congenital abnormalities of the muscle lineage as well as with later-onset muscle pathologies [1] . In contrast , less is known about the mechanisms of functional diversification within the muscle lineage . Such diversification may be either metabolic - fast versus slow fibres , for example - or morphological , such as the position and shape of individual muscles . Genes controlling diversification too are likely to be of clinical significance [2]–[4] , since several human muscular dystrophies do not affect all muscles evenly , but specifically target regionalized groups [5] . This is true for limb girdle muscular dystrophy ( LGMD ) , oculopharyngeal muscular dystrophy ( OPMD ) , myotonic dystrophies with oculomotor involvement , distal myopathies , scapuloperoneal dystrophy , and facioscapulohumeral dystrophy ( FSHD ) [5]–[6] . In no case , however , is the rationale for this geographic specificity currently understood . One characteristic example of focal myopathies is FSHD , which affects subsets of muscles in the facial and shoulder areas [6] . The main form of FSHD - FSHD1 - is an autosomal dominant disorder associated with the contraction of an array of 3 . 3 Kb macrosatellite repeats ( D4Z4 ) , located at the subtelomeric 4q35 locus [6] . The mechanism by which the D4Z4 contraction triggers the disease represents one of the most enigmatic conundrums for human geneticists and remains incompletely understood . The D4Z4 array has been suggested to act as an insulator between telomeres and subtelomeric genes [7]–[8] , such that its contraction might result in regulatory changes in neighbouring genes that could in turn alter muscle physiology [6] , [9]–[11] . Despite intense focus on deregulated 4q35 genes , including one of the close neighbours , FRG1 [12] , and despite numerous large-scale investigations aimed at uncovering additional relevant candidates , none of the genes reported accounts for all aspects of FSHD , and additional players are still actively sought [6] , [9] , [13] . An emerging model is that the pathogenic effect of D4Z4 contraction in FSHD1 is mediated in part by DUX4 , a retrogene present within D4Z4 repeats themselves encoding a homeobox containing transcription factor that is normally silent in muscle [14]–[15] . In FSHD1 patients , the contraction of the D4Z4 repeat array leads to a change in chromatin structure that facilitates DUX4 expression [16] . Furthermore , the pathogenicity of the D4Z4 contraction requires polymorphisms distal to the last D4Z4 repeat , that create a polyadenylation signal and thereby stabilize DUX4 mRNA [17] . This stabilized RNA thus leads to increased expression levels in FSHD muscles of a pathogenic isoform of DUX4 , which activity is thought to be toxic for muscles through transcriptional activation of various target genes including Pitx1 and p53 [18]–[21] . Another less frequent form of FSHD , clinically identical to FSHD1 , is observed in absence of D4Z4 contraction . These cases , referred to as contraction-independent FSHD , include cases called FSHD2 , that were shown to exhibit hypomethylated D4Z4 repeats , recently shown to be caused by mutations in the SMCHD1 gene [22] . FSHD2 is caused by the combination of such SMCHD1 mutations with a DUX4 permissive ( polyA ) context , and also leads to DUX4 overexpression [22] . While FSHD2 cases represent so far the majority of contraction-independent cases , rare cases of contraction-independent FSHD with typical symptoms may also occur without hypomethylation , and be caused by yet unidentified pathogenic contexts . Neither the specificity of SMCHD1 or of DUX4 expression nor of its target genes identified so far [18]–[21] , [23]–[24] , provide sufficient account for the specificity of the muscle map and the non-muscular symptoms that characterize FSHD . The regional specificity in the map of muscles affected in FSHD suggests that the causal abnormality interferes with a muscle subtype-specific developmental process . A gene involved in functional diversification during muscle development would thus provide a logical candidate to fill this gap . We focused on the cell adhesion molecule FAT1 because Fat-like protocadherins are known modulators of the planar cell polarity ( PCP ) pathway [25]–[27] , a genetic cascade involved in coordinating tissue polarity , morphogenetic movements , and polarized cell flow [28]–[30] . Fat1 has been reported to be expressed in developing muscles and tendons [31] and to be regulated by muscle developmental genes such as Pax3 , Lbx1 , or Met [32]–[34] . Thus , FAT1 may control muscle shape through PCP-like mechanisms analogous to those involved in polarized migration of vascular endothelial smooth muscle cells [35] . Here , we report the unexpected finding that Fat1-deficient mice reproduce the highly selective muscular and non-muscular aspects of the clinical picture of FSHD . We show that Fat1 is required during development to shape specific groups of shoulder and facial muscles by modulating the polarity of myoblast migration . While constitutive inactivation of Fat1 leads to neonatal lethality due to defects in kidney development [36] , Fat1 hypomorphic mice exhibit defects of muscle integrity with a topography prefiguring the map of muscles affected in FSHD . Furthermore , conditional mutagenesis suggests that a cell-autonomous function of Fat1 in migrating muscle cells may account for a significant part of its muscle shaping function . The human FAT1 gene is located only 3 . 6 Mb from the critical FSHD genomic region at 4q35 , and emerges as a potential transcriptional target of DUX4 or p53 [18] , [37]–[38] . We present evidence of altered FAT1 levels in some foetal FSHD1 cases , in muscle , but not brain , accompanied with epigenetic modifications characteristic of silenced chromatin . Finally , we identified genetic variants deleting variable lengths of a putative cis-regulatory enhancer in the FAT1 locus , which segregate with FSHD . Thus , either in presence or absence of D4Z4 contractions , mechanisms leading to tissue-specific deregulation of FAT1 expression are associated with FSHD and may contribute to causing regional-specific muscle shape abnormalities that prefigure muscle degeneration in the adult .
In search of mechanisms that control muscle position and form , we studied Fat1 expression at stages of muscle morphogenesis . We chose first to study a muscle with a characteristic fan-shaped form , the subcutaneous muscle cutaneous maximus ( CM ) . During embryogenesis , following delamination from the dermomyotomal lip at forelimb levels , CM precursors , identified through their specific expression of GDNF , reach the base of the limb , turn , and spread under the skin in a radial manner [39]–[40] ( Figure 1A ) . This migration pattern reflects collective and polarized cell migration , visible owing to expression of the MLC3F2E reporter line or of the muscle fate marker MyoD , through the formation of chains of myoblasts aligned in radial directions ( Figure 1B and 1E top right panel ) . At the stages of CM migration , whole mount X-gal staining in embryos carrying a LacZ reporter gene-trap insertion in the mouse Fat1 gene revealed a hot-spot of Fat1 expression highlighting the migration area ( Figure 1C , Figure S1 ) . We found that CM myoblasts express Fat1 RNA and appear to be positioned in a subcutaneous layer which itself expresses Fat1 RNA , this surrounding subcutaneous tissue displaying a rostrocaudal gradient of intensity , with highest intensity caudal to the extremity of the CM ( Figure 1C; D ) . Thus , CM myoblasts express Fat1 and appear to migrate along an increasing gradient of Fat1 expression . We therefore asked whether Fat1 was required for CM location and/or form . We first took advantage of a mouse model carrying a gene-trap insertion in the mouse Fat1 gene [41]–[42] ( allele referred to as Fat1LacZ ) . Initial differentiation along the muscle lineage was unaffected in Fat1LacZ/LacZ embryos since CM myoblasts retained expression of broadly-expressed markers such as MyoD ( n = 6 ) , and markers of subsets of myoblasts ( such as Six1 ( n = 2 ) , gdnf ( n = 2 ) , and Lbx1 ( n = 2 ) ; data not shown ) . This allowed us to use MyoD expression to monitor precursor migration in Fat1 mutants . In E12 . 5 Fat1LacZ/LacZ embryos , we observed 1 ) an aberrant morphology of the CM muscle , reduced in size , and with ill-defined anterior limits ( Figure 1E ) , 2 ) a dispersion of migrating myoblasts not only within the CM but also in ectopic areas traditionally devoid of muscle cells . In the CM , higher magnification observations revealed that migration myoblasts failed to show a preferential alignment of their nuclei into migratory chains ( Figure 1E–H ) . This phenotype was associated with morphological changes in individual myoblasts , such as the loss of long cytoplasmic protrusions extending from the leading edge and rounded morphology of some nuclei within the chains ( Figure 1G , H ) . In further support of a role for Fat1 in migration polarity , numerous clusters of ectopic myoblasts or disoriented single myoblasts were found in the shoulder region of E12 . 5 mutants , either in ectopic places , or within additional shoulder muscles such as the spinotrapezius muscle ( Figure 1E orange arrowheads in orange dotted area; Figure S2 , red arrows ) . Further genetic evidence of such a function of FAT1 in control of muscle shape was obtained with another targeted allele of the Fat1 locus , which we engineered by flanking two exons , 24 and 25 , the latter containing the transmembrane domain , with LoxP sites ( Figure S3A , targeted allele referred to as Fat1Fln ) . Crossing of mice carrying the conditional Fat1Fln allele with a ubiquitous CRE-expressing mouse line produced , by germline excision of the floxed exons , a constitutively recombined allele , Fat1ΔTM , which encodes FAT1 protein isoforms lacking the corresponding transmembrane domain ( Figure 2A , B ) . Analysis of myogenic differentiation by in situ hybridization with a myoD probe indicated that Fat1ΔTM/ΔTM embryos exhibited phenotypes identical to those seen in Fat1LacZ/LacZ embryos ( data not shown ) . This new allele also allowed studying later steps of muscle differentiation by crossing Fat1ΔTM mice with a transgenic line in which nls-LacZ reporter activity is driven by an enhancer from the mlc3f gene ( MLC3F-2E ) [43] . Expression of this transgene ( MLC3F-2E:LacZ ) is detected slightly later than myoD expression as it reflects differentiation in myocytes and sarcomere assembly [43] , hence it allows visualising muscle shapes , but not migrating myoblasts . MLC3f-2E expression in Fat1ΔTM/ΔTM embryos revealed again the altered morphology of the CM muscle , with missoriented chains of myocytes in the ventral/pectoral half of the CM and shoulder belt muscles ( Figure 2D , and Figure S3B ) . Furthermore , Fat1ΔTM/ΔTM embryos were found to exhibit an extra muscle ectopically located in the shoulder area ( Figure 2D ) . Finally , we also visualized multinucleated myofibres owing to the nuclear β-galactosidase staining at late gestation stages , and confirmed the persistence of misoriented myofibers in the mature CM muscle of Fat1ΔTM/ΔTM E18 . 5/P0 embryos ( Figure 2D ) . Taken together , our data show that Fat1 is required to control the shape and position of subsets of migratory muscles in the developing embryo , by controlling coordinated polarity of collectively migrating myoblasts . We next wished to extend our description of the map of Fat1-dependent muscles by exploring the phenotypes exhibited by Fat1ΔTM/ΔTM embryos carrying the MLC3F-2E transgene at later developmental stages ( E14 . 5 and E15 . 5 ) , when migration has been completed and muscle shapes are determined . In the scapulohumeral area of all Fat1ΔTM/ΔTM;MLC3F-2E embryos examined , we consistently observed an extra muscle in a stereotyped ectopic position , systematically attached between the spinodeltoid muscle and the triceps brachii muscles ( Figure 3A , B ) . Just dorsal to the spinodeltoid , we found a subcutaneous portion of the spinotrapezius muscle ( SpTS ) to be drastically reduced in Fat1ΔTM/ΔTM;MLC3F-2E embryos ( Figure 3A , orange arrows ) . Observation from a dorsal point of view reveals that midline junction of the CM muscle and of Rhomboid muscles ( Rh ) is delayed , so that a large gap is seen in the back of an E14 . 5 Fat1ΔTM/ΔTM embryo ( Figure 3B , orange line ) . Numerous mispositionned myofibres create ectopic bridges between the acromiotrapezius and spinotrapezius muscles in Fat1ΔTM/ΔTM;MLC3F-2E embryos ( Figure 3B; read arrows in top and middle picture ) . Analysis of muscles in the face at E14 . 5 , E15 . 5 , and at P0 , reveals abnormalities in shape , myofibre orientation , and density in several subcutaneous muscles in the facial skin ( Figure 3C , red arrows ) that occupy positions reminiscent of the position of human muscles of facial expression . The flat structure of these subcutaneous muscles is analogous to that of the CM muscle , and the alterations observed in Fat1ΔTM/ΔTM neonates also include random orientation of multinucleated myofibres ( Figure 3C ) . In contrast , deeper muscles such as the masseters display normal shape in Fat1ΔTM/ΔTM mutants ( see Figure 3C and data not shown ) . Of notice , although muscle shape defects were found in stereotyped places , their severity was variable , and Fat1ΔTM/ΔTM embryos were frequently asymmetrically affected ( Figure S4 , see also Figure S12A ) . As previously observed in Fat1LacZ/LacZ mutants , examination of muscle development at E14 . 5 and E15 . 5 in Fat1ΔTM/ΔTM embryos confirmed that Fat1 loss of function selectively affects muscles of the facial and scapulohumeral ares , and that Fat1 is not required to shape other migratory muscles such as the diaphragm or hindlimb muscles , which were identical between wild type and Fat1ΔTM/ΔTM embryos ( Figure S4 and data not shown ) . Overall , in addition to the abnormal shape of the cutaneous maximus muscle , we found that Fat1 was required to shape selective and stereotyped groups of muscles in the scapulohumeral interface , as well as subcutaneous muscles of the face . We next asked what the consequences of these muscle shape abnormalities were at postnatal stages . Constitutive deletion of Fat1 was initially shown to lead to neonatal lethality most likely due to defects in kidney filtration [36] , [42] . Likewise , constitutive deletion of the transmembrane domain ( Fat1ΔTM/ΔTM mice ) also leads to more than 50% lethality at birth , with only a small proportion of mutants surviving to adulthood ( Figure S3C ) . We chose to examine adult Fat1LacZ/LacZ mutants , since the hypomorphic Fat1LacZ allele , which results from an insertion of a gene-trap construct in an intron , not deleting any functional domain , allows expression of variable amounts of residual Fat1 RNA and FAT1 protein in Fat1LacZ/LacZ mutants ( Figure 2E , Figures S5 , and S13 ) . This hypomorphic allele , in the genetic background we used , allowed bypassing the neonatal lethality in Fat1LacZ/LacZ mutants , with more than half the mutant mice surviving after 3 months ( Figure 4C ) , and enabled us to study the postnatal consequences of reduced Fat1 levels . The variable amounts of residual Fat1 correlates with the variability in the severity of phenotypes and in the age of death of Fat1LacZ/LacZ mice . A fraction of these adult phenotypes , in particular the lethality , is likely to result from systemic consequences of kidney phenotype . Indeed , analysis of kidney morphology in the subset of Fat1LacZ/LacZ mice that exhibited severe weight loss revealed features characteristic of polycystic kidneys , such as cysts formed of enlarged tubules in the cortical renal area ( data not shown ) . Therefore , to score with an objective criterion the progression through adult phenotype stages , body weight was measured for each individual and compared to its own maximal weight [44] . We arbitrarily set the moment a Fat1LacZ/LacZ mutant mouse has lost 10% of its weight as the visible onset of symptoms associated with kidney malfunction or with other phenotypes likely to have systemic consequences . Mutant mice showing more than 10% loss at the stage of analysis were defined as “symptomatic” ( related to generalized symptoms , and not to muscles only ) , and the degree of severity was recorded as percentage weight loss , while Fat1LacZ/LacZ mutant mice that did not exhibit any weight loss yet were defined as presymptomatic . Although this threshold of 10% weight loss was defined arbitrarily , and even though we cannot exclude that kidney phenotypes also have systemic consequences earlier than this limit , it is difficult , during symptomatic phase , to attribute a primary cause to the symptoms observed . We therefore focused on the presymptomatic phase for most of our studies of adult muscle , and also chose to exclude from our adult studies mutant mice with an impaired growth curve . While Fat1LacZ/LacZ mice at symptomatic stages ( with 20–30% body weight loss ) displayed generalized muscle mass reduction ( Figures S6B–C , presymptomatic mutant mice showed scapular winging , whereas lumbar posture and hindlimb function appeared unaffected ( Figure 4A ) . Postural abnormalities affecting the shoulder area , indicating weakness of the muscles involved in scapular movements , can be seen when presymptomatic mice move on a cage grid , especially in situations in which they challenge the shoulder girdle muscles by transferring bodyweight rostrally on their forelimbs . These postural abnormalities were accompanied by functional motor defects evidenced in rotarod assays at presymptomatic stages ( Figure 4E ) . Early symptomatic mice ( around the 10% threshold ) also showed kyphosis , a curvature of the spine known as a hallmark of muscle wasting in the shoulder girdle ( Figure 4D , F ) , without displaying skeletal abnormalities ( Figure 4B , X-ray ) . Similar observations were made in the small proportion of Fat1ΔTM/ΔTM mice that survived to adult stages . We next investigated the pathological basis for the selective postural abnormality of the scapulae at presymptomatic stages . Dissection of individual muscles in presymptomatic Fat1LacZ/LacZ mice revealed a significant mass reduction for both rhomboid muscles when compared to controls ( Figure 4D ) . As expected from the embryonic defect , a severe reduction in thickness of the CM muscle was also observed , although its subcutaneous location made accurate dissection and therefore mass measurement unfeasible . Defects in myofibre orientation similar to those observed at late embryonic stages were confirmed in CM ( Figure S6D and data not shown ) and in rhomboid muscles ( Figure 4G ) at all stages examined . In contrast , masses of muscles with unaltered shape when examined during development ( i . e hindlimb muscles such as gastrocnemius or soleus ) were also not significantly reduced at presymptomatic stages ( Figure 4D , Figure S6B , S7 ) . This argues that persistence in mature muscles of misoriented myofibres resulting from fusion of depolarized myoblasts contributes to the shoulder muscle phenotype in presymptomatic mice , although it does not rule out an additional direct function of Fat1 in muscle , whose loss may also cause muscle degeneration . Lastly , another consequence of developmental dysgenesis that is likely to contribute to focal muscle wasting is the persistence of ectopic muscles ( Figure S7 ) . Such ectopic muscles were found to share tendon attachment sites with existing muscles ( typically two ipsilateral muscles ) including shoulder belt muscles ( trapezius , LD , pectoral muscles ) , and the humeral muscle triceps brachii ( Figure S7 ) . This association correlated with a unilateral reduction of the corresponding muscle mass , reduction that nevertheless did not result significant until early symptomatic stages ( Figure 4D and data not shown ) . The phenotypes resulting from developmental dysgenesis were not restricted to muscle shape and mass . Histological analyses revealed that a significant reduction in fibre diameter was detectable already at early symptomatic stages in those muscles in which we detected developmental defects , including the CM , Rhomboids ( Figure 4G , superior and profundis ) , and Trapezius muscle ( Figure 5C , pooled analysis ) . This was also true for Fat1ΔTM/ΔTM mice analysed at presymptomatic stages ( Figure S8 ) . In contrast , at presymptomatic stages , analysis of myofiber diameters in muscles whose shape was unaffected at developmental stages ( such as gastrocnemius or soleus , and also diaphragm ) revealed no significant abnormality as compared to control mice ( Figure 4D , Figure S6B , and data not shown ) . In affected muscles ( trapezius , rhomboid , Pectoralis Major , LD , and CM ) , we observed a range of additional abnormalities including inflammatory infiltrations between myofibres , most frequently perivascular , in both presymptomatic Fat1LacZ/LacZ and Fat1ΔTM/ΔTM mice ( Figure S6D and Figure S7 ) . Fibre necrosis was also observed at more advanced symptomatic stages ( beyond 10% weight loss , Figure S7L and data not shown ) , but as mentioned earlier , it is impossible to distinguish whether any abnormality at symptomatic stage is strictly related to muscle defects , or reflects systemic consequences of unrelated phenotypes . Finally , observation of myofibre structure in affected muscles ( trapezius , rhomboid , Pectoralis Major , LD , and CM ) revealed progressive disruption of higher level organization , with appearance at presymptomatic stages of multiple faults disrupting the regular alignment of sarcomeric structures ( Figure 5A , D ) , and the detachment of the sarcolemma from the contractile apparatus ( Figure 5D ) . Overall , alterations of muscle integrity at pre-symptomatic stages were only detected in those muscles in which we reported fully penetrant myoblast or myofibre orientation defects ( CM , Rhomboids , and Tapezius ) . Analysis of neuromuscular junctions in affected shoulder muscles also revealed a proportion of junctions showing fragmentation ( Figure 5B ) , denervation , and atrophy ( Figure S9 ) . Such defects did not reflect a primary failure of NMJ innervations , as all neuromuscular junctions observed at early postnatal stages ( P3 ) were indistinguishable from wild type ( data not shown ) . Nevertheless , although the muscles that were spared during development and at presymptomatic stages ( e . g gastrocnemius , soleus , masseters ) were seen to harbour histological signs of muscle atrophy ( evenly reduced myofiber diameter ) at advanced symptomatic stages ( Figure S6B ) , we did not observe muscle degeneration , inflammation , necrosis , or fragmentation of the contractile apparatus ( data not shown ) . These results are consistent with the possibility that the developmental abnormalities of muscle shape constitute a topographic frame in which muscles might be predisposed to undergo early onset muscle wasting , prior to the appearance of systemic consequences of non-muscle phenotypes and the concomitant generalization of muscle wasting . These findings do not exclude however the possibility that Fat1 may play additional roles during muscle biology other than controlling shape during development . We next asked if the function of Fat1 in shaping facioscapulohumeral muscles was exerted cell-autonomously in migrating muscle precursors . In order to perform tissue-specific ablation of Fat1 in muscles at a stage compatible with migration , we reasoned that transgenic lines in which CRE expression would reproduce that of genes of the muscle differentiation cascade , such as myoD or Myf5 , would occur too late to have an impact on the migration itself . Therefore , to ablate Fat1 exons 24 and 25 in premigratory myoblasts , we took advantage of the Pax3-cre knock-in line [45] ( Figure S10 ) . Our conditional allele of Fat1 ( Fat1Fln ) initially includes the neo cassette that was used to engineer the mouse model . Although presence the neo cassette caused mild lowering of Fat1 expression levels ( Figure S11 ) , this only resulted in subtle , although statistically significant , morphological defects in Fat1Fln/Fln embryos/mice compared to controls ( Figure 6 and Figure S12 ) . This allowed using the Fat1Fln/Fln mutants for conditional studies with tissue-specific CRE lines , without requiring Flp/FRT recombination to further ablate the neo cassette . We therefore compared muscle development in Fat1Fln/Fln;Pax3cre/+ and Fat1Fln/Fln embryos , taking advantage of the MLC3F-2E transgene 1 ) to visualize the shape of every muscle and 2 ) to quantify the number of muscle cells dispersed in ectopic areas . We followed muscles belonging to Pax3-derived territories in the scapulohumeral area , where ablation of Fat1 leads to measurable phenotypes in Fat1ΔTM/ΔTM;MLC3F-2E+ embryos ( Figure 6A ) . First , we found significantly higher numbers of dispersed myocytes in the forelimb of Fat1Fln/Fln;Pax3cre/+ embryos than in Fat1Fln/Fln embryos ( Figure 6A , B ) . Second , an ectopic muscle similar to the one found in Fat1ΔTM/ΔTM embryos could be measured in Fat1Fln/Fln;Pax3cre/+ embryos , and its surface was significantly larger than in Fat1Fln/Fln embryos ( Figure 6A , C ) . At later developmental stages , in addition to confirming the persistence and position of this ectopic muscle in Fat1Fln/Fln;Pax3cre/+ embryos , as in Fat1ΔTM/ΔTM; MLC3F-2E+ embryo . Furthermore we also detected a reduced density of myofibers in the CM muscle and in the subcutaneous part of the spinotrapezoid muscle ( Figure S12 ) . As the Pax3cre/+ line is a CRE knock-in , but also a knock-out of the endogenous Pax3 locus , the resulting loss of one copy of Pax3 may be in itself sufficient to enhance FAT1-dependent phenotypes . To rule this out , we have evaluated the effect of combining a Pax3cre/+ context to the recombined Fat1ΔTM allele , and found no enhanced phenotype in either Fat1ΔTM/+:Pax3cre/+ or Fat1ΔTM/ΔTM:Pax3cre/+ embryos compared to Fat1ΔTM/+ or Fat1ΔTM/ΔTM embryos , respectively ( data not shown ) . Finally , Fat1Fln/Fln;Pax3cre/+ embryos did not display significantly more abnormalities in the subcutaneous facial muscles or in the spinotrapezius muscle than the mild phenotypes observed in Fat1Fln/Fln embryos ( Figure S12 ) , consistent with the fact that facial muscles do not belong to the Pax3-CRE lineage [46] . Furthermore , if ablation in facial neural crest cells , driven by Pax3-CRE activity , had been responsible for altering muscle shape , it would have done so as efficiently in facial muscles as in trunk muscles . The lack of enhancement of facial muscle phenotypes in Fat1Fln/Fln;Pax3cre/+ compared to Fat1Fln/Fln embryos thereby also excludes a contributing role of Fat1 expression in neural crest-derived cells . Thus ablating Fat1 in Pax3-derived cells is sufficient to partially reproduce the defects observed in scapulohumeral muscles of the constitutive Fat1 mutants , indicating that Fat1 is required cell-autonomously in migrating myoblasts to control the polarity of their migration . As we asked whether in addition to the control of muscle migration , Fat1 may play additional roles in mature muscle , we noticed that in mouse , Fat1 is also expressed in differentiated muscle fibres after migration stages . This expression can be detected through the pattern of β-galactosidase expression in Fat1LacZ/+ embryos , and by in situ hybridization ( Figure 7A ) . Furthermore antibodies against FAT1 C-terminal cytoplasmic tail detected a protein localized in stripes within muscle fibres ( Figure 7B–D ) , on either side of alpha-actinin-positive sarcomere boundaries ( so called Z-bands , Figure 7B ) . In adult mouse muscle , the stripes of FAT1 protein are closely juxtaposed with DHPR , a calcium channel present in transverse ( t ) -tubules [47] ( Figure 7B ) . Such localization is consistent with Fat1 also playing a direct role in muscle biology , distinct from its early function in orienting myoblast polarity . Consistent with previous reports showing that cytoplasmic variants in FAT1 proteins exhibit distinct subcellular localisation [48] , and that the cytoplasmic domain can translocate in the nucleus [49] , another antibody directed against the cytoplasmic domain ( FAT1-1465 antibody ) also detected FAT1 protein in significant proportion of nuclei in adult mouse muscle fibres ( data not shown ) . Western blot analyses indicated that a full length FAT1 protein is only detected in whole embryo extracts ( at E12 . 5 , Figure 2B ) or in isolated brain tissue , but not in muscle tissue , where the most abundant bands detected with anti-FAT1-ICD antibodies were smaller molecular weight proteins ( Figure S13 ) , which production is spared by the genetic alterations in both Fat1LacZ/LacZ and Fat1ΔTM/ΔTM mutants ( Figure 7C , D , Figure S5 , S11 , S13 and data not shown ) . While some of these smaller isoforms might be cleavage products of full length FAT1 [50]–[52] , additional short isoforms are also consistent with gene products resulting from transcript initiation at alternative downstream promoters , as proposed by genome browsers ( Ensembl , UCSC; Figure S5A , with EST-based genes referenced in NCBIM37 mouse genome and in GRCh37 human genome assemblies ) . Neither the gene trap insertion after the first exon ( this study ) , nor the removal of the entire first exon ( in the published knockout allele [36] ) , suppress such gene products . Deletion of the transmembrane domain in Fat1ΔTM/ΔTM mutants also allowed expression of protein products with unchanged size ( Figure S13 ) , although it nevertheless led to a more severe phenotype with drastic neonatal lethality ( compare Figure S3C and Figure 4C ) . Quantitative RT-PCR confirmed the presence of significant amounts of Fat1 RNA containing the last exons ( 26 to 28 ) in Fat1ΔTM/ΔTM mutants , albeit at reduced levels when compared to wild types ( Figure S11 ) . Thus , in the case of all mutant alleles , the remaining smaller isoforms might still carry out Fat1 functions at least partially , resulting in hypomorphic phenotypes with variable severity . Consistently , in immunohistochemistry experiments on muscle sections , residual FAT1 staining is also observed in myofibres of Fat1ΔTM/ΔTM mutants and Fat1LacZ/LacZ mice , and staining intensity in Fat1LacZ/LacZ mice that survived to adulthood inversely correlated with phenotype severity at the level of individual myofibers ( Figure 7C , D and data not shown ) . Presence of unchanged smaller FAT1 isoforms in muscles of Fat1ΔTM/ΔTM mutants precludes using this mouse line to investigate their function . However , it indicates that the phenotype of muscle migration is not the consequence of their deletion , but results from ablation ( constitutive or driven by Pax3-cre ) of the transmembrane domain in full length FAT1 proteins that are abundant at developmental stages ( Figure 2B ) . Strikingly , the topography of selective alterations in muscle shape that we observed during development in Fat1 mutant mice closely resembles the map of muscles affected in early phases of human FSHD . Muscle shape abnormalities such as those seen in facial subcutaneous muscles , in trapezius , or in rhomboid muscles are expected to result in lack of facial skin mobility and scapular winging , two symptoms that are frequently the first clinical manifestations of FSHD . The selective muscle weakness observed in presymptomatic Fat1 mutants in muscles belonging to the developmental map was also reminiscent of the early phase of FSHD . Even at the scale of EM observations , defects in myofibre structure , such as sarcolemma detachment ( Figure 5D ) , included aspects similar to those reported in FSHD biopsies [53] . Finally , asymmetry of muscle symptoms is an important aspect of FSHD symptoms . Asymmetries in muscle shape abnormalities were observed not only in the robust phenotypes displayed by Fat1ΔTM/ΔTM embryos , but also in the very subtle phenotypes associated with by mild lowering of FAT1 expression in Fat1Fln/Fln embryos ( Figure 6 , Figure S12A ) . In this context , it was interesting to note that the human FAT1 gene is located at 4q35 . 2 , 3 . 6 Mb proximal to the D4Z4 array whose contraction is associated with FSHD ( Figure 8A ) . We therefore asked whether in addition to muscle phenotypes , Fat1-deficient mice may also share similarities with non muscular symptoms of FSHD . Besides muscular abnormalities , the phenotypic spectrum of FSHD patients also includes vision defects linked to vascular abnormalities [6] , [54]–[55] . As previously reported , constitutive FAT1 loss-of-function causes abnormalities in eye development , with variable severity and penetrance [36] . The Fat1LacZ/LacZ mice surviving as adults carried milder phenotypes ranging from residual patterning defects ( aniridia , small eye , Figure 8B ) to perfectly shaped eyes and retina , in which analysis of vasculature with IB4 or PECAM staining revealed numerous areas with intraretinal telangiectasia , microvascular lesions , micro-aneurysms , and frequent retinal detachments ( Figure 8C ) . Additional non-muscular symptoms associated with FSHD also include high frequency hearing loss , although the cause of these deficits remains underexplored . Fat1-deficiency was recently reported [56] to cause mild morphological defects in the inner ear , such as reduced cochlear elongation , and to exacerbate the appearance of ectopic sensory hair caused by loss of FAT4 , another FAT-like protocadherin , reflecting their cooperation during in elongation and sensory hair cell patterning in the cochlea [26] , [56]–[58] . Furthermore , owing to expression of the MLC3f-2E transgene during inner ear development [59] , we observed shortening of the endolymphatic duct and endolymphatic sac in Fat1ΔTM/ΔTM embryos at E12 . 5 ( 7 affected sides out of 12 ) , this shortening being frequently asymmetric ( Figure 8D , E ) . These phenotypes are expected to influence audition . Thus , in addition to the similarity of muscle abnormalities , adult Fat1 mutant mice also show non-muscular defects reminiscent of clinical symptoms of FSHD . Nevertheless , the severity scale of these phenotypes includes phenotypes more dramatic than those seen in FSHD , and Fat1-deficiency also leads to phenotypes such as the previously reported kidney abnormalities , that have no equivalent in FSHD . Considering the gene location and the provocative similarities between Fat1-deficiency in mouse and FSHD , we therefore asked whether alterations in Fat1 expression might be an essential step in the molecular mechanism leading to FSHD pathology in human . As in spite of the essential role of Fat1 in kidney development , FSHD is not known to be associated with kidney abnormalities , if a mechanism linking FSHD to Fat1 exists , it is expected to involve partial functional alterations only , such as tissue-specific deregulation of FAT1 during development . We thus first asked whether in addition to the previously reported gene expression changes [9]–[11] , [60] , any deregulation of FAT1 expression levels could be detected in the classical context of FSHD1 , in which the pathology is due to the presence of a contracted D4Z4 array on a permissive/pathogenic DUX4-activating context ( 4qA haplotype ) [17] . This possibility was reinforced by the finding that FAT1 appears to be downregulated by DUX4-fl , but not by DUX4-short in human myoblasts [18] . This result was further validated by qPCR , after lentiviral infection of human myoblasts with DUX4-fl as compared with GFP control ( Figure S15D ) , indicating that DUX4 overexpression is capable of lowering FAT1 expression in cultured muscle cells . As our results in mice point to the crucial role of FAT1 deregulation during development , we aimed to analyse FAT1 expression in rare cases of biopsies from foetuses with a prenatal diagnosis of FSHD1 , in spite of the fact that stages of myoblast migration were not accessible to experimentation in this context . Nevertheless , the observation that FAT1 protein is a component of differentiated muscle fibres , enriched in the t-tubule system , is consistent with additional later functions of FAT1 necessary for muscle integrity . Possible alterations of FAT1 expression were therefore assessed in muscle biopsies of human FSHD1 cases at foetal stages through a series of independent approaches . Human FAT1 protein was detected by immunohistochemistry in human muscle biopsies from control foetuses of various stages with antibodies against FAT1 C-terminal cytoplasmic tail , with a striped pattern similar to that seen in mice ( Figure 9A , Figure S15 ) . We thus first studied FAT1 expression levels in tissues from an FSHD1 human foetus carrying a pathogenic 4qA allele harbouring 1 . 5 D4Z4 copies , expected from previous family history to lead to severe infantile FSHD ( Figure S14 ) . Immunocytochemistry with anti-FAT1 antibodies on sections from the quadriceps muscle revealed an overall decrease in FAT1 protein levels compared to quadriceps biospies from control foetuses ( Figure 9A ) , with an irregularly stripped pattern of FAT1 in myofibres that otherwise show a normal distribution of other muscle proteins , such as DHPR . To assess this FAT1 lowering quantitatively , mRNA expression levels were then followed by qRT-PCR in muscle biopsies from 4 FSHD human foetuses carrying pathogenic 4qA alleles harbouring 1 . 5 , 4 . 3 , and 7 D4Z4 copies ( referred to as F1 , to F4 , respectively; Figure S14A ) . In F1 foetus , FAT1 levels were reduced 5-fold in the deltoid ( a muscle belonging to the FSHD map ) and 3-fold in the quadriceps muscles ( a muscle traditionally affected only at late stages in the human disease; Figure 9B ) . This was also confirmed by Western Blot with anti-FAT1-ICD antibodies ( Figure S15A ) . Additional regulatory changes were detected ( Figure S15B ) , such as an increased level of MURF1 or dysferlin RNAs , while RNA of other muscle components , such as DHPR or γ-Sarcoglycan , were unchanged , ruling out secondary effects of loss of muscle integrity at this stage or quality of the biopsy . In contrast , no significant difference in FAT1 mRNA levels could be observed in brain when comparing FSHD and control samples from the same foetuses ( Figure 9B ) . Reduction of FAT1 mRNA levels , albeit to a lesser extent ( 25% reduction; Figure 9B ) , and aberrant protein localisation ( Figure S15C ) were observed in the quadriceps of a second FSHD foetus harbouring 4 . 3 D4Z4 repeats ( F2 ) , from an independent family with previous FSHD history ( Figure S14 ) . Finally , no significant quantitative changes were observed in muscle biopsies of twin FSHD foetuses with 7 D4Z4 repeats ( Figure 9B ) , although accumulation of FAT1 protein could be observed in some myofibre nuclei ( data not shown ) , a localization never observed in age matched control biopsies , but reminiscent of adult mouse muscles . In contrast to foetal stages , analysis of FAT1 mRNA levels in a series of adult FSHD1 biopsies or FSHD-derived myoblasts did not reveal any significant change compared to control biopsies or myoblasts ( data not shown ) , a result consistent with published data [10] , [60] , or with data available on GEO NCBI . Overall , these results indicate that 1 ) a reduction of FAT1 levels in differentiated muscles can be observed is some FSHD1 cases but is not common to all FSHD1 cases at the stages examined; 2 ) the observed changes in FAT1 expression levels in FSHD1 occur only during development . We next asked whether the changes we observed were accompanied with alterations in chromatin state around regulatory sequences of the FAT1 locus . We thus performed chromatin immunoprecipitations ( ChIP ) on muscle biopsies derived from these same FSHD1 and control foetuses ( Figure 9C ) , looking for potential changes in the levels of two widely studied chromatin marks: H3K4me3 ( trimethylation of histone H3 on lysine 4 ) , a mark of active promoters , and H3K27me3 ( trimethylation of histone H3 on lysine 27 ) , which marks transcriptionally silent chromatin [61]–[62] . Consistent with RT-PCR data , we observed a significant decrease in the level of H3K4me3 decorating the FAT1 promoter region in the two FSHDs foetuses with less than 5 repeats , but not in the foetuses with 7 repeats , as compared to 4 control muscle biopsies of similar age range ( Figure 9C right ) . However , all 4 FSHD1 foetuses nevertheless showed a significant increase in H3K27me3 levels ( Figure 9C left ) . These data are consistent with a switch in chromatin conformation towards the silenced state in the same FSHD1 samples in which RNA levels were reduced , a switch that has the potential to account for a large part of the observed decrease in FAT1 levels . FAT1 deregulation is not the only gene expression change reported to be associated with the D4Z4 contraction causing FSHD1 . As we also wished to determine to what extent the changes we found were relevant to the specific clinical phenotype , rather than a silent consequence of the D4Z4 contraction , we therefore extended our investigation to contraction-independent FSHD cases . Such patients have typical FSHD symptoms , but are not genetically associated to a pathogenic contraction of the D4Z4 array on chromosome 4 . A large fraction of these contraction-independent FSHD cases is now known as FSHD2 , in which hypomethylated D4Z4 repeats are combined with with a normal sized D4Z4 array on chromosome 4 permissive for DUX4 expression [22] , [63]–[64] . Besides , other rare cases of contraction-independent FSHD cases remains unexplained , and represent interesting candidates to test whether alterations of the FAT1 locus might be directly associated with FSHD . To identify such alterations of the FAT1 locus , we performed an array-based comparative genomic hybridization screen ( CHG [65] ) , a method used to uncover copy number variants . The custom-designed CGH array we employed covered the whole FAT1 genomic region , including non-coding sequences . In our CGH survey of 29 FSHD cases , including 10 FSHD1 cases and 19 contraction-independent cases ( 5 of which at least not showing D4Z4 hypomethylation , see Table S1 for clinical and genetic characterization of patients ) , we detected 5 cases exhibiting loss of portions of the intron 17 ( between exons 17 and 18 ) , or intron 16 of the FAT1 gene ( Figure 10A , B , Figure S16 ) . Besides the overlap with exon 17 , we noticed that these deletions mapped near or within a hot spot of H3K4me1 methylation , a hallmark of cis-regulatory enhancers [61] , spanning across intron 16 and part of intron 17 ( Figure 10A , and Encode high throughput data , available on the UCSC browser [66] ) . According to the ENCODE ChIP seq data set [67] , this element appears labeled as having strong enhancer activity in a human skeletal muscle myoblast line ( HSMM ) but not in 8 other non-muscle cell lines ( Figure S16B ) . Examining the chromatin status at this locus by ChIP experiments , we consistently found that in control foetal muscle biopsies , intron 16 but also intron 17 were decorated by high levels of the enhancer signature H3K4me1 and negligible amounts of H3K4me3 ( promoter signature ) ( Figure 10D , blue lanes , and data not shown ) , providing further in vivo support to the possibility that this sequence might indeed act as regulatory element in vivo . To determine whether loss of functional portions of the putative enhancer were associated with FSHD , we analyzed copy number variants ( CNVs ) in a set of 40 healthy controls , 19 contraction-independent FSHD cases , and 10 FSHD1 cases . As the sensitivity of the CGH method might not allow detecting all cases with accurate precision , we applied a more precise qPCR method , and evaluated relative copy numbers by comparing 3 positions within and around the putative enhancer to a control spot on another chromosome ( Figure 10A , C; 3 additional positions shown in Figure S16 ) . Having set the threshold for considering a genome as carrying reduced copy numbers ( loss ) to 75% of the value in a healthy control used as reference genome , we found some healthy controls that exhibited reduced copy numbers of genomic regions at the core of the H3K4me1 hotspot in intron 16 ( 5% of controls ) or in either surrounding exons ( 10% of control cases in both cases ) . This finding is consistent with a study , available through public datablases , that identified cases with loss of similar genomic segments at this locus in a group of 90 healthy individuals [68] . Thus , such deletions/copy number reductions are not sufficient on their own to cause FSHD symptoms , when occurring on only one allele of FAT1 . However , in all three positions , the proportion of FSHD cases ( all cases included ) who exhibited loss was significantly higher than the proportion of healthy controls carrying reduced copy numbers at the same spot ( Figure 10C , D; X2 test , p values<0 . 016; <0 . 00075; and <0 . 00041 , for exon 17; enhancer; and exon 16 , respectively ) . Cases with a deletion spanning the whole region were also significantly more frequent in the FSHD group than among controls . When considering only contraction-independent FSHD cases , as much as 47% carried the CNV including the putative enhancer , as compared to 5% of controls , and up to 68% carried a CNV encompassing at least one of the three considered positions , as opposed to 20% of the controls ( Figure 10C , D , Fischer test , p<0 . 0004 and p<0 . 0001 for enhancer and exon 16 , respectively ) . Conversely , when considering the distribution of cases with increased copy numbers ( gain , above a threshold of 1 . 25× over the average control value ) we found that there were significantly less FSHD cases with gain-CNVs than among the control group ( X2 test , p<0 . 017 and p<0 . 014 when considering all FSHD cases or contraction-independenty cases only , respectively ) . Finally , we also analyzed the methylation status at D4Z4 repeats on chromosome 4 on a subset of our group of contraction-independent FSHD patients ( 5 out of 19 ) , and found no indication of hypomethylation ( at the CpoI site , Table S1 ) on the proximal D4Z4 unit [64] . This does not exclude that others patients in our c . i-FSHD group would be diagnosed as FSHD2 , but indicates that FSHD can occur in non-contracted patients independently of the hypomethylation , known FSHD2 hallmark [22] , [64] . Together , these results indicate that partial or complete deletions of FAT1 intron 16/17 putative enhancer represent a polymorphism not sufficient to cause FSHD by itself when present on one allele only of chromosome 4 , but which segregates with FSHD . Therefore , this CNV can be combined with pathogenic or sub-pathogenic contexts , and may act as a novel disease modifier in FSHD .
The altered myoblast migration polarity caused by loss of Fat1 functions leads to selective developmental dysgenesis of scapulo-humeral and subsets of subcutaneous muscles of the face . Understanding how Fat1 controls muscle shape required first determining which part of its expression domain accounts for this function . In addition to the muscles , Fat1 is expressed in several of the cell types that interact with migrating muscle cells . The highest expression was seen in non-muscle cells , such as the subcutaneous layer towards which CM myoblasts migrate ( Figure 1 ) . This muscle-skin interface is analogous to the bone-muscle interfaces ( tendons , joints ) of skeletal muscles , where Fat1 also accumulates at later stages ( Figure 7A ) . Here , however , we show that ablating the floxed transmembrane domain of FAT1 with a Pax3-cre knock-in line leads to efficient excision in premigratory muscles of the limb but not the face , and reproduces at least partially the migration phenotype observed in constitutive Fat1 knockouts in the scapulohumeral region . Pax3-cre excision does not occur in motor neurons , hence ablation in this cell type does not contribute to the phenotype observed in Fat1Fln/Fln;Pax3 cre/+ embryos . No significant muscle shape defects were caused by Pax3-cre -mediated Fat1 ablation in subcutaneous muscles of the face . This is not surprising , as muscles in the face do not derive from Pax3-expressing precursors but were previously shown to derive from a subset of islet1-expressing pharyngeal mesoderm cells [46] , [70] . In addition to trunk migrating myoblasts , Pax3-cre-mediated excision occurs in dorsal neural tube and neural crest . Although Fat1 expression is detected in Schwann cells ( neural crest-derived ) along the nerves at P0 , we did not detect such an expression at the stage of muscle migration ( E12 . 5 , see Figure S11C ) , making it unlikely to for Fat1 to control migration polarity by acting in neural crest derivatives . Furthermore , as Pax3-cre-derived neural crest amply colonizes the developing face , the lack of enhanced muscle phenotype in the face of Fat1Fln/Fln;Pax3 cre/+ embryos disqualifies the neural crest component of Fat1 expression from playing a major contribution in muscle shaping , and strongly suggests that Fat1 is required cell-autonomously in migrating myoblasts to control the polarity of their migration . As however , the muscle phenotype of Fat1Fln/Fln;Pax3 cre/+ embryos is significantly weaker than the phenotype of constitutive mutants , it leaves the possibility that other component of Fat1 expression domain may also contribute to its function in muscle patterning . The rationale for why such a selective group of muscles is affected by Fat1 loss of function is still unclear . This group of muscle includes subsets of migratory muscles of the face and shoulder area . In the face , defects are restricted to branchiomeric muscles derived from the second brachial arch ( subcutaneous muscles of the skin , Figure 3 ) , while first branchial arch derived muscles ( masseters and temporalis ) , as well as extraocular muscles , are unaffected ( Figure 5 and data not shown ) [70]–[72] . The scapulohumeral region can be divided in two components: 1 ) the CM , as well as humeral muscles ( triceps , deltoid , or muscles which pattern is affected by the supernumerary muscle ) derive from somitic Pax3-driven hypaxial migratory precursors ( Figure S10 ) ; 2 ) In contrast , some of the shoulder muscles such as the acromiotrapezius and spinotrapezius , or the rhomboids , belong to the cucullaris group and were previously shown to derive from non-somitic , occipital lateral plate mesoderm [46] , [72]–[73] . Such specificity is in apparent contrast with the broader expression domain of Fat1 in muscles as observed at E12 . 5 and later ( Figure 1 , 7 , and S1 ) , although clear differences in expression levels between muscles can be distinguished ( Figure 7A ) . Given that distinct regulatory programs govern the development of these muscle groups [2] , [74] , the selective impact of Fat1 on muscle shapes could be determined by its interaction with some of the selective myogenic regulators . Advanced symptomatic stages in Fat1-deficient mice are likely systemic consequences of such non-muscle phenotypes . Nevertheless , the muscle wasting and dystrophic features measured at presymptomatic stages were detectable selectively in those muscles that exhibited myofiber orientation defects , even in cases with no other detectable phenotypes . Despite the important variability in postnatal phenotype strengths observed with the Fat1LacZ allele , myofibre orientation defects and dystrophic features in the CM and shoulder muscles ( Rhomboids , Trapeze ) were observed in all mutant cases examined , not only of embryos , but also at adult stages , even in cases of Fat1LacZ/LacZ mice surviving to old ages with no other detectable phenotype . This specificity argues against the idea that restricted topography of muscle defects would be a consequence of renal problems or of other non-muscular defects . Furthermore , the observed match between the topography of the developmental phenotype and the specific map of muscles that undergo wasting at presymptomatic stages in adult Fat1LacZ/LacZ mice supports the idea that the selective muscle degeneration might occur as a consequence of the altered muscle shape . Future experiments will be necessary to determine whether the limited defects observed in Pax3-cre/Fat1 embryos are sufficient to predispose muscles to early onset degeneration , and whether additional triggers might be required for degeneration to occur in adult life . Among phenotypes observed in adult Fat1-deficient muscles , it will also be interesting to distinguish secondary consequence of the altered muscle shapes , from phenotypes reflecting additional , independent functions of Fat1 , whether exerted in muscles too or in other cell types . The spatial distribution of muscles mis-shaped as a result of Fat1 loss of function as seen at E14 . 5/E15 . 5 ( Figure 3 ) appears to overlap very closely with , and thus to predict , the map of muscles affected at early stages in FSHD . Furthermore , the observation of non-muscle phenotypes such as defects in retinal vascularisation or inner ear patterning also bears some similarities with symptoms observed in FSHD patients . Despite this strong concordance between the phenotype of Fat1-deficient mice and FSHD symptoms , the selectivity of the shared phenotypes raises a paradox . Fat1 expression during development is not restricted to FSHD-relevant tissues , and constitutive deletion of Fat1 leads to pronounced renal defects and neonatal lethality . Even the Fat1 hypomorphic phenotypes presented above cannot be considered as an exact phenocopy of FSHD . Overall this mouse model is also more severe than FSHD , and 50% of the mice die within 3 months , likely of milder versions of the kidney phenotype ( such as polycystic kidney ) . In contrast , FSHD is not known as a lethal disease , and has no reported association with kidney problems . Absence of renal dysfunction in FSHD is a strong indication that FSHD cannot simply be considered a “FAT1 knockout” . Thus , cases of patients with severe FAT1 loss of functions and kidney failure might be fatal before onset of muscle dystrophy and might thus fail to be classified as FSHD . In support of this hypothesis , a rare case of a 5-year-old girl carrying a duplication of the D4Z4 array and showing vascular retinopathy and sensorineural deafness was also reported to have focal glomerulosclerosis of the kidney [75] . Instead , lack of association between FSHD and renal dysfunction indicates that any FSHD mechanism involving FAT1 alterations must necessarily preserve FAT1 expression/function in kidney ( at least ) . Our results with mice suggest that such selective alterations of FAT1 function/expression may matter during development , in muscle precursors , at a stage when their migration occurs , for which FSHD human material was not available so far - and can ethically not be sought . FAT1 levels may not be changed to an equal extent in all tissues and times , consistent with our observation that FAT1 levels were reduced in disease-relevant muscles but not in brain , and at foetal but not adult stages . Thus , an engineered mouse model in which Fat1 functions are specifically ablated in muscles and preserved in the renal system , even though lacking effects of other DUX4 target genes , may represent a more suitable tool to study consequences of the muscle abnormalities in adult , and a better model reflecting the tissue-specific FAT1 depletion that we propose might be occurring in FSHD . The finding that human cases of contraction-independent FSHD , with such a characteristic and restricted set of clinical symptoms , segregate with the deletion of a putative regulatory genomic element in the FAT1 locus instead of the traditional D4Z4 contraction , strongly supports the idea that altered FAT1 regulation plays a key role in the pathology . The putative cis-regulatory enhancer reported in this study , which deletion segregates with FSHD in contraction-independent cases is likely to carry tissue-specificity information driving FAT1 expression in FSHD-relevant cell types , and future experiments are required to demonstrate such activity . The finding that healthy controls can exhibit heterozygous loss of this fragment of the FAT1 locus , containing two exons and an enhancer , is consistent with the observation that heterozygous loss of Fat1 functions in mice does not have major consequence of life span , health , and muscle integrity . However , we did observe a significant degree of haploinsufficiency in Fat1ΔTM/+ embryos , evidenced by the presence of subtle muscle shape defects ( Figure 6B , C , see indicated p values ) , the most frequent position being between the acromyotrapezius and spinotrapezius muscles ( as in Figure 3B ) . Such phenotypes were also consistently detected in Fat1Fln/Fln embryos ( Figure 6 and Figure S10D ) , in which expression levels were similar to those measured in Fat1ΔTM/+ embryos ( Figure S10B , C ) , suggesting that muscles in the shoulder area are highly sensitive to Fat1 dosage . While copy number variants outside of the putative enhancer might occur without causing any regulation change , we reasoned that the further such deletions would extend into the ENCODE predicted enhancer , the more functional transcription factor binding sites they may remove , hence increasingly interfering with FAT1 regulation on the deleted allele , thereby sensitizing the locus to additional contexts that may additionally impact on FAT1 expression . Interestingly , two of the FSHD1 cases presented here were monozygotic twins , both carrying a contracted 4q35 allele with 3 D4Z4 units , one of the twins being asymptomatic while the other twin had been diagnosed with a classical FSHD . We found that the twin with FSHD symptoms displayed reduced copy numbers throughout the length of the studied area , encompassing both exons 16 and 17 and the intron 16 putative enhancer , while the asymptomatic twin exhibited reduced copy numbers only at the distal-most region towards exon 16 , this difference possibly representing a de novo somatic mutation ( Figure 10 and Table S1 ) . Although this correlation does not constitute a demonstration of causality , it provides support to the hypothesis that this lowered copy numbers ( heterozygous ) of FAT1 exons 17/16 and of portions of the putative FAT1 enhancer portions have the potential to worsen FSHD symptoms when combined to a pathogenic context . However , obtaining a formal demonstration of this hypothesis will require studying phenotypes/genotype correlations on a large cohort of patients , and knowing in each case if the FSHD-causing genetic context is FSHD1 , FSHD2 , or other un-identified contraction-independent contexts . Overall , deregulation of the FAT1 gene is associated with FSHD , either as a consequence of DUX4 overexpression , and/or epigenetically encoded in FSHD1 and FSHD2 , or through the deletion of a putative enhancer that segregates with contraction-independent FSHD patients . Among possible products of the Fat1-gene , our results in mice indicate that the control of migration polarity and muscle shape requires a Fat1 RNA containing a transmembrane domain encoded by the floxed exons and deleted in the Fat1ΔTM allele . In contrast , other functions can be executed by incomplete Fat1 isoforms . Residual RNAs containing 3′ Fat1 exons can rescue ( to an extent correlating with RNA levels ) kidney defects and their consequences , but not muscle dysgenesis . Interestingly , however , both mouse models retain the capacity to produce FAT1 protein isoforms containing an intracellular domain , albeit at reduced levels quantified by qPCRs ( Figure S10B , C ) , ruling out a major contribution of these isoforms to the muscle shape phenotypes observed in both mouse models . In muscle fibres , FAT1 is a novel component of t-tubules . Does Fat1 expression in differentiating and mature muscle reflect additional functions in muscle biology ? The presence of FAT1 protein in close association with the contractile apparatus , as soon as differentiation starts , may reflect a role in sarcomere assembly . These FAT1-enriched stripes are maintained in mature muscle fibres , tightly juxtaposed with the t-tubule system ( Figure 7B ) . This may indicate a further involvement in excitation-contraction coupling , an essential process required throughout adult life for muscle function and maintenance . However , this striped pattern is established as early as the contractile apparatus assembles ( Figure 7C ) , before the alignment and docking of T-tubules to the contractile apparatus takes place , the latter phenomenon occuring postnatally in mice [76] . This indicates that in muscle , FAT1 isoforms are not inserted in the t-tubule compartment itself , but may be located at an interface juxtaposing t-tubules and the contractile units , possibly reflecting a new function for Fat1 for example during assembly of the t-tubule network . As myoblast migration precedes differentiation and sarcomere assembly , the accumulation of these FAT1 protein isoforms in the contractile apparatus occurs too late to be accountable of the function in migration polarity . FAT-like proteins were previously reported to be subject to various cleavage events by Furin convertase or by α- or γ-secretases [50]–[52] . Furthermore , alternative splicing events in the cytoplasmic exons were reported to influence subcellular targeting of FAT1 proteins [48] . Our work in mice unexpectedly indicated that in addition to producing a large transmembrane protein and its cleavage products , the Fat1 gene also produces small molecular weight protein products which appear not to contain a transmembrane domain , and synthesis of which is largely preserved in both Fat1-deficient mouse models , although at reduced levels . Bioinformatic scans and existing ESTs reported on all genomic browsers are indeed consistent with the possibility that short isoforms may result from transcript initiation at alternative downstream promoters , and may code for protein products devoid of leader peptide and transmembrane domain and potentially produced in the cytosol ( lacking a leader sequence ) . Thus , understanding the roles played by the isoforms of FAT1 produced in muscles will require first characterizing the exact exon and domain composition of the Fat1 RNA and protein isoforms produced in muscle ( wild type and Fat1ΔTM/ΔTM ) , and second designing novel strategies to ablate them independently of the transmembrane domain containing isoforms . Interestingly , residual expression of such muscle-specific isoforms is genetic background dependent and its levels in Fat1LacZ/LacZ mice inversely correlated with phenotype severity . Furthermore , reduced expression levels and abnormal sub-cellular localization were observed in muscle of human foetal cases with expected severe and early onset FSHD1 ( as predicted by the degree of D4Z4 contraction and family history ) , while no significant changes in RNA levels were detected in adult FSHD1 muscles compared to controls . These observations are consistent with the idea that deregulated FAT1 expression in differentiated muscle may be predictive of early ( infantile ) onset and severe dystrophy . These data suggest that the causes of the early phase , common to all FSHD patients and restricted to muscles of the face and shoulder , might be uncoupled from the causes of later phases of the disease - which spreads to other muscles , a condition that occurs in a subset of FSHD patients with childhood onset , the latter ending up wheel-chair bound [6] . Recent studies have brought to light several possible molecular pathways by which the D4Z4 contraction on a 4qA allele may exert its pathological effect in FSHD1 . Among those , stabilization of DUX4-fl mRNAs by polyA-creating polymorphisms was shown to enable expression of a toxic form of DUX4 , the latter causing muscle dystrophy through altered regulation of numerous target genes , including Pitx1 , p53 , and other germline-specific genes or myogenic regulators [17]–[21] , [23]–[24] . Another mechanism involves production by the contracted region of DBE-T , a chromatin-associated long-non-coding RNA that causes de-repression of several 4q35 genes [77] , including FRG1 , whose overexpression was previously proposed to contribute to causing muscle degeneration too [11]–[12] . Other mechanisms also influencing 4q35 gene expression include a telomeric position effect , according to which propagation across 4q35 of changes in methylation or chromatin conformation might be due to the loss of the CTCF barrier function of the D4Z4 array [8] , [13] , [78] . The relative contribution of DUX4-mediated gene regulation and of mechanisms leading to altered 4q35 gene expression is controversial [9] , [17] and may reflect an underestimated diversity in the clinical expression of FSHD1 [79]–[80] . Understanding which of these mechanisms , or what combination , contributes to modifying tissue-specific distribution of FAT1 will require developing cellular or animal models adequately reproducing FSHD mechanisms and mimicking in vitro key steps of muscle shape development . This will also allow defining whether there are differences in the sensitivity to a contracted allele between developmental stages and adult muscle , but also between FAT1 isoforms . DUX4 can repress FAT1 expression in human myoblasts ( [18] and Figure S15D ) . Such regulatory influence could involve some DUX4 target genes such as p53 [37]–[38] , or myogenic transcription factors . Our data suggest that irrespective of whether FAT1 is regulated by DUX4 , by DBE-T , or by anyone of their respective downstream or upstream targets , this regulation must occur primarily during development , in the cell type in which FAT1 is required to control migration polarity . This model does not exclude the possibility that the pathogenic 4q35 allele may further contribute to directly triggering muscular dystrophy in adult muscle , through additional mechanisms independent of FAT1 de-regulation . A number of clinical features of FSHD , including non-muscular symptoms such as hearing loss and retinal vasculopathy [81]–[82] , carry the signature of defects in the Wnt/PCP pathway [26] , a cascade of tissue polarity regulating genes , involving non-canonical Wnt/Frizzled signalling ( core PCP genes ) and modulated by the protocadherins FAT and Dachsous [25] , [27] . Sensory hair cell polarity in the cochlea is the best mammalian PCP paradigm , and deafness has become a traditional hallmark of altered PCP signalling [26] , [57]–[58] . Even through the anatomical nature of auditory abnormalities in FSHD is not known , it will be relevant to explore whether it carries further characteristics in common with altered PCP . Furthermore , vascular abnormalities in the retina , also known as Coats disease , are phenotypically similar to familial exudative vitroretinopathy ( FEVR ) , recently linked to mutations in the Wnt receptor Frizzled4 ( FZD4 ) and its ligand Norrin [83]–[85] . Moreover , the Wnt/PCP pathway is also known to play key roles in muscle biology . PCP-activating Wnts , such as Wnt11 or Wnt7a act as instructive signals for myofibre orientation during muscle morphogenesis [86] , for muscle satellite cell expansion through symmetric division [87] , and for neuromuscular synapse development [88] . Thus , altered regulation of FAT1 may in turn de-regulate the function or expression of its genetic partners , such as other components of the planar cell polarity cascade but also of the Hippo pathway . Mutations in other components of these genetic cascades may also play a causal role in a subset of the FSHD patients lacking the D4Z4 contraction . Overall , by linking FSHD to FAT1 , our work opens new avenues for the exploration and treatment of this and other neuromuscular disorders .
Animals were maintained and sacrificed in accordance with institutional guidelines . Adult mice were either sacrificed for experiments through anaesthesia , or euthanized by cervical dislocation . Efforts were made to minimize the number of adult Fat1-deficient mutant mice examined after more than 25% weight loss . Human DNAs were obtained from FSHD and control cases at La Timone Hospital ( Marseille , France ) . The protocol for their collection was approved by the Université de la Méditerranée ( Marseille , France ) Committee on Human Research and an agreement of informed consent authorizing scientific experiments was signed by each individual patients . Human Tissues samples were obtained from abortus cases at La Timone Hospital ( Marseille , France ) and at AP-HP ( Assistance Publique-Hopitaux de Paris , France ) . The protocol for their collection was approved by the Université de la Méditerranée ( Marseille , France ) Committee on Human Research and an agreement authorizing scientific experiments was signed by the parents . Termination of pregnancy ( performed at the stages corresponding to individual cases ) was decided after late prenatal diagnosis . Human Tissues samples were obtained from abortus cases ( see ethics statement ) after termination of pregnancy , decided after late prenatal diagnosis ( of FSHD1 or of non muscular medical symptoms for control cases ) . The cases used , and their respective stages are described in Figure S8A . Four cases of foetuses diagnosed with FSHD were used ( Figure S8A ) referred to as F1 , F2 , F3 and F4 , respectively . Family history included in the F1 case early-onset and severe FSHD phenotypes in a sibship carrying the same haplotype ( family tree shown in Figure S8D ) . In the she second ( F2 ) and third ( F3 and F4 , twin foetuses ) cases one parent had FSHD . FSHD diagnosis was characterized by standard procedures involving southern blotting using a combination of restriction enzymes and probes , to characterize contraction status , 10 versus 4 chromosome , and haplotype . The p13E-11 probe was used on genomic DNA digested with EcoRI alone or with EcoRI and BlnI , hence determining D4Z4 array length and distinguishing and 4q contractions from 10q contractions [93] . Molecular combing is then performed with a combination of probes ( including those for D4Z4 , the p13E-11 , qA and qB-specific probes , and 10q versus 4q specific ) allowing to distinguish simultaneously 10q from 4q as well as qA from qB haplotypes and the degree of contraction [94] ( see also simplified probe set in Figure S14B–E ) . Control biopsies ( Figure S14A ) were also obtained from abortus cases , for which termination of pregnancy was performed on the basis of medical diagnosis different from FSHD or other muscle related diseases . Detailed information on clinical and genetic diagnostic for the patients used for CGH and qPCR studies is provided in the Table S1 . X-gal staining was performed using classical procedures on embryos or postnatal tissues previously fixed in paraformaldehyde ( PFA ) 4% ( time depending on strength of lacZ expression ) , rinsed in PBS , and incubated in X-gal in combination with potassium ferri- and ferro-cyanide ( FeCN ) . Staining was terminated by rinsing in PBS , and post-fixing in PFA4% . Embryos were transferred in 100% glycerol for imaging and counting dispersed myoblasts . For adult murine tissues , anaesthetized mice were perfused with PFA 4% in phosphate buffer saline ( PBS ) prior to dissection . Shoulder belt muscles were carefully dissected under a stereomicroscope , rinsed in PBS , shortly incubated with fluorescent alpha-Bungarotoxin to visualise neuromuscular junctions . When necessary , observation under fluorescence was used to visualise and sub-dissect zones enriched in neuromuscular junctions . Samples were cryoprotected in 25% sucrose ( in PBS ) , embedded in a mix with 7 . 5% gelatine and 15% sucrose in PBS , and frozen for cryostat sections . Immunofluorescence was performed using primary antibodies to neurofilament ( NF-M , Ab1789 , Chemicon ) , tau ( AbCAM ) , laminin ( Sigma ) , alpha-actinin ( Clone EA-53 , Sigma ) , Ryanodine Receptor RyR1 ( MA3-925 , Thermo scientific ) , Dyhydropyridine Receptor alpha 1S ( MA3-920 , Thermo scientific ) , rabbit anti-GFP ( invitrogen ) . Antibodies against FAT1 were the following: two rabbit polyclonal antibodies raised against human FAT1 , HPA001869 , HPA023882 from SIGMA ( epitopes described in the human protein Atlas ( http://www . proteinatlas . org ) recognised two regions of the extracellular domain of FAT1 indicated FAT1-1869 and FAT1-23882 , respectively , in Figure 2A , B . Two antibodies against the intracellular domain of mouse FAT1 were used: Fat1-ICD from ref [35] , and an additional anti-Fat1 rabbit antisera ( Rb1465 ) we raised against a GST-fusion protein encompassing the intracellular domain of mouse FAT1 ( see complete procedure below ) . Secondary antibodies used were Cy3- or Cy5-conjugated ( Jackson Immunoresearch ) or conjugated with Alexa-488 or Alexa-555 ( Invitrogen ) . NMJs were visualised with Alexa-488 conjugated alpha-Bungarotoxin ( 1/2000 ) , and F-actin with alexa-594 or Alexa-647 conjugated-Phalloidin ( Invitrogen ) . Retinal vasculature was visualised with Alexa-488-conjugated GS-IB4 ( Invitrogen ) , as described [83] , including CaCl2 1 mM and MgCl2 1 mM in all incubating solutions . Image acquisition was performed with a Zeiss Axioplan equipped with Apotome . For electron microscopy analysis , muscles were dissected from mice previously perfused in 4% PFA , and post-fixed in 2% PFA , 2 . 5% glutaraldehyde , 50 mM CaCl2 in 0 . 1 M cacodylate buffer ( pH 7 . 4 ) . Muscles were additionally postfixed with 1% OsO4 , 0 . 1M cacodylate buffer ( pH 7 . 4 ) for 2 h at 4°C and dehydrated in a graded series of ethanol , with a 2h incubation step with 2% uranyl acetate in 70% Ethanol at 4°C . Samples were further dehydrated and embedded in epon resin . Thin ( 70-nm ) sections were stained with uranyl acetate and lead citrate and examined by transmission electron microscope ( Zeiss EM 912 ) . Images were acquired with a digital camera Gatan Bioscan 792 , using the Digital Micrograph software . Embryos were collected in PBS and fixed in 4% PFA . In situ hybridizations were performed with a MyoD RNA probe on whole mount E12 . 5 embryos , according to previously published procedures [95] . In order to assess the orientation of myoblasts , the CM muscle sheet was dissected and flat mounted , after completion of the ISH procedure , for high magnification imaging with a Zeiss Axioplan . For each muscle , three areas in stereotyped positions of the CM ( 3 positions in which the main chain direction made a 10° , 45° , and 70° angle with the DV axis , respectively ) were imaged at 63X resolution . Scoring myoblast direction was done using AxioVision image software ( Zeiss Imaging ) . For each picture , three to four chains were outlined . For each cell , an angle between the closest outlined chain and the nucleus direction was measured . Every cell for which the nucleus was visible was assigned such an angle . The distribution of angles was thus determined for each embryo side ( two CM muscles per embryo ) , by defining angle ranges of 10° , and determining the percentage of cells showing an angle in the given angle range . This distribution was averaged between 3 wild types embryos sides ( n = 3 ) , and 5 Fat1LacZ/LacZ embryo sides ( n = 5 ) . Tissue extracts were prepared in EBM buffer ( 20 mM Tris–HCl pH 7 . 5 , 150 mM NaCl , 1% Triton , 5 mM EDTA , 5 mM EGTA , 10% glycerol ) supplemented with protease inhibitors [95] . To enrich the lysates in membrane associated proteins , lysates were lectin-purified by incubation with Lectin-sepharose beads . For immunoblotting , 50 µg of protein extracts were separated by SDS–PAGE using 3–8% gradient gels ( Invitrogen ) , blotted onto nitrocellulose membrane and detected with specific antibodies . Immunoblots were revealed by ECL ( Amersham ) . We first constructed a GST-FAT1 fusion protein , containing the C-terminal part of the intracellular domain of mouse FAT1 ( from aa 4451 to 4588; an epitope entirely contained by exon 28 , and comprising approximately one third of the cytoplasmic domain ) , in PGEX2 vector . Serum was collected from two rabbits immunized with the GST-FAT1 fusion protein ( Rb 1465 and 1464 ) . Antibodies were affinity purified from the two antisera , using the same GST-FAT1 ( GST-Fat1-aa4451-4588 ) fusion protein , loaded on Affi-gel 15 support in poly-prep chromatography columns , following the manufacturer's instruction ( Biorad ) . In both mouse and human samples , total RNA was isolated using Trizol reagent ( Gibco , BRL ) . RNA was resuspended in 100 µl DEPC-treated H2O and quantified by spectrophotometry; samples used for RT-PCRs had a 260/280 absorbance ratio greater than 1 . 8 . cDNA was synthesized from 1 µg of total RNA using Superscript III ( Invitrogen ) or the First Strand cDNA Synthesis Kit ( Fermentas RevertAid: K1622 ) and random oligonucleotides . In mouse RNA samples , expression levels of Fat1 , Creatine kinase B ( CKB ) or HPRT were determined by semi-quantitative and/or quantitative RT-PCRs using real-time sybrgreen PCR assay ( life technologies ) , using the following primer sets . Fat1 Primer set exons 20–21 ( product size: 511 or 525 bp ) , 5′ CCA CGC GGT TGT CAT GTA CG 3′ ( exon 20-Fw ) , and 5′ TCC AGT AGG CGA GGG ATT GC 3′ ( exon 21-rev ) . Fat1 Primer set exon 6–8 ( product size: 545 ) : 5′ AAG CCC CTT GAT GCA GAA CA 3′ ( exon6-Fw ) ; 5′ TCA GCG TTC CTC CCT TTG TC 3′ ( exon8-rev ) . Fat1 primer set exons 24–25 ( product size 142 bp ) 5′ TGC TGT CTG TCA GTG TGA CTC AGG C 3′ ( exon 24-Fw ) ; 5′ GAG AGG CAT CCT CAC AGT GCT TCC C 3′ ( exon 25-rev ) ; Fat1 primer set exons 26–28 ( product size varies according to splice variants expressed; 3 products are observed:268 bp , 304 bp; and 330 bp ) 5′ CGC TTA GCT CCT TCC AGT CAG AGT CC 3′ ( exon 26-Fw ) ; 5′ GGG TGG GTG TAT GGA CTC GAA CTG G 3′ ( exon 28-Rev ) ; HPRT primer set: HPRT-fw: 5′ CAC AGG ACT AGA ACA CCT GC 3′ HPRT-rev: 5′ GCT GGT GAA AAG GAC CTC T 3′ . Creatine kinase B-type ( CKB ) ; CKB-Fw: 5′ ACG ACC ACT TCC TCT TCG ATA A 3′; CKB-rev: 5′ TTT TCA GTG TCA GCA ACA GCT T 3′ . For qPCR experiments , the HPRT gene was used as endogenous reference gene to normalize the data across all samples . For each gene examined , primers were chosen at the junction between two exons , to distinguish by their size the RT-PCR products from the genomic DNA PCR products . For Fat1 primer sets , sizes expected from genomic PCR amplicons , in case of genomic DNA contamination , have been indicated on Figures S4 and Figures S5 ) . Expression of the human FAT1 gene was monitored by a real time quantitative RT-PCR method using TaqMan gene expression assay reference number Hs00170627_m1 targeting the 5′ part of the FAT1 sequence ( Applied biosystem ) , or using real-time sybrgreen PCR assay ( Roche ) ( see primers below ) . The ubiquitous beta-glucuronidase ( GUS ) , was used as endogenous reference gene to normalize the data across all samples . FAT1 primers were chosen at the exon2-3 junction: forward primer: 5′- CAT TAG AGA TGG CTC TGG CG-3′; reverse primer: 5′- ATG GGA GGT CGA TTC ACG-3′ ) . ( Fw GUS: 5′-CTC ATT TGG AAT TTT GCC GAT T-3′; Rev GUS: 5′- CCG AGT GAA GAT CCC CTT TTT A-3′ ) . Primers used for other muscle genes: DHPR-Fw: 5′- CGC AAC TGG TGG GTT GCC AGC-3′; DHPR-Rev: 5′- GGC CCA TCC TCC AGC AAC GC -3′; MURF1-Fw: 5′- CTT GAC TGC CAA GCA ACT CA -3′; MURF1-Rev: 5′- CAA AGC CCT GCT CTG TCT TC -3′; DYSF-Fw: 5′- GAA GCC AAG GTC CCA CTC CGA -3′; DYSF-Rev: 5′- CAG GCA GCG GTG TGT AGG ACA -3′; Calp3-Fw: 5′- TCT CTT CAC CAT TGG CTT CGC -3′; Calp3-Rev: 5′- TGC TGC TTG TTC CCG TGC -3′; B2M-Fw: 5′- CTC TCT TTC TGG CCT GGA GG -3′; B2M-Rev: 5′- TGC TGG ATG ACG TGA GTA AAC C -3′; γSARC-Fw: 5′- CGA CCC GTT TCA AGA CCT TA-3′; γSARC-Rev: 5′- CCT CAA TTT TCC CAG CGT GA -3′ . Similar results were obtained with two other normalizing genes ( β-2-microglobulin ( B2M ) or the human acidic ribosomal phosphosprotein ( PO ) ) . Each experiment was performed in triplicate and repeated at least three times against age matched unaffected foetuses used as controls ( see Figure S6A ) . For quantitative RT-PCR experiments , relative quantities of RNA expression were calculated using the comparative cycle threshold ( ΔΔCt ) method [96] and were normalized with GUS RNA levels as endogenous reference gene . Briefly , the fold change of RNA expression levels was calculated by the equation 2−ΔΔCt , where Ct is the cycle threshold . The cycle threshold ( Ct ) is defined as the number of cycles required for the fluorescent signal to cross the threshold in qPCR . ΔCt was calculated by subtracting the Ct values of the endogenous control ( GUS ) from the Ct values of the RNA of interest ( FAT1 or control muscle genes , such as DHPR , MURF1 , DYSF , Calp3 , γ-Sarcoglycan ) . ΔΔCt was then calculated by subtracting ΔCt of the sample used as control from the ΔCt of FSHD biopsies . Human primary myoblasts unaffected by muscle disease were infected with lentivirus carrying either DUX4-fl or GFP as control for 24 hr . RNA was extracted with Qiagen RNeasy kit , DNAse'd with Ambion Turbo DNAse and reverse transcribed with Invitrogen SuperScript III according to manufacturers' instructions . Real time quantitative PCR was performed with the following primers: FAT1-f 5′ – GGA AAG CCT GTC TGA AGT GC - 3′; FAT1-r 5′ – TGT ATG TCC GGC AGA GGA AC -3′; RPL13a-f 5′ – AAC CTC CTC CTT TTC CAA GC - 3′; RPL13a-r 5′ – GCA GTA CCT GTT TAG CCA CGA - 3′ . FAT1 values were normalized to the internal standard RPL13a and expressed as percent relative to control condition . ChIP assays were performed on chromatin from fetal muscle biopsies ( tissue samples obtained as described above ) using the Magna A ChIP kit ( Millipore/Upstate ) . For chromatin preparation , muscle samples ( ∼50 mg ) were weighed , cut in small pieces , and cross-linked with 1 . 5% paraformaldehyde for 10 minutes , the cross-linking reaction being stopped by addition of glycin . Nuclei were extracted from the tissue samples by using a 2 ml dounce tissue grinder and the kit's cell lysis buffer . Chromatin was then sheared by sonication and quantified after DNA extraction . Immunoprecipitations were performed on 5 µg of Chromatin , with 3 µg of the following antibodies: anti-H3K4me3 ( 17–614 , Millipore ) , anti-H3K27me3 ( 07–449 , Millipore ) , and anti-H3K4me1 ( ab8895 , Abcam ) , following the ChIP kit instructions , using _proteinA-conjugated mareferredbeads . Immunoprecipitaded material was then washed , cross linking was reversed with proteinase K at 56° for 2 h , and DNA was extracted . The presence of individual regulatory regions in immunoprecipitated chromatin was analyzed by qPCR using sybr green ( Invitrogen ) on a Biorad CFX96 apparatus . Relative quantities of each chromatin bound fragment expression were calculated using the comparative cycle threshold ( ΔΔCt ) method again [96] and were normalized either relative to the amount of input DNA ( in the same amount of chromatin before immunoprecipitation , quantified with the same PCR ) , or with levels of the promoter region of a normalizing genes GUSB . Oligonucleotides for FAT1 Promoter are: FAT1_P1_Fw: 5′ CTT AAG TTT GCC CTG GTC GGA AGC C 3′; FAT1_P1_Rev: 5′ AAA GTC CTC GGC AGC TCC GTG ATC C 3′; Oligonucleotides for the 17/18 intronic enhancer were: FAT1_inton17_Fw: 5′ gga gtg ggg agg agg gaa gag tgg g 3′; FAT1_intron_Rev: 5 ctt ccc tct tgc tct tct tct agc c 3′ . Primers for the normalizing GUSB promoter were: GUSB_E/P_Fw: 5′ AGA GGA TGT AGA CCA GGC AAA AGC C 3′; GUSB_E/P_Rev: 5′ TAG AGG ACA GGA CAT GAC ATC AGG C-3′ . All sequences were selected based on the Encode ChIP tracks on the UCSC browser ( available with the base genome Human Mar . 2006 ( NCBI36/hg18 ) ) . DNA was hybridized on a Nimblegen HD2 . 1 genomic array consisting of 135 , 000 probes targeting the 4q35 . 2 genomic region . Probes were designed with a spacing of 10 bp between consecutives probes in the exonic/intronic regions and 100 bp in the intergenic regions . Labeling of DNA and hybridization was based on the Nimblegen protocols . Arrays were scanned with the MS200 scanner ( Roche ) and the acquired paired files were analyzed using the CGHweb algorithms [97] . To visualize the deletion/duplication events , coordinates were formatted as a bed file and added in the custom tracks of the genome browser ( genome . ucsc . edu ) . Patients that had CNVs in the intron 16–17 area are refered to with the number corresponding to their number in the patient summary table ( Table S1 ) . A first PCR validation was performed using primers flanking the deleted area . This approach is expected to yield a PCR product of approximately 1500 bp from a control locus , and a smaller size product in the case of the deleted allele , the deletions being approximately 1 kb long . Primer sequences are: Del-Fw: 5′- CCT TCA CCT GCA GTA AG-3′; Del-Rev: 5′- CTA GGA TTC CTA AGA GC -3′ . This approach led to validate the presence of both the 1500 band and the smaller band in the three independent patients ( numbers 11 , 12 and 25 ) , plus patient 13 ( sibling of 12 ) , as carrying a deleted allele as well . Moreover , 20 unaffected controls were tested with this method and only yielded the control 1500 bp band , indicating the absence of deleted alleles . Further validation of the deletion was performed by quantitative PCR , comparing the relative amount of PCR products scanning the deleted area , using as reference a PCR product outside the considered zone , a method used to quantify copy number variations [96] , [98] . All DNA samples , whether from healthy controls or from FSHD patients , were normalized with the ADORA Reference PCR , against one healthy control DNA used as standard DNA ( set to 100% ) . The reference primers are as follows: chr17p12 : ADORA2B-2F: 5′-GTC ACT CTT TTC CAG CCA GC-3′ ; ADORA2B-2R:5′-AAG TCT CGG TTC CGG TAA GC – 3′ . The primers corresponding to the deleted area were as follows: qPCR-primer-1: 5′ – GCA ACA GAG GCC AAT GGA AA – 3′; qPCR-primer-2: 5′ – CTG AAA AGA TTT CAG GTT ACA CGC T – 3′; qPCR-primer-3: 5′ – TTC GGT AAG ATG GGA GCA GCC TTC C - 3′; qPCR-primer-4: 5′ – GGT CCT GAC AAG CTA ATC CTG AGG G - 3′; qPCR-primer-5: 5′ – GGA GTG TGG TGT GTT CTA GGT TAT GG – 3′; qPCR-primer-6: 5′ – AGC AGA CAA GAG CAC AAG GCA TTT C – 3′; qPCR-primer-7: 5′ – GGA ACA CAG CCA AAT CTA TAT GGG – 3′; qPCR-primer-8: 5′ – TCT TCC TCC TCA CAC TCC CTT TC – 3′; qPCR-primer-9: 5′ – CCT GGG CAA TGA GTG TAA CTC C – 3′; qPCR-primer-10: 5′ – CCA ACC TCC TCC CTA CTC CAC TT -3′; qPCR-primer-11: 5′ – CCA GTG GCA GCA GGT CTG ATT AAG C - 3′; qPCR-primer-12: 5′ – GGG AAA CGT AGA ATT CAA GAA GTC GC - 3′ ( primers numbered as in Figure 10A and Figure S16A ) . Among contraction-independent cases , a large proportion , referred to as FSHD2 , harbours hypomethylated D4Z4 [63]–[64] , while others do not and are expected to carry unrelated causal abnormalities . Hypomethylation was assessed for 8 patients as indicated in the patient summary table , by digesting genomic DNA , with BlnI , CpoI and Eco91I , and hybridizing the southern blot with the p13E-11 probe , as described [64] . Numbers indicated represent the percentage of methylated proximal D4Z4 unit at chromosome 4q35 . Results were expressed as the mean ± s . e . m . Statistically significant differences were assessed by unpaired t-Student test , or Mann-whitney test for non-Normally distributed data , X2 test or Fischer tests ( for linkage studies ) , calculated with the StatEL add-in program to excel . The Kaplan-Meier plot was made with the StatEL add-in program to excel , and P value was calculated with the logrank test . * indicates P value<0 . 05; ** indicates P value<0 . 001 .
|
Facioscapulohumeral muscular dystrophy ( FSHD ) is a hereditary human myopathy affecting groups of skeletal muscles in the face and shoulders . Despite recent advances on the molecular cascade initiated by its main genetic cause , with identification of DUX4 as the main pathogenic agent , how this leads to the specific clinical picture is still poorly understood . Here , we investigated the role of the FAT1 protocadherin gene , located near the FSHD locus , which was repressed by DUX4 in human muscle cells . Disruption of the mouse Fat1 gene causes muscular and non-muscular phenotypes highly reminiscent of FSHD symptoms . We show that Fat1 is required in migrating muscle precursors , and that the altered muscle shapes caused by Fat1 mutations are predictive of early onset defects in muscle integrity in adult mutants , with a topography matching the map of muscles affected in FSHD . In humans , we observed FAT1 lowering in muscle but not brain of foetal cases with canonical FSHD1 , and identified deletions of conserved elements in the FAT1 locus predictive of changes in FAT1 expression , that were enriched among FSHD patients . Thus , deregulating Fat1 in FSHD-related tissues provides a unique means to mimic FSHD symptoms in mice and learn about pathogenesis of this complex disease .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"medicine",
"gene",
"regulation",
"anatomy",
"and",
"physiology",
"cadherins",
"dna",
"transcription",
"animal",
"models",
"developmental",
"biology",
"model",
"organisms",
"molecular",
"development",
"skeletal",
"development",
"molecular",
"genetics",
"morphogenesis",
"chromatin",
"myocytes",
"musculoskeletal",
"system",
"cell",
"adhesion",
"adhesion",
"molecules",
"birth",
"defects",
"gene",
"expression",
"muscle",
"fibers",
"biology",
"mouse",
"muscle",
"cells",
"congenital",
"hereditary",
"myopathies",
"cell",
"migration",
"genetics",
"cellular",
"types",
"musculoskeletal",
"anatomy",
"human",
"genetics",
"molecular",
"cell",
"biology",
"genetics",
"of",
"disease"
] |
2013
|
Deregulation of the Protocadherin Gene FAT1 Alters Muscle Shapes: Implications for the Pathogenesis of Facioscapulohumeral Dystrophy
|
HIV-1 reservoirs preclude virus eradication in patients receiving highly active antiretroviral therapy ( HAART ) . The best characterized reservoir is a small , difficult-to-quantify pool of resting memory CD4+ T cells carrying latent but replication-competent viral genomes . Because strategies targeting this latent reservoir are now being tested in clinical trials , well-validated high-throughput assays that quantify this reservoir are urgently needed . Here we compare eleven different approaches for quantitating persistent HIV-1 in 30 patients on HAART , using the original viral outgrowth assay for resting CD4+ T cells carrying inducible , replication-competent viral genomes as a standard for comparison . PCR-based assays for cells containing HIV-1 DNA gave infected cell frequencies at least 2 logs higher than the viral outgrowth assay , even in subjects who started HAART during acute/early infection . This difference may reflect defective viral genomes . The ratio of infected cell frequencies determined by viral outgrowth and PCR-based assays varied dramatically between patients . Although strong correlations with the viral outgrowth assay could not be formally excluded for most assays , correlations achieved statistical significance only for integrated HIV-1 DNA in peripheral blood mononuclear cells and HIV-1 RNA/DNA ratio in rectal CD4+ T cells . Residual viremia was below the limit of detection in many subjects and did not correlate with the viral outgrowth assays . The dramatic differences in infected cell frequencies and the lack of a precise correlation between culture and PCR-based assays raise the possibility that the successful clearance of latently infected cells may be masked by a larger and variable pool of cells with defective proviruses . These defective proviruses are detected by PCR but may not be affected by reactivation strategies and may not require eradication to accomplish an effective cure . A molecular understanding of the discrepancy between infected cell frequencies measured by viral outgrowth versus PCR assays is an urgent priority in HIV-1 cure research .
Treatment of HIV-1 infection with highly active antiretroviral therapy ( HAART ) can reduce plasma HIV-1 RNA levels in treated patients to below the detection limit of clinical assays ( 50 copies of HIV-1 RNA/ml ) [1]–[3] . The effective suppression of viremia initially encouraged hopes that the virus could be eradicated with two to three years of HAART [3] . However , a latent form of HIV-1 infection persists in vivo [4] , [5] . A small fraction of resting memory CD4+ T cells carry integrated viral genomes . These cells do not produce virus particles while in the resting state , but can give rise to replication-competent virus following cellular activation [4] , [5] . These latently infected cells are rare but stable , even in patients on prolonged HAART [6]–[11] . Interruption of HAART leads to a rebound in viremia [12] , [13] , typically from an archival variant [14] . The latent reservoir is widely recognized as the major barrier to HIV-1 eradication [15] . Strategies aimed at reactivating latent virus and thereby accelerating the clearance of the latent reservoir are now in advanced pre-clinical testing or early clinical trials [15] . Approaches for the reactivation of latent HIV-1 include T cell activating cytokines [16]–[19] , T cell receptor and T cell receptor signaling pathway agonists [20]–[24] , histone deacetylase inhibitors [25]–[27] , DNA methylase inhibitors [28] , [29] , and compounds like 5-hydroxynaphthalene-1 , 4-dione [30] and disulfiram [31] . A single dose of the histone deacetylase inhibitor suberoylanilide hydroxamic acid ( SAHA ) has recently been shown to increase cell-associated HIV-1 RNA in CD4+ T cells from patients on HAART [32] . A major question for current and future trials of eradication strategies is how to evaluate the effectiveness of the interventions . The principal approach for quantifying HIV-1 persistence during HAART is a viral outgrowth assay performed on highly purified resting CD4+ T cells . These cells do not produce virus without stimulation [4] . In the assay , limiting dilutions of resting CD4+ T cells are stimulated with the mitogen phytohemagglutinin ( PHA ) or with anti-CD3 plus anti-CD28 antibodies in the presence of irradiated allogeneic peripheral blood mononuclear cells ( PBMC ) [5] , [6] , [9] , [10] , [33] . These stimuli induce global T cell activation , which reverses latency at least in a fraction of cells carrying integrated HIV-1 genomes . The viruses released from these cells are expanded in CD4+ lymphoblasts from HIV-1-negative donors and detected after 2–3 weeks by an ELISA assay for HIV-1 p24 antigen in the supernatant . This assay detects individual latently infected cells that release replication-competent virus following cellular activation . The frequency of latently infected cells , expressed in terms of infectious units per million ( IUPM ) resting CD4+ T cells , is determined using Poisson statistics and is on the order of 0 . 1–10 IUPM in most patients on long term HAART . The value of this assay is that it detects cells that can , when activated , release viruses capable of robust replication . It therefore provides a minimum estimate of the frequency of latently infected cells that that must be eliminated to ensure eradication and is used here as a standard for comparison . In principle , this assay can also detect resting CD4+ T cells harboring labile unintegrated forms of HIV-1 DNA [34] , [35] , although the frequency of cells containing unintegrated DNA during HAART is low [36]–[38] . Although the viral outgrowth assay has important advantages , it is expensive and labor intensive , and it requires large amounts of blood ( 120–180 ml ) . Alternative approaches generally involve PCR assays for HIV-1 DNA . Some of these assays distinguish between integrated proviruses and unintegrated HIV-1 DNA [39] , [40] . A problem with all PCR-based assays is that they fail to distinguish between replication-competent and defective forms of the viral genome . A significant but poorly characterized proportion of infected resting CD4+ T cells contain proviruses that are defective , hypermutated , or silenced [41] , [42] . PCR assays are now also being used to quantify HIV-1 DNA in CD4+ T cells from the gut associated lymphoid tissue ( GALT ) , where the frequency of HIV-1 infection is generally higher than in the blood [43] , [44] . Highly sensitive PCR methods are also now used to quantify HIV-1 RNA in cells [32] . Free virus particles are also found in the plasma of patients on HAART [45]–[47] . This residual viremia is an important indication of ongoing virus production . Several studies have shown that residual viremia is not reduced by treatment intensification [48]–[50] , and thus it is likely to reflect virus production from stable reservoirs . For example , residual viremia could in part reflect virus production by latently infected cells that have become activated . It is currently unclear which assay ( s ) should be used to monitor HIV-1 reservoirs in clinical trials of eradication strategies . The development of a high-throughput scalable assay to measure the latent reservoir in patients has been identified as a key priority in HIV-1 eradication research [51] . Here we present a comparative analysis of eleven different approaches for measuring for HIV-1 reservoirs in two well characterized cohorts of patients on long term HAART . The goal of the study was to determine how these assays correlate with the viral outgrowth assay . The results provide insights into how reservoirs should be evaluated in future clinical trials aimed at curing HIV-1 infection .
The baseline characteristics of the cohort are shown in Table 1 . Of 30 study participants , 10 started HAART during acute or early HIV-1 infection while the remaining 20 started HAART during chronic infection . The mean ( ±SD ) age was 53 . 2±9 . 6 years . Patients starting therapy during acute/early infection were slightly younger ( 47 . 8±9 . 3 vs . 55 . 9±8 . 7 years ) . The majority ( 76 . 7% ) of study subjects were white/non-Hispanic . The current CD4+ T cell counts were not significantly different between patients starting HAART during acute/early vs . chronic infection ( 727±287 vs . 672±144 , P = 0 . 58 ) . For patients starting HAART during chronic infection , the CD4 nadir was 202±138 cells/µl . The average duration of viral load suppression on HAART was 5 . 8±2 . 5 years for patients starting HAART during acute/early infection and 8 . 0±4 . 2 years for patients starting HAART during chronic infection . No patient in either cohort had a documented “blip” above 40 copies RNA/mL in the year preceding the blood draw . Samples were processed , split , and sent to laboratories with expertise in the assays described above . Each assay was developed independently by the relevant laboratory , with different input material , assay methodology , normalization method , statistical characteristics , and caveats . These are indicated in Table 2 . The cell types analyzed and viral species detected in each assay are indicated in Table 3 . Except for the single copy assay for plasma HIV-1 RNA , assay results are presented in the form of infected cell frequencies to facilitate cross-assay comparisons . However , the cell populations included and viral species detected in each assay ( Table 3 ) must be kept in mind in interpreting these frequencies . The statistical characteristics of each assay , such as the coefficient of variation , the limit of detection , and the dynamic range ( summarized in Table 2 ) are important considerations in choosing assays to monitor viral persistence . For example , the coefficient of variation for the viral outgrowth assay is considerably higher than that for most PCR-based assays . Statistical characteristics must also be considered in evaluating the correlations between the results of different assays because problems related assay precision , accuracy , and sensitivity can obscure correlations . Table 2 also describes the primers used for each PCR assay . Negative results for any single PCR assay on a given patient sample can reflect sequence variation in the primer binding site [47] . Replication-competent HIV-1 was isolated from purified resting CD4+ T cells from peripheral blood in 29/30 study participants ( Figure 1 ) . Infected cell frequencies showed a log normal distribution with a geometric mean frequency of 0 . 64 IUPM , consistent with previous reports [6] , [9] , [10] , [52] . Latently infected cells were readily detected by this method in patients starting HAART during acute/early HIV-1 infection ( mean = 0 . 28 IUPM ) . The mean frequency of latently infected cells was significantly lower in patients starting HAART in acute/early HIV-1 infection compared to those starting during chronic infection ( 0 . 28 vs . 0 . 97 IUPM , P = 0 . 048 ) , although there was substantial overlap between the two populations ( Figure 1 ) . The frequency of latently infected cells was not correlated with the time between infection and the initiation of a suppressive HAART regimen ( r = 0 . 18 , P = 0 . 34 ) , suggesting that the size of the reservoir does not increase continuously during untreated HIV-1 infection . For a single study participant , replication-competent virus was not detected even after repeat culture . Based on input cell number , the frequency of latently infected cells in this patient was <0 . 06 IUPM . The limit of detection of this assay is determined by the input number of resting CD4+ T cells . With a 180 ml blood sample , the average yield of resting CD4+ T cells in millions was 28 . 3±14 . 7 . With therapeutic strategies that reduce the reservoir by more than 1 . 5 logs , latently infected cells would no longer be detectable in the majority of patients unless larger blood volumes or leukopheresis samples were used ( Figure 1 ) . A simple approach for quantifying persistent HIV-1 is to measure HIV-1 DNA using PCR in unfractionated PBMC . This was done using a droplet digital PCR approach that has greater accuracy than standard real time PCR methods , particularly with low template numbers [53] . HIV-1 DNA was detected in 28/30 PBMC samples ( Figure 1 ) . Values varied over a ∼2 log range with a geometric mean value of 74 copies/106 PBMC for the entire cohort . Two subjects ( 2113 and 2453 ) had values that were below the limit of detection ( 2 copies/106 PBMC ) . Interestingly , for these two subjects , cells with replication-competent virus were readily measured in the virus outgrowth assay ( 5 . 4 and 0 . 1 IUPM , respectively ) . HIV-1 DNA values were generally lower in subjects starting HAART in acute/early HIV-1 infection compared to those starting during chronic infection ( geometric mean values 47 vs . 93 copies/106; P = 0 . 30 ) . The frequency of PBMC with HIV-1 DNA showed a modest but significant correlation with the time between infection and the initiation of a suppressive HAART regimen ( r = 0 . 38 , P = 0 . 037 ) . To determine whether HIV-1 DNA levels in PBMC could be used as a surrogate measure of the size of the latent reservoir , we examined the correlation between results of the viral outgrowth assay on purified resting CD4+ T cells and the droplet digital PCR assay for HIV-1 DNA in PBMC from the same blood sample . As shown in Figure 2A and Table 4 , there was essentially no correlation between the two assays ( r = 0 . 20 , P = 0 . 29 ) for the combined study population . Based on 95% confidence intervals for r , a strong correlation ( r>0 . 6 ) could be excluded ( Table 4 ) . For patients treated during acute/early infection , a modest correlation that did not reach statistical significance was observed ( r = 0 . 46 , P = 0 . 18 ) , but there was no correlation between the two assays for patients initiating HAART during chronic infection ( r = −0 . 038 , P = 0 . 87 ) . We next examined whether the correlation between the results of the viral outgrowth and PCR assays might be improved if HIV-1 DNA levels were measured in purified resting CD4+ T cells instead of unfractionated PBMC . HIV-1 DNA was detected in 14/16 samples ( Figure 1 ) . The geometric mean level of HIV-1 DNA was 186 copies/106 resting CD4+ T cells . In two patients ( #2013 , 2418 ) , levels were below the limit of detection ( 2 copies/106 resting CD4+ T cells ) . For these two subjects , cells with replication-competent virus were readily measured in the virus outgrowth assay ( 1 . 05 and 0 . 04 IUPM , respectively ) . As a measure of infected cell frequency , the PCR assay gave substantially higher values than did the viral outgrowth assay performed on the same blood samples ( 186 vs . 0 . 62 infected cells/106 resting CD4+ T cells , P<0 . 0001 ) . This difference has been noted previously [5] and may reflect the high fraction of proviruses that are defective [41] , [42] . Cells harboring defective viral genomes could accumulate over time during untreated disease . We did not , however , observed a significant correlation between the frequency of resting CD4+ T cells with HIV-1 DNA and the time between initial infection and suppression of viral replication on HAART ( r = 0 . 33 , P = 0 . 20 ) , the time on a suppressive HAART regimen ( r = 0 . 076 , P = 0 . 78 ) , or the total time since infection ( r = 0 . 30 , P = 0 . 26 ) . If the proportion of defective proviruses was the same in different patients , then the measurement of HIV-1 DNA in resting CD4+ T cells from patients on HAART might provide a simpler surrogate measure of the latent reservoir . However , as is shown in Figure 3 , the ratio of infected cells detected in the PCR vs . viral outgrowth assays is highly variable from patient to patient ( range <2 to 3540 ) even when both assays are performed on the same sample of purified resting CD4+ T cells . A subset of patients who initiated therapy during chronic infection showed very high ratios ( >1000∶1 ) . For this reason , there is only a modest correlation between the results of the two assays for the combined population ( Figure 2B and Table 4 , r = 0 . 45 , P = 0 . 08 ) . In patients treated during chronic infection , no correlation is observed ( r = 0 . 10 , P = 0 . 76 ) The levels of HIV-1 DNA in unfractionated PBMC and in purified resting CD4+ T cells showed a strong correlation ( r = 0 . 78 , P = 0 . 0004 ) . This reflects the fact that in patients on HAART , the stable reservoir for HIV-1 is located primarily in resting CD4+ T cells [6] . For most patients , infected cell frequencies were higher in resting CD4+ T cells than in PBMC ( Figure 1 ) . This is the expected result if most of the infected cells in the blood are resting CD4+ T cells . However , if substantial numbers of activated T cells and monocytes are infected , then the resting CD4+ T cell value may not be higher . Chun and colleagues have shown that even in patients on HAART , infected cells frequencies as measured by DNA PCR can be higher among activated than resting CD4+ T cells [54] . Levels of integrated HIV-1 DNA were also measured in PBMC and purified resting CD4+ T cells from study participants using a previously described Alu PCR assay [39] , [40] , [55] . As shown in Figure 1 , integrated HIV-1 DNA was detected in 19/19 PBMC samples , at a geometric mean frequency of 186 copies/106 PBMC . The frequencies were significantly lower in patients starting HAART in acute/early vs . chronic infection ( 84 vs 286 copies/106 PBMC , P = 0 . 04 ) . As was observed with the droplet digital PCR assay for HIV-1 DNA , levels of integrated HIV-1 DNA were higher in purified resting CD4+ T cells than in unfractionated PBMC ( geometric mean values 604 vs 186 copies/106 cells , Figure 1 ) . Also consistent with the results of the droplet digital PCR assay was the finding that the frequency of resting CD4+ T cells with integrated HIV-1 DNA was much higher than the frequency of latently infected resting CD4+ T cells detected in the viral outgrowth assay performed on the same sample ( by 1000 fold ) . In paired samples , the mean infected cell frequencies were 604 vs . 0 . 61/106 resting CD4+ T cells ( P<0 . 0001 ) . Measurements of integrated HIV-1 DNA by Alu PCR and of total HIV-1 DNA by droplet digital PCR correlated well with each other both for samples of PBMC ( Figure 2C , r = 0 . 63 , P = 0 . 0042 ) and resting CD4+ T cells ( r = 0 . 85 , P = 0 . 0079 ) . These results are consistent with the conclusion that in patients on long term HAART , most of the HIV-1 DNA is integrated , with unintegrated forms making only a minor contribution ( see below ) [38] . The fact that infected cell frequencies detected by Alu PCR were higher ( 186 vs 46 copies/106 PBMC , P = 0 . 0003 ) may reflect differences in assay standardization . This assay provides definitive detection of integrated HIV-1 DNA through the use of an initial PCR with a primer in an Alu element and a primer in HIV-1 . For each infected cell , the distance to the nearest Alu element is different [56] . This influences amplification efficiency , and some proviruses are located too far from an Alu element to be detected . To circumvent this problem , a correction is applied based on an integration standard containing proviruses integrated at different distances from the nearest Alu element [39] , [40] , [55] . The accuracy of the method depends on how closely the distribution of provirus-Alu element distances in the standard matches the distribution of distances in a particular patient sample . It is possible that the higher levels of integrated HIV-1 DNA detected in some patients result from over-correction for proviruses missed by Alu-PCR . Alternative explanations include underestimation of infected cell frequency by droplet digital PCR or issues with the normalization methods used in one or both assays . Interestingly , there was a highly significant positive correlation between the level of integrated HIV-1 DNA in PBMC and the frequency of latently infected resting CD4+ T cells determined in the viral outgrowth assay on the same blood sample ( Figure 2D and Table 4 , r = 0 . 70 , P = 0 . 0008 ) . When only patients starting HAART during chronic infection were considered , the correlation remained significant ( r = 0 . 76 , P = 0 . 0038 ) . However , when the Alu PCR and viral outgrowth assays were performed on the same sample of purified resting CD4+ T cells , the correlation was weaker and not statistically significant ( r = 0 . 41 , P = 0 . 13 ) , possibly related to the small number of samples and small number of genomes assayed . Because the GALT is the site of very active viral replication in untreated patients with acute HIV-1 infection [57]–[61] , we measured levels of HIV-1 DNA and RNA in the rectal biopsy samples from 19 study participants using qPCR . CD4+ T cells were enumerated in each sample by flow cytometry , and results were expressed as copies per CD4+ T cell . HIV-1 DNA was readily detectable in all samples ( Figure 1 ) . The geometric mean value for these 19 samples was 3015 copies/106 CD4+ T cells . In paired comparisons , these values were significantly higher than the levels of infection of resting CD4+ T cells from the peripheral blood detected by digital droplet PCR ( 4282 vs . 263 copies/106 cells , paired t-test: P<0 . 0001 ) or by Alu PCR ( 2977 vs . 600 copies/106 cells , paired t-test: P = 0 . 0008 ) . The level of HIV-1 DNA in rectal CD4+ T cells was not significantly correlated with the frequency of latently infected resting CD4+ T cells in blood as measured by the viral outgrowth assay ( Figure 2E , r = 0 . 26 , P = 0 . 28 ) . However , a strong correlation could not be formally excluded based on 95% confidence intervals ( r = −0 . 22 to 0 . 64 , Table 4 ) . There was a significant correlation between the level of HIV-1 DNA in rectal biopsy samples and the frequency of infected cells in the peripheral blood as measured by digital droplet PCR ( r = 0 . 58 , P = 0 . 015 ) or by Alu PCR ( r = 0 . 65 . P = 0 . 016 ) . HIV-1 RNA was also detected by qRT-PCR in all samples . The geometric mean level was 1985 copies/106 rectal CD4+ T cells . These values cannot be used to establish infected cell frequencies because of the likely presence of multiple HIV-1 RNA molecules in some individual infected cells . Levels of HIV-1 RNA correlated well with measures of HIV-1 DNA in the same samples ( r = 0 . 8811 , P<0 . 0001 ) . The geometric mean RNA/DNA ratio was 0 . 68 . RNA/DNA ratios in rectal biopsies were significantly correlated with the frequency of resting CD4+ T cells in peripheral blood that scored in the viral outgrowth assay ( r = 0 . 57 . P = 0 . 013 ) . 2-LTR circles represent abortive integration events . They have been used as a measure of recent infection in some studies , although controversy remains about their stability [62]–[66] . 2-LTR circles were measured in PBMC and purified resting CD4+ T cells from study subjects using droplet digital PCR ( Figure 1 ) . Circles were detected in only 9 of 30 PBMC samples . Among these the geometric mean level was 6 . 8 copies/106 PBMC . This was 27 fold lower than the total level of HIV-1 DNA in the same samples measured by droplet digital PCR ( 162 copies/106 PBMC , P<0 . 0001 ) . As expected , levels of 2-LTR circles in purified resting CD4+ T cells from peripheral blood were higher than levels in unfractionated PBMC ( geometric mean 13 copies/106 resting CD4+ T cells ) , but again this level was much lower ( by 34 fold ) than the total level of HIV-1 DNA in the same samples measured by droplet digital PCR ( 467 copies/106 resting CD4+ T cells , P<0 . 0001 ) . These results demonstrate that 2-LTR circles make up only a small fraction of the total HIV-1 DNA measured by PCR in patients receiving on HAART . By Spearman's rank correlation analysis , levels of 2-LTR circles in PBMC or resting CD4+ T cells were not significantly correlated with infected cell frequencies measured in the viral outgrowth assay ( rho = 0 . 19 , P = 0 . 31 and rho = 0 . 38 , P = 0 . 15 respectively ) . Residual viremia was detectable in 20/30 study subjects , 13/20 in the chronic cohort and 7/10 in the acute cohort ( Figure 1 ) . Among the patients with detectable residual viremia , the geometric mean level was 0 . 78 copies/ml , consistent with previous studies [46] , [47] , [67] . There was no significant difference between the acute and chronic cohorts in the proportion of patients with detectable residual viremia ( 7/10 vs . 13/20 ) or in the level of detectable residual viremia ( 0 . 84 vs . 0 . 75 copies/ml , P = 0 . 75 ) . Scatter plots show no obvious correlation between residual viremia and the viral outgrowth assay ( Figure 2F ) . We considered the possibility that such a correlation could have been obscured by the failure to detect residual viremia in one third of the subjects . Negative results in the single copy assay could be due to primer mismatch or levels of residual viremia below the detection limit [47] . If patients for whom the single copy assay was negative are excluded , the correlation between residual viremia and the frequency of latently infected cells in the viral outgrowth assay is weak ( r = 0 . 24 , P = 0 . 33 ) . If all patients are included in the analysis and a single copy assay value of 0 . 1 copies/ml is assumed for those patients with negative results in this assay , the rank correlation is also very weak ( rho = 0 . 070 , P = 0 . 71 ) . Thus regardless of whether the negative values in the single copy assay represent primer mismatch or values below the limit of detection , it is difficult to construct a scenario where there is strong correlation between residual viremia and the viral outgrowth assay . A strong correlation could be formally excluded for the total population and for subpopulations starting HAART during acute/early or chronic infection . Residual viremia was not correlated with the level of infection in PBMC by droplet digital PCR ( rho = −0 . 25 , P = 0 . 18 ) , the level of infection of CD4+ T cells in the rectal biopsies ( rho = 0 . 12 , P = 0 . 61 ) , or the level of 2LTR circles ( rho = −0 . 109 , P = 0 . 57 ) . Because the limit of detection of this assay with a sample volume of 8 ml is 0 . 2 copies/ml , a one log reduction in the size of viral reservoirs contributing to residual viremia would render values unmeasurable .
With the discovery of new agents that reactivate latent HIV-1 , clinical trials of HIV-1 eradication strategies have begun [32] . No available clinical assay measures the size of the latent reservoir . Patients being enrolled in eradication studies have been on HAART for years and already have undetectable levels of viremia by standard clinical assays ( detection limit 50 copies/ml ) . Therefore , there is an urgent need for laboratory assays to determine the efficacy of eradication strategies . Here we compare eleven different approaches for quantitating persistent HIV-1 in patients on HAART . The analysis involved seven analytical approaches and four different kinds of tissue samples . Assays were carried out using well characterized patients on long term stable HAART . Results were compared to the viral outgrowth assay that was originally used to define the latent reservoir [5] , [6] , [9] , [10] , [33] . We first evaluated PCR assays for HIV-1 DNA in unfractionated PBMC or resting CD4+ T cells . PCR quantitation of HIV-1 DNA in unfractionated PBMC perhaps offers the best chance for a scalable , high throughput assay for the latent reservoir . Using a novel droplet digital PCR assay , we detected HIV-1 DNA in PBMC from 28/30 subjects , with a mean infected cell frequency more than 100 fold higher than the mean frequency of resting CD4+ T cells that release replication-competent virus in the viral outgrowth assay . An even greater difference was observed when the viral outgrowth and droplet digital PCR assays were run on the same sample of purified resting CD4+ T cells . In part , this difference is likely to reflect the detection by PCR methods of cells harboring defective viral genomes such as cells with viral genomes that have been lethally hypermutated by APOBEC3G [42] . The difference between viral outgrowth and PCR-based assays was also observed in patients who start HAART in acute/early HIV-1 infection , suggesting that it is not simply the result of accumulation of cells with defective viruses over time . If a constant proportion of the infected resting CD4+ T cells contained defective genomes , then PCR measurements might provide a reliable surrogate measure of reservoir size in cross-sectional analysis . However , for the whole study population and the subset initiating HAART during chronic infection , a strong correlation ( r>0 . 6 ) between results of the PCR assay for HIV-1 DNA and the viral outgrowth assay can be formally excluded . A larger sample size is needed to determine whether some correlation exists for patients initiating HAART early . It is possible that as the infection progresses , cells with defective viral sequences accumulate at different rates in different patients so that there is eventually very little correlation between the viral outgrowth assay and PCR-based assays . The ratio of infected cell frequencies determined by the two different assays varies by over 3 logs , with a subset of the patients who initiated therapy during chronic infection showing very high ratios . Taken together , these results are consistent with the hypothesis that differential accumulation of resting CD4+ T cells with defective viral sequences obscures the relationship between the frequency of cells detected in the virologic and molecular assays . Most patients start therapy during chronic infection , and it is problematic that readily scalable PCR assays for total HIV-1 DNA on PBMC do not provide a precise reflection of the size of the latent reservoir , at least for cross-sectional analysis . It remains possible that PCR assays will be useful in following individual patients participating in eradication trials , but it is not yet clear that latency-reversing strategies will cause proportionate reductions in the latent reservoir and in the total pool of cells with HIV-1 DNA . For example , some cells with defective viral genomes may not express viral genes in response to latency-reversing agents . Hypermutated genomes typically have stop codons in every open reading frame [42] , and thus cells carrying hypermutated genome may not be eliminated by latency reversing strategies that depend on viral cytopathic effects or CTL-mediated clearance . These cells might still be detected by PCR-based assays even when cells with replication-competent viral genomes were being eliminated . Initial studies established that the latent reservoir in resting CD4+ T cells consists of cells with stably integrated viral genomes , [4] , [5] . We therefore evaluated a well established Alu PCR assay specific for integrated viral genomes . Infected cell frequencies determined by this method were similar to and well correlated with frequencies determined by the droplet digital PCR assay , which does not distinguish integrated and unintegrated viral genomes ( r = 0 . 85 , P = 0 . 0079 for resting CD4+ T cells ) . These results are consistent with the notion that most of the HIV-1 DNA in resting CD4+ T cells of patients on HAART is integrated . Linear unintegrated forms , which are prevalent in untreated patients [35] , are labile [37] , [68] and are not seen in the absence of ongoing viral replication . Circular unintegrated forms ( 2-LTR circles ) , when detected , were present only at extremely low levels . Although levels of integrated HIV-1 DNA correlate well with measurements of HIV-1 DNA by the droplet digital PCR assay , both assays can detect defective as well as replication-competent proviruses . Among the approaches evaluated , analysis of integrated HIV-1 DNA in PBMC showed the best correlation with results of the viral outgrowth assay on purified resting CD4+ T cells ( Table 4 , r = 0 . 70 , P = 0 . 0008 ) . The correlation was weaker when integrated HIV-1 DNA was measured in purified resting CD4+ T cells . Because the GALT provides a major site for HIV-1 replication [57]–[61] , we also measured the HIV-1 DNA and RNA levels in rectal biopsy samples . When normalized for the number of CD4+ T cells present , the DNA PCR assay gave infected CD4+ T cell frequencies that were significantly higher than infected cell frequencies in the blood , consistent with previous studies [43] , [44] . HIV-DNA levels in rectal biopsy samples showed a modest correlation with HIV-1 DNA levels in cells from the peripheral blood , but not with results of the viral outgrowth assay . Overall , these results highlight the potential importance of the GALT as an HIV-1 reservoir . However several critical questions remain . It is important to understand the fraction of CD4+ T cells in the GALT that can produce replication-competent virus and whether or not the virus is generally latent in these sites . The HIV-1 RNA/DNA ratios measured in these samples were generally <1 , but these values are difficult to interpret because of uncertainty regarding the distribution of RNA molecules among infected cells . Interestingly , RNA/DNA ratios in rectal biopsy samples showed a statistically significant correlation with the viral outgrowth assay ( r = 0 . 57 , P = 0 . 013 ) . This may reflect the fact that the RNA∶DNA ratio provides some indication of the number of infected cells that have the capacity to produce viral RNA . As a measure of viral persistence , the single copy assay for residual viremia is of particular interest because it detects ongoing virus production in patients on suppressive HAART regimens . Residual viremia was only detectable in two thirds of study subjects and did not correlate with infected cell frequencies as measured by the viral outgrowth assay . A precise correlation might be expected if the activation of latently infected resting CD4+ T cells was the major source of residual viremia . However , recent studies [69]–[71] have show that in many patients the residual viremia is dominated by viral clones that are profoundly underrepresented in resting CD4+ T cells from the peripheral blood , suggesting an additional source of residual viremia . Another important factor in evaluating assays for persistent HIV-1 is the dynamic range . Specifically , it is important to understand how much of a reduction in the reservoir would be measurable with each assay assuming reasonable sample volumes . Among the assays evaluated , the single copy assay for residual viremia has the lowest operating range . The viral outgrowth assay is already run on large blood volumes ( 180 ml ) , and its dynamic range cannot be extended much further . PCR-based assays perhaps offer the largest dynamic range , but suffer from the caveats discussed above . Other studies have compared assays for persistent HIV-1 , and the results differ to some extent from the findings presented here . In an early study , Anton et al . compared several measures of persistent HIV-1 infection in patients on HAART [72] . Although these investigators noted some modest correlations between the results of different culture and PCR-based assays , their findings cannot be readily compared to those of the present study because the culture assays were not run on purified resting CD4+ T cells and the PCR-based assays were not normalized for input CD4 cell number . Chun et al . reported a weak but statistically significant correlation ( r = 0 . 29 , P = 0 . 001 ) between linear values for residual viremia measured with an assay that has a detection limit of 20 copies/ml and the level of HIV-1 DNA in resting CD4+ T cells [73] . Murray et al . [74] observed an accumulation of cells with HIV-1 DNA during the first two years of untreated HIV-1 infection , consistent with the differences in infected cells frequencies between the acute and chronic cohorts in our study . Cells with defective proviruses may fail to express viral proteins and may therefore be protected from viral cytopathic effects and host CTL . The cells will thus have a chance to accumulate . This may happen at different rates in different patients , greatly complicating measurement of the reservoir by PCR-based approaches . Of note , no other study has shown a precise correlation between the results of the viral outgrowth assay and any simpler assay . Overall , the results of this study suggest that no PCR-based assay provides a precise and internally consistent indication of the amount of replication-competent HIV-1 in resting CD4+ T cells . These findings raise the important issue of how to quantify decreases in the latent reservoir in future HIV-1 eradication trials . The fundamental problem is that infected cell frequencies determined by PCR-based assays are at least 2 logs higher than infected cell frequencies determined by the viral outgrowth assay . Much of this difference may be due to cells carrying defective proviruses , for example those that have been lethally hypermutated by APOBEC3G . These defective proviruses may not be eliminated by strategies designed to target latently infected cells . In this situation , successful clearance of latently infected cells might be masked by a larger and unchanging pool of cells with defective proviruses . While PCR-based assays may overestimate the size of the reservoir , the viral outgrowth assay provides only a minimal estimate of the frequency of cells harboring replication-competent virus . The assay conditions were carefully chosen to induce blast transformation in 100% of the input resting CD4+ T cells [6] . In this situation , the failure of an infected cell to produce replication-competent virus can be due to defects in the provirus such as APOBEC3G-induced hypermutation [42] or large internal deletions [41] . However , epigenetic silencing [75] , transcriptional interference [76] , [77] , and other factors could also in principle prevent some proviruses without intrinsic defects from scoring in the viral out growth assay . Importantly , no other culture assay , including those that use alternative T cell activating stimuli [7] , [8] , has detected a higher frequency of cells with replication-competent virus . Nevertheless , the development of assays that precisely quantitate the number of the latently infected cells that have the potential to release replication-competent virus in vivo is an important goal in eradication research .
This study enrolled 30 patients from two well established cohorts at the University of California San Francisco ( UCSF ) . All study subjects provided informed consent . Twenty patients were from the SCOPE cohort , an ongoing longitudinal study of ∼1500 HIV-1-infected and uninfected adults . Infected individuals in this cohort started HAART during chronic infection ( >180 days from estimated date of infection ) . Subjects were seen and interviewed at four-month intervals to ascertain: ( 1 ) current medications , ( 2 ) medication adherence , ( 3 ) recent intercurrent illnesses , and ( 4 ) recent diagnoses or hospitalizations . Plasma HIV-1 RNA levels and routine T cell immunophenotyping were performed at each visit . Ten patients were recruited from the OPTIONS cohort , an ongoing longitudinal study of adults enrolled within 8 months of HIV-1 infection . The evaluation of patients with possible acute infection included detailed HIV-1 testing and exposure history and the following laboratory studies: ( 1 ) a high-sensitivity assay for plasma HIV-1 RNA , ( 2 ) a standard HIV-1 antibody EIA with western blot confirmation if positive , and ( 3 ) a less-sensitive ( detuned ) antibody EIA ( LS-EIA ) . Screened subjects met one or more of the following criteria to be defined as having primary or recent HIV-1 infection: ( 1 ) repeated plasma HIV-1 RNA >5 , 000 copies/ml combined with a negative or indeterminate HIV-1 antibody test; ( 2 ) seroconversion within 6 months of a documented negative HIV-1 antibody test or ( 3 ) a history compatible with primary HIV-1 infection ( including no prior positive HIV-1 antibody tests ) and laboratory testing consistent with recent infection on the “detuned” antibody EIA . Eligible subjects were followed approximately every 3 months . Eligibility criteria for all patients enrolled in the present study included: ( 1 ) confirmed HIV-1 infection , ( 2 ) documented prior initiation of one of the Department of Health and Human Services recommended/alternative HAART regimens [78] , ( 3 ) at least 36 months of continuous HAART at study entry with no regimen changes in the preceding 24 weeks , ( 4 ) maintenance of plasma HIV-1 RNA levels below the limit of detection of conventional assays for at least 36 months ( intermittent isolated episodes of detectable low-level viremia were allowed ) , ( 5 ) most plasma HIV-1 RNA levels below the level of detection ( <40 copies RNA/ml ) , and ( 6 ) documented CD4+ T-cell count above 350 cells/µl for preceding 24 weeks . We excluded subjects who ( 1 ) had recent hospitalization , ( 2 ) recent infection requiring systemic antibiotics , ( 3 ) recent vaccination or ( 4 ) exposure to any immunomodulatory drug ( including maraviroc ) in the preceding 16 weeks . All subjects who met entry criteria for screening and agreed to participate in the study had an initial structured interview , phlebotomy , and HIV-1 testing using the established SCOPE and OPTIONS infrastructure . Once eligibility for the large volume blood draw ( 220 ml ) and gut biopsy procedures were determined , blood was collected in tubes containing acid-citrate-dextrose ( ACD ) , and 180 ml of the sample were shipped overnight at ambient temperature to the Johns Hopkins School of Medicine where peripheral blood mononuclear cells ( PBMC ) and resting CD4+ T lymphocytes were isolated for quantitative studies of proviral DNA and replication competent virus . Extensive previous studies have shown that cells can be recovered with high viability for virus culture assays after overnight shipment [79] . The remaining peripheral blood ( 40 ml ) was sent to the UCSF AIDS Specimen Bank for studies of the host response and additional virologic studies . At John Hopkins , PBMC were isolated using density gradient centrifugation . Supernatant plasma was frozen at −80°C and sent on dry ice to Dr . Sarah Palmer of the Karolinska Institute in Stockholm , Sweden for the single copy assay for plasma HIV-1 RNA [46] . Aliquots of PBMC were frozen as cell pellets for analysis of total and integrated HIV-1 DNA . Resting CD4+ T cells were purified from PBMC by negative depletion using biotinylated antibodies and anti-biotin magnetic beads . Briefly , CD4+ T lymphocytes were first isolated from PBMC by removing unwanted cell populations ( CD4+ T cell Isolation Kit II; Miltenyi ) . Non-CD4+ T cells were labeled first with a cocktail of biotin-conjugated monoclonal antibodies followed by incubation with anti-biotin-conjugated magnetic microbeads . Unwanted cells were then removed using a LS MACS Column with the MACS Separator magnet ( Miltenyi ) . Activated CD4+ T lymphocytes were then removed from the total CD4+ T cell population by labeling unwanted cells with biotin-conjugated antibodies to CD25 , CD69 , and HLA-DR and anti-biotin microbeads ( Miltenyi ) . Labeled cells were magnetically removed using MACS MS columns with the MACS separator . The resting CD4+ T cell population was typically 98–99% pure as assessed by FACS analysis . Latent HIV-1 in resting CD4+ T cells was quantitated using the viral outgrowth assay ( see below ) . Aliquots of purified resting CD4+ T cells were frozen as cell pellets and sent along with aliquots of unfractionated PBMC to UCSD and the University of Pennsylvania for assays of total and integrated HIV-1 DNA . Details of individual assays are given below . A subset of study subjects underwent a rectosigmoid biopsy at San Francisco General Hospital . Up to 30 3-mm biopsies were obtained 10–30 cm above the anus with a disposable biopsy forceps ( 3 . 3-mm outside diameter ) . Biopsy specimens were suspended in RPMI 1640 containing 10% fetal calf serum , piperacillin–tazobactam ( 500 µg/ml ) , and amphotericin B ( 1 . 25 µg/ml ) , and transported within 2–3 h to the UCSF Core Immunology Laboratory where the tissue was digested with collagenase and needle shearing ( see below ) . Cells were counted and frozen as cell pellets until analyzed for HIV-1 DNA and RNA . Blood and rectal biopsies from patients were obtained through protocols approved by the UCSF Committee on Human Research . For the virus culture assay , blood was obtained from healthy donors through a protocol approved by the Johns Hopkins University School of Medicine Internal Review Board #4 . All study subjects provided written informed consent prior to participation in the study . Acute/early and chronic cohorts were compared using 2-tailed t tests for independent samples . Log transformed virologic data met the D'Agostino-Pearson test for Normal distribution , and correlations were performed on log transformed data . For DNA PCR measurements of cell associated HIV-1 genomes , we assumed that infected cells carried a single provirus [84] and expressed the results as the frequency of infected cells . For culture assay and HIV-1 DNA data , one or two samples were below the limit of detection of the relevant assays . For these , an imputed value representing the lower of the limit of detection or a value corresponding to 1 percentile of a log normal distribution fitted to the measured values was used in the calculation of the Pearson correlation coefficient . In the case of the single copy assay for HIV-1 RNA in plasma , for which one-third of the measurements were below the limit of detection ( 0 . 2 copies/ml ) , a low imputed value of 0 . 1 was used to calculate the Spearman rank correlation coefficient . Similar results were obtained when an imputed value of 0 . 01 was used . Data analysis was done using Microsoft Excel and MedCalc software .
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Efforts to cure HIV-1 infection have focused on a small pool of CD4+ T cells that carry viral genetic information in a latent form . These cells persist even in patients on optimal antiretroviral therapy . Novel therapeutic strategies targeting latently infected cells are being developed , and therefore practical assays for measuring latently infected cells are urgently needed . These cells were discovered using a virus culture assay in which the cells are induced to release virus particles that are then expanded in culture . This assay is difficult , time-consuming , and expensive . Here we evaluate alternative approaches for measuring persistent HIV-1 , all of which rely on the detection of viral genetic information using the polymerase chain reaction ( PCR ) . None of the PCR-based assays correlated precisely with the virus culture assay . The fundamental problem is that infected cell frequencies determined by PCR are at least 2 logs higher than frequencies determined by the culture assay . Much of this difference may be due to cells carrying defective forms of the virus . These cells may not be eliminated by strategies designed to target latently infected cells . In this situation , successful clearance of latently infected cells might be masked by a large unchanging pool of cells carrying defective HIV-1 .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"infectious",
"diseases"
] |
2013
|
Comparative Analysis of Measures of Viral Reservoirs in HIV-1 Eradication Studies
|
Tuberculosis ( TB ) disease in HIV co-infected patients contributes to increased mortality by activating innate and adaptive immune signaling cascades that stimulate HIV-1 replication , leading to an increase in viral load . Here , we demonstrate that silencing of the expression of the transcription factor nuclear factor of activated T cells 5 ( NFAT5 ) by RNA interference ( RNAi ) inhibits Mycobacterium tuberculosis ( MTb ) -stimulated HIV-1 replication in co-infected macrophages . We show that NFAT5 gene and protein expression are strongly induced by MTb , which is a Toll-like receptor ( TLR ) ligand , and that an intact NFAT5 binding site in the viral promoter of R5-tropic HIV-1 subtype B and subtype C molecular clones is required for efficent induction of HIV-1 replication by MTb . Furthermore , silencing by RNAi of key components of the TLR pathway in human monocytes , including the downstream signaling molecules MyD88 , IRAK1 , and TRAF6 , significantly inhibits MTb-induced NFAT5 gene expression . Thus , the innate immune response to MTb infection induces NFAT5 gene and protein expression , and NFAT5 plays a crucial role in MTb regulation of HIV-1 replication via a direct interaction with the viral promoter . These findings also demonstrate a general role for NFAT5 in TLR- and MTb-mediated control of gene expression .
Mycobacterium tuberculosis ( MTb ) , the causative agent of tuberculosis ( TB ) , is the most common co-infection and cause of death in patients infected with human immunodeficiency virus type 1 ( HIV-1 ) [1] , [2] . Direct engagement of pathogen recognition receptors ( PRRs ) by MTb on mononuclear phagocytes activates signaling cascades that directly induce transcription from the proviral LTR ( reviewed in [3] ) . Furthermore , inflammatory cytokines and chemokines produced by the human host in response to MTb infection activate signal transduction pathways in CD4 T cells and monocytic cells that also result in transcriptional activation of the HIV-1 LTR [4]–[6] . Activation of HIV-1 replication via these MTb-induced pathways ultimately leads to higher viral loads and , in turn , expedited CD4 T cell loss and progression to AIDS ( [7] , reviewed in [8]–[10] ) . Furthermore , the progressive immune compromise associated with HIV-1 infection itself is a major cause of latent MTb reactivation , as well as increased susceptibility to primary TB infection ( [11]–[15] , reviewed in [8] ) . The primary PRR on monocytic cells triggered by MTb infection is toll-like receptor ( TLR ) 2 [16]–[20] . Engagement of TLR2 results in engagement of the adaptor protein MyD88 and the subsequent recruitment of several kinases , including IRAK1 and IRAK4 , and the ubiquitin ligase TRAF6 ( [21]–[23] , reviewed in [10] , [24] ) . TRAF6 activates IκB kinase ( IKK ) and mitogen-activated protein ( MAP ) kinases that , in turn , ultimately induce activation of specific transcription factor families , including the NF-κB and AP-1 families , which have been shown to associate with the HIV-1 LTR and to drive its transcription ( [22] , [25]–[27] , reviewed in [10] ) . Notably , HIV-1 comprises several subtypes , and the LTR of each subtype is unique with respect to the number and organization of activator binding sites . For example , HIV-1 subtype B , the most highly characterized viral subtype and the primary cause of infection in the Americas , Europe , Japan , and Australia , has two tandem NF-κB motifs in its LTR . By contrast , HIV-1 subtypes C and E , which have spread disproportionately in TB-burdened sub-Saharan Africa and southeast Asia , have three and one NF-κB binding sites , respectively [1] , [28]–[30] . We previously showed that the most primordial member of the nuclear factor of activated T cells ( NFAT ) family , NFAT5 ( also known as TonEBP ) , binds to a site within the HIV-1 LTR that is highly conserved across all HIV-1 subtypes , and is also conserved in HIV-2 and SIV LTRs . This NFAT5 site overlaps the core NF-κB binding motifs in the LTR and is required for constitutive replication of representative HIV-1 subtype B , C , and E isolates in human primary monocyte-derived macrophages ( MDM ) [31] . Given that NFAT5 has previously been shown to be transcriptionally activated by the MAP kinase p38 , which is downstream of MyD88 signaling , [32] , we speculated that NFAT5 may also be involved in MTb-induced activation of HIV-1 replication via a TLR-mediated pathway in monocytes and peripheral blood mononuclear cells ( PBMC ) . Here , we show that NFAT5 and its cognate binding site are of crucial importance for efficient MTb-induced stimulation of HIV-1 replication in human MDM and PBMC . Moreover , we demonstrate that MTb infection increases NFAT5 gene expression in human monocytes in a MyD88-dependent manner . Thus , these results expand the known stimuli of NFAT5 expression to the PRR-mediated innate immune response , and demonstrate that NFAT5 is a critical modulator of MTb-induced enhancement of HIV-1 replication .
In our studies we used unidentified human discarded blood cells ( peripheral blood mononuclear cells , PBMC ) , which we obtained from the Blood Bank of Children's Hospital in Boston . PBMC from normal unidentified donors were isolated by Ficoll-Hypaque ( Pharmacia Corporation , Peapack , NJ ) density gradient centrifugation and were cultured in RPMI 1640 medium with 2 mM L-glutamine ( BioWhittaker , Inc . , Walkersville , MD ) supplemented with 10% heat-inactivated fetal calf serum ( FCS ) ( Gemini Bio-Products , www . gembio . com ) . Human monocytes were isolated from PBMC preparations by positive selection with CD14 microbeads from Miltenyi Biotec ( www . miltenyibiotec . com ) as described by the manufacturer , and were cultured at 1×106 cells per well in 6-well plates in Macrophage-SFM medium ( Gibco , www . invitrogen . com ) supplemented with 15 ng/ml recombinant human MCSF ( R&D , www . rndsystems . com ) and 5% heat-inactivated human AB serum ( Nabi , Boca Raton , FL ) . The cell cultures were incubated at 37°C and 5% CO2 for 5 days , after which supernatant was replaced with fresh medium lacking MCSF before manipulation . More than 95% of the adherent cells obtained with this technique were CD14+ macrophages as verified by flow cytometry . THP-1 cells were obtained from ATCC ( www . atcc . org ) and cultured in RPMI 1640 medium supplemented with 10% FCS ( BioWhittaker , www . lonzabio . com ) . 293T cells were obtained from ATCC ( www . atcc . org ) and were maintained in Dulbecco's Modified Eagle's medium ( DMEM ) ( Gibco , www . invitrogen . com ) supplemented with 10% FCS . HIV-1Bal , HIV-1Lai , HIV-193TH64 , HIV-192TH51 , HIV-192TH53 , HIV-198CH01 , and HIV-198IN22 were obtained from The Centralized Facility for AIDS Reagents , National Institute for Biological Standard and Control ( NIBSC ) , United Kingdom . HIV-1KR25 was isolated in our laboratory as described before [33] . LTR reporter plasmids were constructed by inserting nucleotides −208 to +64 relative to the transcriptional initiation site of HIV-1Lai , HIV-1Bal ( B subtype ) , HIV-198IN17 , HIV-198IN22 , HIV-198CH01 , HIV-1CM9 ( C subtype ) , HIV-193TH64 , HIV-192TH53 , HIV-192TH51 , and HIV-1KR25 ( E subtype ) into the reporter vector pGL3 ( Promega BioSciences , www . promega . com ) using Xho I and Hind III restriction enzyme sites . Sequences were aligned and analyzed with CLUSTAL W ( www . ebi . ac . uk/clustalw/ ) . The HIV-1Lai NFAT5 binding site-mutant ( N5-Mut ) reporter plasmid was created by standard PCR-based mutagenesis methods [34] . THP-1 cells ( 0 . 8×106/ml ) were transfected with 0 . 3 µg/ml LTR wild-type ( WT ) or mutated reporter plasmids in combination with 0 . 03 µg/ml Renilla luciferase ( pRL-TK ) control vector using Effectene transfection reagent ( Qiagen; www . qiagen . com ) . Cells were incubated at 37°C for 16 hours after which they were stimulated with 10 µg/ml MTb CDC1551 lysate or left unstimulated for 8 hours . Reporter gene expression was quantitated by dual-luciferase reporter assay according to the manufacturer's protocol ( Promega; www . promega . com ) . Recombinant NFAT5 ( amino acids 175–471 ) with an N-terminal 6× His tag was expressed in E . coli BL21 ( DE3 ) cells ( Stratagene; www . stratagene . com ) and purified under native conditions using Ni-NTA agarose ( Qiagen ) . Recombinant p50 and p65 were purchased ( Active Motif , www . activemotif . com ) . Quantitative DNase I footprinting was performed as previously described [31] . The plasmid encoding the full-length infectious molecular clone of HIV-1Lai was obtained from the NIH AIDS Reagent and Reference Program . The HIV-1Lai/Bal-Env infectious molecular clone was constructed by replacing the envelope ( env ) gp160 amino acids 103–717 of the HIV-1Lai ( B subtype that utilizes CXCR4 ) molecular clone with the corresponding region of HIV-1Bal ( B subtype that utilizes CCR5 ) . The HIV-1Lai/Bal-Env chimeric virus uses CCR5 as a secondary receptor . The infectious molecular clone of HIV-198IN22 was constructed using DNA extracted from PBMC that were infected with a primary isolate of HIV-198IN22 . HIV-1Lai/Bal-Env and HIV-198IN22 mutant viruses were constructed by introducing point mutations using standard PCR-based mutagenesis methods . An siRNA was constructed ( Ambion Inc . , www . ambion . com ) to target a sequence unique to the NFAT5 transcript: 5′-CAACATGCCTGGAATTCAA-3′ ( nt 335 to 353 ) [31] . As described , a control for non-specific siRNA effects , we used an siRNA targeting the green fluorescent protein ( GFP ) , 5′- GGCTACGTCCAGGAGCGCACC-3′ . MDM were transfected in 6-well plates using 1 µM of the indicated siRNA in siPORT NeoFX transfection reagent ( Ambion Inc . , www . ambion . com ) , prepared as recommended by the manufacturer , in a final volume of 750 µl in Macrophage-SFM medium plus 5% heat-inactivated human AB serum . The cultures were left at 37°C overnight after which cells were washed and incubated in fresh medium . MDM were transfected two times for efficient knock down of NFAT5 expression before infection experiments were performed [31] . The lentiviral plasmid pLKO . 1 expressing shRNA targeting human MyD88 was purchased from Open Biosystems ( www . openbiosystems . com ) and was validated in our laboratory . shRNA targeting human IRAK1 ( forward primer 5′-CCGGAGCAGCTGTCCAGGTTTCGTCTCATAAAACCTGGACAGCTGCTCCTTTTTG-3′ , reverse primer 5′-AATTCAAAAAGGAGCAGCTGTCCAGGTTTTATGAGACGAAACCTGGACAGCTGCT-3′ mRNA ( IRAK1 mRNA target sequence is underlined ) and human TRAF6 ( forward primer 5′-CCGGAGAAACCTGTTGTGATTCGTCTCATAAATCACAACAGGTTTCTCCTTTTTG-3′ , reverse primer 5′-AATTCAAAAAGGAGAAACCTGTTGTGATTTATGAGACGAATCACAACAGGTTTCT-3′ ( TRAF6 mRNA target sequence is underlined ) were designed in our laboratory and were cloned into the pLKO . 1 plasmid . Lentiviruses encoding shRNA sequences were generated by transfecting the packaging cell line HEK-293T with the shRNA-encoding pLKO . 1 plasmids in combination with the packaging plasmid psPAX2 and the envelope plasmid pMD2 . G using Effectene transfection reagent ( Qiagen , www . qiagen . com ) . Supernatants were collected 48 hours post-transfection , clarified by centrifugation , and stored at −80°C . THP-1 cells were transduced with the lentiviral particles by culturing the cells with supernatants from the virus-producing cells in the presence of 8 µg/ml polybrene ( Millipore , www . millipore . com ) and spinoculation for two hours at 2000 RPM . Successfully transduced cells were selected and expanded by treatment with 0 . 8 µg/ml puromycin . The MTb clinical strain CDC1551 was prepared by adding 100 µl of frozen bacteria stock into 10 ml of Middlebrook 7H9 medium ( Difco BD , www . bd . com ) supplemented with albumin dextrose complex ( ADC ) and 0 . 05% Tween 80 ( Sigma-Aldrich , www . sigmaaldrich . com ) . The cultures were grown to an OD650 of 0 . 4 at 37°C to ensure that they were in the logarithmic growth phase . Bacteria were then plated , washed with PBS , resuspended in PBS , and passed through a 5 µm filter to ensure that the bacteria were in a single cell suspension . Bacterial cell numbers were determined by measurement of OD650 before further dilution with RPMI 1640 medium for cell infection studies at 10∶1 PBMC∶ bacilli or 1∶1 MDM∶ bacilli and THP-1∶bacilli . Colony-forming unit ( CFU ) analysis was performed and on days 4 and 7 the average CFU counts were 6×103 and 5×104 , respectively , confirming that mycobacteria levels increased over the course of infection of primary MDM . Whole cell extracts were collected with lysis buffer containing 150 mM NaCl , 50 mM Tris–HCl , pH 7 . 5 , 1% Triton , 10% glycerol , and 1 tablet of Complete EDTA-free Protease Inhibitor Cocktail ( Roche ) per 25 ml of buffer . Extracts were boiled for 5 min in 1× Laemmli sample buffer with 5% v/v 2-mercaptoethanol and proteins were separated by SDS-PAGE . The gel was transferred to a nitrocellulose Trans-Blot Transfer Membrane ( BioRad ) . The blot was then blocked for 1 h at 37°C in a solution of 4% BSA ( Sigma ) and 0 . 1% Tween-20 ( BioRad ) in a buffer containing 50 mM Tris and 150 mM NaCl at pH 7 . 6 ( BSA/TBST ) . Primary incubation was carried out with a1∶200 dilution of rabbit anti-NFAT5 antibody ( H-300 ) ( Santa Cruz Biotechnology ) and a 1∶500 dilution of goat anti-Lamin-B1 antibody ( sc-6217; Santa Cruz Biotechnology ) in BSA/TBST for 2 h at room temperature . The blot was washed 3×5 min in TBST and incubated in 1∶6000 donkey anti-goat-HRP ( Santa Cruz Biotechnology ) or goat anti-rabbit-HRP ( BioRad ) as appropriate for 1 h . The blot was again washed 3×5 min in TBST and developed with SuperSignal West Pico Chemiluminescent Reagent ( Pierce ) . The mRNA expression levels were determined by SYBR Green-based real-time PCR ( Applied Biosystems , www . appliedbiosystems . com ) . The reaction conditions were 95°C for 10 min followed by 40 cycles of 95°C for 15 sec and 60°C for 1 min . The results were normalized using β-actin mRNA as an internal control and expressed as relative values . Where applicable , results are expressed as mean ± SEM . Comparison between two groups was performed using the paired Student t-Test with the aid of Microsoft Excel software . p≤0 . 05 was considered significant .
To compare the functional impact of MTb stimulation on subtype-specific HIV-1 LTR activity , we first constructed reporter plasmids containing viral subtype B , C , and E LTRs ( −208 to + 64 nt relative to the transcription start site ) linked to the firefly luciferase reporter gene . After transfection of the monocytic THP-1 cell line with these plasmids , cells were stimulated with an irradiated whole cell lysate of MTb ( H37Rv ) . We note that MTb lysate induces inflammatory responses in monocytes that resemble those induced in response to live MTb ( see for example , [35]–[37] ) . Upon stimulation , the B , C , and E LTR-driven reporters demonstrated a significant enhancement in luciferase activity ( Figure 1A ) and the magnitude of this effect was subtype-specific . Subtype C LTRs displayed the strongest activity , while the LTRs from subtype E isolates consistently showed the weakest activity ( Figure 1A ) , consistent with previous studies demonstrating subtype-specific LTR activity that used TNF as a stimulus [38] , [39] . Although the transcription factor NFAT5 binds to a core NF-κB binding motif in the HIV-1 LTR enhancer region of subtype B , when two thymines ( TT ) are changed to cytosines ( CC ) in the proximal NF-κB binding motif ( named N5-Mut ) ( bottom of Figure 1B ) , the binding of NFAT5 can be disrupted while leaving binding of NF-κB unperturbed [31] . We examined the requirement of NFAT5 for MTb-induced LTR activity by transfecting a wild-type and NFAT5 mutant LTR reporter construct into THP-1 cells , followed by stimulation with an MTb lysate . As shown in Figure 1B , in the absence of MTb lysate stimulation the activity of the NFAT5 binding site-mutant LTR was significantly reduced ( p<0 . 05 ) in comparison to the wild-type LTR . When the NFAT5 binding site-mutant LTR was examined in THP-1 cells stimulated with the MTb lysate , its activity was reduced to an even more significant extent ( p<0 . 01 ) . To determine whether MTb lysate stimulation directly enhances NFAT5 expression in THP-1 cells , we stimulated cells for 8 or 24 hours or left them unstimulated and examined NFAT5 protein levels by western blot . As shown in Figure 1C , we found that NFAT5 protein levels steadily increased in response to MTb lysate stimulation , revealing that TLR engagement by MTb results in enhanced levels of NFAT5 , consistent with its playing a role in MTb-induced activation of the HIV-1 LTR . Given that macrophages , which are the primary target of MTb infection , are also a major reservoir of HIV-1 as infection progresses [40] , we next investigated whether MTb is able to directly enhance NFAT5 mRNA expression in primary human MDM . We prepared MDM from five normal donors , stimulated the cells with the MTb lysate or left them unstimulated , and measured NFAT5 gene expression levels by quantitative real-time PCR at 24 and 48 hours . We also investigated whether HIV-1 infection is capable of inducing NFAT5 mRNA synthesis by infecting MDM with live or heat-inactivated R5-tropic representatives of subtype B ( HIV-1Bal ) , C ( HIV-198IN22 ) , or E ( HIV-192TH64 ) . As shown in Figure 2A , stimulation with MTb lysate significantly increased NFAT5 mRNA levels at 24 ( p<0 . 05 ) and 48 ( p<0 . 01 ) hours , whereas infection with viable or heat-inactivated HIV-1 isolates did not increase NFAT5 mRNA levels ( Figure 2A ) . Thus , MTb specifically enhances NFAT5 mRNA expression in human MDM , and this response continues to increase for at least 48 hours post-stimulation ( Figure 2A ) . To extend the results we obtained in the reporter assays to a physiological TB/HIV co-infection model , we next tested the effect of siRNA-mediated ablation of NFAT5 mRNA levels in MDM co-infected with a subtype B HIV-1Lai infectious molecular clone and a clinical isolate of MTb . To perform this experiment , we first constructed an HIV-1Lai clone bearing the CCR5-tropic envelope region of HIV-1Bal ( HIV-1Lai/Bal-env ) so that it would efficiently infect primary MDM . We used this approach to ensure that our analysis of the roles of NF-κB and NFAT5 in MTb-induced HIV-1 replication could be interpreted in the proper context of previous research findings that examined LTR regulation in the context of full-length viral replication [41]–[46] . The HIV-1Lai/Bal-Env infectious clone is isogenic for the entire sequence of the parental HIV-1Lai infectious clone except for the substitution of the HIV-1Bal envelope co-receptor binding region in place of the HIV-1Lai envelope co-receptor binding region . We note that we confirmed that HIV-1Lai/Bal-Env grew in PBMC at a similar rate to wild-type HIV-1Bal , indicating proper co-receptor engagement and internalization of this infectious clone ( data not shown ) . Next , we knocked down NFAT5 mRNA levels in MDM using an siRNA that suppresses both NFAT5 mRNA and NFAT5 protein levels [31] . As shown in Figure 2 , transfection of the siRNA specific for NFAT5 reduces NFAT5 mRNA levels in both MTb-uninfected ( p = 0 . 048 ) and MTb-infected ( p = 0 . 021 ) MDM as compared to transfection of control GFP siRNA into MTb-uninfected or -infected MDM ( Figure 2B ) . We note that although siRNA normally is effective for 48–72 hours in cell lines that divide rapidly , in human MDM , which are non-dividing cells , siRNA to host factors remains detectable and functional up to at least 15 days post-transfection [47] . MDM in which NFAT5 expression had been inhibited with NFAT5 siRNA or that were transfected with control GFP siRNA were then infected with 1000 TCID50 of HIV-1Lai/Bal-env . After overnight virus infection , the cells were then co-infected with the MTb clinical strain CDC1551 . Free virus levels were then measured in culture supernatants from MDM transfected with NFAT5-specific siRNA or GFP control siRNA at days 6 , 9 and 12 post-HIV-1 infection to measure the impact of NFAT5 inhibition on MTb-induced HIV replication . As shown in Figure 2C , HIV-1 replication was suppressed at days 6 and 9 post-infection in the NFAT5 siRNA-treated cells as compared to cells treated with control siRNA , and it was significantly inhibited by day 12 post-infection ( p<0 . 05 ) ( Figure 2C ) . Thus , knock down of NFAT5 expression significantly impairs HIV-1 subtype B replication in MDM co-infected with MTb . To demonstrate that the impact of NFAT5 silencing on MTb-induced viral replication was a direct effect due to modulation of recruitment of NFAT5 to the HIV-1 LTR and not due to secondary , NFAT5-regulated effects , we set out to disrupt NFAT5 binding to the viral LTR in the context of HIV-1/MTb co-infection . This series of experiments also allowed us to dissect the relative importance of NFAT5 and NF-κB binding to the HIV-1 LTR in MTb regulation of HIV-1 replication . To perform these experiments , we constructed a panel of infectious HIV-1Lai/Bal-env molecular clones where either the NFAT5 site was specifically disrupted or the two NF-κB binding sites were either individually or dually disrupted ( Figure 3A ) . Specifically , we constructed a clone in which the NFAT5 binding site was mutated ( named HIV-1Lai/Bal-env-N5-Mut ) by changing the CC dinucleotide to TT in the core NFAT5 binding site and introduced substitution mutations into the NF-κB/NFAT5 shared binding element that we predicted would disrupt NF-κB binding but preserve NFAT5 binding . Specifically , we mutated two guanines ( GG ) in the κB I site and changed them to thymine and adenine ( TA ) ( HIV-1Lai/Bal-env-κB I-mut ) and also introduced the same GG to TA change in the second , more distal NF-κB site ( κB II ) in HIV-1Lai/Bal-env ( HIV-1Lai/Bal-env-κB II-Mut ) . We also created a double NF-κB site mutant virus ( HIV-1Lai/Bal-env-κB I+II-Mut ) in order to test the impact of complete disruption of NF-κB binding to the HIV-1 LTR on MTb regulation . To determine the specificity of these substitutions upon NFAT5 and NF-κB p50/p65 binding to the LTR , we performed a quantitative DNase I footprinting analysis using the wild-type LTR or mutant LTRs in combination with increasing concentrations of recombinant NFAT5 or NF-κB p50/p65 proteins . As shown in Figure 3B , the N5-Mut LTR could not bind NFAT5 ( compare lanes 12–15 and lanes 17–20 ) , but p50/p65 binding was not impaired ( compare lanes 2–5 with lanes 7–10 ) . By contrast , changing the GG dinucleotide to TA in either NF-κB binding motif , as predicted , inhibited p50/p65 binding at each site ( compare lanes 2–5 with lanes 7–10 and lanes 12–15 of Figure 3C ) . As expected , mutation of both NF-κB sites ( κB I+II-Mut ) resulted in abrogation of p50/p65 binding to the LTR . However , recombinant NFAT5 binding to the single and the double NF-κB mutant LTRs was not impaired and indeed appeared enhanced ( compare lanes 2–5 to lanes 7–20 of Figure 3D ) . Thus , binding of NFAT5 or NF-κB p50/p65 to their respective motifs can be specifically disrupted within an overlapping or shared binding site . We next examined the functional impact of specifically disrupting NF-κB or NFAT5 binding on regulation of HIV-1 replication by infecting bulk PBMC from four normal donors with the wild-type or mutant HIV-1Lai/Bal-env molecular clones . After overnight infection , cells were co-infected with MTb ( CDC1551 ) or mock-infected and free virus levels were detected by measuring p24 levels in culture supernatants at days 4 , 7 , and 12 post-HIV-1 infection . Replication of each mutant virus was reduced in comparison to wild-type virus in mock- and MTb-co-infected cells at all timepoints examined ( Figures 4A–4B ) . As depicted in the histograms displayed in Figure 4C , at day 12 post-HIV-1 infection in MTb-co-infected cells there was a significant reduction in levels of HIV-1Lai/Bal-env-κB I-Mut , HIV-1Lai/Bal-env-κB II-Mut , HIV-1Lai/Bal-env-κB I+II-Mut , and HIV-1Lai/Bal-env -N5-Mut in comparison to wild-type virus ( Figure 4C ) . In the absence of MTb co-infection , p24 levels were also significantly lower in the PBMC cultures infected with HIV-1Lai/Bal-env–κB II-Mut , HIV-1Lai/Bal-env-κB I+II-Mut and HIV-1Lai/Bal-env-N5-Mut , but not HIV-1Lai/Bal-env-κB I-Mut , in comparison to wild-type virus ( Figure 4C ) . None of the mutations completely abolished viral induction by MTb co-infection . However , the replication of each mutant virus was impaired to a more significant extent in the context of MTb co-infection , consistent with important roles for both NF-κB and NFAT5 in MTb-induced HIV-1 replication . To specifically examine the functional impact of NF-κB and NFAT5 binding site usage in MDM , which are efficiently infected by both pathogens , we next isolated MDM from four normal donors and infected these cells with 1000 TCID50 of wild-type HIV-1Lai/Bal-env or with the mutant viral clones HIV-1Lai/Bal-env-κB I-Mut , HIV-1Lai/Bal-env-κB II-Mut , HIV-1Lai/Bal-env-κB I+II-Mut , or HIV-1Lai/Bal-env-N5-Mut . After overnight incubation , cell cultures were co-infected with MTb ( CDC1551 ) or left infected with virus alone . Supernatants were collected at days 4 , 7 , and 12 post-HIV-1 infection and virus replication was measured . As shown in Figures 5A and 5C , in the absence of MTb co-infection , replication of mutant viral molecular clones ( HIV-1Lai/Bal-env-κB I-Mut ( p<0 . 05 ) , HIV-1Lai/Bal-env-κB II-Mut ( p<0 . 01 ) , and HIV-1Lai/Bal-env-κB I+II-Mut ( p<0 . 01 ) as well as HIV-1Lai/Bal-env-N5-Mut ( p<0 . 01 ) were significantly reduced in comparison to the wild-type virus ( Figures 5A–5C ) . When the cultures were co-infected with MTb , p24 levels increased when the cells were infected with either the wild-type or , to lower levels , with the κB-I mutant virus ( Figure 5B ) . However , p24 levels were significantly inhibited in cells infected with HIV-1Lai/Bal-env -N5-Mut ( p<0 . 01 ) ( Figure 5D ) . Moreover , p24 levels were significantly lower at day 12 in cultures infected with HIV-1Lai/Bal-env-κB II-Mut and HIV-1Lai/Bal-env-κB I+II-Mut as compared to infection of cells with wild-type virus ( Figure 5D ) . Notably , although replication of the mutant virus with the single , proximal NF-κB binding site mutation in the shared NFAT5/NF-κB element ( HIV-1Lai/Bal-env-κB I-Mut ) was diminished , this was not significant at day 12 ( Figures 5B–5D ) . We note that overall p24 levels were noticeably lower in the cultures co-infected with MTb compared with those infected with HIV-1 alone . This is consistent with previous observations showing that MTb infection of human primary macrophage cultures ex vivo suppresses HIV-1 infection due to chemokine synthesis and the enhanced expression of cellular inhibitory factors [48]–[50] . Given that NF-κB is efficiently activated in primary MDM in response to TLR agonists [51] and as we have shown in Figure 2 , NFAT5 gene expression is also induced by TLR agonists , MDM are a suitable experimental system to analyze the effect of NF-κB versus NFAT5 binding site mutations on virus replication in isolated MDM in the absence or presence of MTb co-infection . Taken together , the results from PBMC and MDM co-infection experiments demonstrate that the conserved NFAT5 binding site plays as important a transcriptional role in LTR regulation by MTb as do the NF-κB sites . As shown in Figure 1 , the subtype C LTR was the most active of the HIV-1 LTR subtypes in the reporter assays . Subtype C LTRs generally have three functional NF-κB sites in their LTRs , and subtype C is the predominant viral subtype in the African and Asian HIV-1 epidemics where MTb co-infection is extremely common [1] , [2] , [28] , [52] . We thus next extended our analysis of the role of NFAT5 in MTb/HIV-1 co-infection to a subtype C isolate . We first constructed an infectious molecular clone of the HIV-1 subtype C primary isolate HIV-198IN22 , which has two NFAT5 binding sites in its LTR and three NF-κB sites ( Figure 6A ) . We disrupted the two NFAT5 binding sites by changing the TT of each site to CC to create a mutant virus that we named HIV-198IN22-N5-Mut . Bulk PBMC from four normal donors were infected overnight with 1000 TCID50 of wild-type HIV-198IN22 or HIV-198IN22-N5-Mut and the cultures were then co-infected with MTb CDC1551 or left infected with virus alone . At day 11 post-viral infection , wild-type HIV-198IN22 replication was greater , but not significantly so , as compared to replication of HIV-198IN22 NFAT5-Mut ( Figure 6B ) . However , in the context of MTb co-infection , wild-type HIV-198IN22 replication was significantly increased ( p<0 . 05 ) at day 11 over HIV-198IN22 NFAT5-Mut replication ( Figure 6C ) , indicating that the absence of NFAT5 binding sites was particularly detrimental to virus replication in the presence of MTb co-infection . Thus , even when three functional NF-κB binding sites are present in the LTR , as in the HIV-1 subtype C infectious clone studied here , disruption of NFAT5 binding to the LTR impairs virus replication in response to MTb co-infection . To begin to decipher the cellular mechanisms underlying NFAT5-dependent , MTb-induced HIV-1 replication , we next investigated the requirement for specific signaling molecules in the TLR pathway that are required for MTb stimulation of NFAT5 gene expression . We began by examining the requirement of TLR2 ligation in enhancing NFAT5 mRNA levels . When THP-1 cells were treated with the TLR2-specific agonist Pam3Cys for 16 hours , NFAT5 mRNA levels were significantly increased in Pam3Cys-stimulated cells as compared to mock-stimulated control cells ( Figure 7A ) . As a positive control for this experiment we also infected THP-1 cells with CDC1551 ( Figure 7A ) . Notably , MTb infection enhanced NFAT5 mRNA levels to a greater extent than Pam3Cys stimulation , suggesting that additional PRRs may be triggered during actual MTb infection , augmenting its stimulatory effect on NFAT5 gene expression ( Figure 7A ) . The adaptor molecule MyD88 transmits signals upon TLR2 ligation ( reviewed in [53] ) . To confirm that this protein and , therefore , that this TLR-dependent pathway is important for MTb induction of NFAT5 gene expression , we constructed a THP-1 cell line that constitutively expresses a lentivirally-delivered short hairpin ( sh ) RNA targeting MyD88 . Real-time PCR analysis demonstrated that MyD88 mRNA levels in these cells were constitutively and significantly reduced in comparison to control THP-1 cells transduced with a lentivirus expressing control GFP shRNA ( data not shown ) . These MyD88 knockdown THP-1 cells were then infected with MTb ( CDC1551 ) and NFAT5 mRNA levels were examined . Since TNF is a MyD88-dependent gene [21] , [54] , [55] , as a positive control we measured TNF mRNA levels . For a negative control , we measured mRNA levels of the MyD88-independent gene CD86 [55] , [56] . As shown in Figure 7B , knockdown of MyD88 specifically inhibited MTb-induced NFAT5 and TNF mRNA levels and had no impact on CD86 mRNA levels . Thus , MTb regulation of NFAT5 is dependent upon MyD88 . To futher dissect the pathway mediating MTb upregulation of NFAT5 transcription , we also constructed THP-1 cells transduced with lentivirally-delivered shRNAs targeting IRAK1 or TRAF6 , two signaling molecules downstream of MyD88 . As shown in Figures 7C and 7D , knockdown of both IRAK1 and TRAF6 significantly inhibited MTb induced NFAT5 mRNA levels . As expected , MTb-induced TNF gene expression was also significantly reduced ( Figures 7C–7D ) , consistent with a role for IRAK1 and TRAF6 in TNF gene expression secondary to MTb infection as previously reported . Although CD86 gene expression did not depend on MyD88 ( Figure 7B ) , abrogation of IRAK1 and TRAF6 gene expression significantly reduced CD86 mRNA levels , indicating that CD86 expression is IRAK1- and TRAF6-dependent , but MyD88-independent in MTb-infected monocytes ( Figures 7C–7D ) . Taken together , these results clearly demonstrate that MTb upregulation of NFAT5 gene expression in human monocytic cells requires activation of MyD88 and the MyD88-associated adaptor molecules IRAK1 and TRAF6 .
The transcription factor NFAT5 is the most evolutionarily divergent member of the Rel family of NFAT proteins [57] . Unlike the other NFAT proteins ( NFATp , NFATc , NFAT3 , and NFAT4 ) , NFAT5 binds DNA as an obligate dimer in a fashion resembling NF-κB proteins [58] , is calcineurin independent , and does not cooperate with the basic region-leucine zipper proteins Fos and Jun in gene activation ( [59] , [60] , reviewed in [61]–[63] ) . To date , osmotic stress , integrin activation , and T cell-stimulation have been shown to regulate NFAT5 activity [59] , [64] , [65] . Based on our demonstration here that the host innate immune response to MTb infection induces MyD88-dependent upregulation of NFAT5 gene expression , we have expanded this list to include MTb as an additional stimulus . We have linked this observation to MTb-stimulated HIV-1 gene expression by showing that MTb enhances the replication of HIV-1 subtypes B and C via a direct interaction between NFAT5 and the viral promoter . Thus , HIV-1 has co-opted NFAT5 , which is induced as part of the innate immune response to TB , for enhanced viral transcription/replication . NF-κB is activated after MTb engagement of PRRs [20] , [66] . Multiple studies of HIV-1 activation have shown that initial recruitment of the NF-κB p50/p65 heterodimer to the HIV-1 proviral enhancer region is crucial for HIV gene transcription ( reviewed in [67] , [68] ) . Upon interaction with the viral LTR , NF-κB rapidly induces hyperacetylation of histones associated with nucleosome 1 ( nuc-1 ) at the HIV-1 transcription start site , resulting in recruitment of the pTEFb complex , which is required for RNA pol II processivity ( [69] , reviewed in [68] , [70] ) . In this report we directly examined the relative roles of NFAT5 and NF-κB p50/p65 in HIV-1 replication under conditions of activation by MTb co-infection , when NF-κB levels in the nucleus are elevated . We found that specific disruption of the NFAT5 binding site ( s ) in R5-tropic subtype B or subtype C infectious molecular clones significantly reduced virus replication in PBMC or MDM co-infected with a clinical isolate of MTb . Thus , NF-κB binding to the viral LTR is not sufficient to compensate for the loss of NFAT5 binding to the LTR under conditions of MTb co-infection . Reciprocally , an intact NFAT5 site could not compensate for disruption of the two NF-κB sites . Thus , both factors are required for MTb-induced HIV-1 replication . Intriguingly , both in the absence and presence of MTb co-infection , disruption of NF-κB site II consistently resulted in greater suppression of virus replication than disruption of NF-κB site I . Consistent with these findings , synthetic reporter assays have previously shown that these two sites have distinct roles in driving transcription . The downstream NF-κB site I binding site , which is directly upstream of three highly conserved Sp binding sites , appears to enhance Sp protein recruitment to the LTR [71]–[73] . Because NF-κB site I overlaps the NFAT5 binding site , it is possible that loss of NF-κB binding to this site is mitigated , at least in part , by increased NFAT5 binding to the mutated site . This hypothesis is consistent with our quantitative DNase I footprinting analysis that shows that specific disruption of NF-κB site I results in enhanced binding of NFAT5 to the LTR . HIV-1 subtype C now makes up greater than 50% of all HIV-1 infections worldwide , and its prevalence is especially high in regions of endemic TB [1] , [2] , [28] , [52] , Most HIV-1 subtype C isolates possess three NF-κB binding sites in the LTR [30] . Our analysis of MTb whole lysate-stimulated , LTR-driven reporter gene expression from representative B , C , and E LTRs showed that LTRs derived from subtype C isolates were superior in driving reporter gene expression as compared to LTRs derived from subtype B or E isolates . Other studies have found that , in response to TNF , subtype C LTRs drive reporter gene transcription more strongly than other subtype LTRs [38] , [39] . In the co-infection model utilized in this report , in which clinical isolates of both MTb and HIV-1 subtype C were used to infect freshly prepared human peripheral blood cells , we found that specific disruption of NFAT5 binding to the LTR significantly impaired viral replication during MTb co-infection , indicating that even the presence of three intact NF-κB binding sites , which is typical for HIV-1 subtype C isolates [30] , cannot compensate for loss of NFAT5 recruitment to the viral LTR in the context of MTb co-infection . Given that the NF-κB and NFAT5 binding motifs overlap , how do NF-κB and NFAT5 function at this overlapping or shared site in the LTR to drive viral transcription in response to MTb infection ? Because NFAT5 expression continues to rise for at least 48 hours post-MTb infection ( Figure 2A ) , one possibility is that the host factors NF-κB and NFAT5 associate with the LTR at different stages after viral activation . While NF-κB presence in the nucleus declines in the hours post MTb infection [74] , [75] , NFAT5 levels escalate . Thus , NFAT5 , which is pivotal for HIV-1 transcription in unstimulated cells [31] , may , after Mtb infection , perpetuate viral transcriptional initiation begun by NF-κB and , with Tat levels also elevated , promote high levels of sustained HIV-1 transcription . Indeed , in response to hypertonic stress , NFAT5 binds to the aldose reductase ( AR ) promoter and induces rapid hyperacetylation of histones H3 and H4 [76] . Furthermore , hypertonic stress of mouse renal collecting duct epithelial cells results in NFAT5 and NF-κB complex formation at NF-κB-dependent gene promoters [77] . Thus , there are precedents for imagining that NFAT5 could function at the HIV-1 LTR by inducing histone remodeling in a manner similar to NF-κB ( [69] , reviewed in [68] , [70] ) or by directly interacting with NF-κB in a cooperative manner . In conclusion , we have demonstrated that NFAT5 is required for the replication of R5-tropic subtype B and subtype C HIV-1 isolates in response to MTb-co-infection of human PBMC and MDM . Functional NF-κB interaction with the viral LTR is also required , but fully intact NF-κB binding elements are unable to compensate for the loss of NFAT5 recruitment to the viral promoter . In addition , we have demonstrated that MTb infection or stimulation with the TLR2 ligand PAM3Cys , induces NFAT5 gene expression in human monocytes . Furthermore , we have shown that MTb stimulation of NFAT5 depends on TLR pathway signaling molecules , including MyD88 , IRAK1 , and TRAF6 . Taken together , the findings presented here enhance the general understanding of the innate immune response to MTb infection by showing that NFAT5 is a major mediator of TLR-dependent gene expression; its importance for gene regulation is likely applicable to other MTb- and TLR-regulated genes . Moreover , these data provide molecular insights into MTb regulation of HIV-1 transcription , thereby elucidating several new targets for therapeutic interventions aimed at controlling TB/HIV-1 co-infection .
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The major cause of AIDS deaths globally has been tuberculosis ( TB ) , which is caused by the bacterium Mycobacterium tuberculosis ( MTb ) . Co-infection with MTb exacerbates human immunodeficiency virus type1 ( HIV-1 ) replication and disease progression via both innate and adaptive host immune responses to MTb infection . In this report , we present evidence that the transcription factor NFAT5 plays a crucial role in MTb-induced HIV-1 replication in human peripheral blood cells and monocytes . We also show that MTb infection itself stimulates NFAT5 gene expression in human monocytes and that its expression involves the TLR signalling pathway and requires the downstream adaptor proteins MyD88 , IRAK1 , and TRAF6 . This identification of a novel role for NFAT5 in TB/HIV-1 co-infection reveals that NFAT5 is a major mediator of TLR-dependent gene expression and thus provides a potential new therapeutic target for treatment of HIV-1 and possibly other diseases .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"bacterial",
"diseases",
"infectious",
"diseases",
"biology",
"microbiology",
"viral",
"diseases"
] |
2012
|
Regulation of Mycobacterium tuberculosis-Dependent HIV-1 Transcription Reveals a New Role for NFAT5 in the Toll-Like Receptor Pathway
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Despite decades of work , our understanding of the distribution of fitness effects of segregating genetic variants in natural populations remains largely incomplete . One form of selection that can maintain genetic variation is spatially varying selection , such as that leading to latitudinal clines . While the introduction of population genomic approaches to understanding spatially varying selection has generated much excitement , little successful effort has been devoted to moving beyond genome scans for selection to experimental analysis of the relevant biology and the development of experimentally motivated hypotheses regarding the agents of selection; it remains an interesting question as to whether the vast majority of population genomic work will lead to satisfying biological insights . Here , motivated by population genomic results , we investigate how spatially varying selection in the genetic model system , Drosophila melanogaster , has led to genetic differences between populations in several components of the DNA damage response . UVB incidence , which is negatively correlated with latitude , is an important agent of DNA damage . We show that sensitivity of early embryos to UVB exposure is strongly correlated with latitude such that low latitude populations show much lower sensitivity to UVB . We then show that lines with lower embryo UVB sensitivity also exhibit increased capacity for repair of damaged sperm DNA by the oocyte . A comparison of the early embryo transcriptome in high and low latitude embryos provides evidence that one mechanism of adaptive DNA repair differences between populations is the greater abundance of DNA repair transcripts in the eggs of low latitude females . Finally , we use population genomic comparisons of high and low latitude samples to reveal evidence that multiple components of the DNA damage response and both coding and non-coding variation likely contribute to adaptive differences in DNA repair between populations .
One of the promises of population genomic analyses is that , when combined with genome annotation , it can provide a rich source of hypotheses regarding the manifold ways in which selection may modify biological function . Because these hypotheses are relatively agnostic with regard to our preconceived notions of the traits influenced by selection and their underlying genetics , such approaches may deepen and broaden our understanding of phenotypic evolution . However , the value of these approaches is greatly enriched when population genomic-driven hypotheses regarding fitness variation can be experimentally investigated . Population genomic analyses in Drosophila melanogaster have revealed that many basic cell biological processes appear to be influenced by spatially varying selection along latitudinal gradients [1–7] . How and why selection modifies these functions generally remains mysterious . For example , Turner et al . [5] reported that several genes associated with DNA repair harbored SNPs ( single nucleotide polymorphisms ) exhibiting high levels of differentiation between high and low latitude populations . Here , we extend that observation in several new directions by integrating multiple data types to produce a portrait of the diverse molecular mechanisms associated with local adaptation in DNA repair , as well as identifying a likely ecological agent of selection . One of the main source of DNA damage in nature is the lower wavelength of solar light ( Ultraviolet: UV ) [8] . The sunlight UV spectrum is , by convention , divided into short ( 100 to 280 nm; UVC ) , middle ( 280 to 320 nm; UVB ) , and long wavelengths ( 320 to 400 nm; UVA ) . The UVC fraction of sunlight is completely absorbed by the higher layers of the atmosphere ( stratosphere ) , while the UVB fraction is only partially absorbed by the lower layers of the atmosphere . Most of the solar UVA wavelengths are able to reach the earth surface . As a consequence , the latitudinal variation in solar elevation angles translates into a latitudinal cline ( S1 Fig ) which is steeper for UVB than for UVA [9 , 10] . However , due to the absorbance properties of DNA , UVB wavelengths are likely to be the main source of UV-induced DNA damage in nature [11] . UVB induces two main types of DNA lesions: CPDs ( cyclobutane pyrimidine dimers ) and 6-4PPs ( pyrimidine- ( 6–4 ) -pyrimidone photoproducts; i . e . ( 6–4 ) photoproducts; [10 , 12–14] ) . These bulky lesions trigger multiple cellular responses aimed at detection , repair and maintenance of genome integrity . For example , a mechanism known as photoreactivation [10 , 15 , 16] relies on photolyases/glycosylases that catalyze the direct photoreversal of CPD lesions without the synthesis of new DNA . Alternatively , nucleotide excision repair generally includes the processing of several base pairs upstream and downstream of the lesion [10 , 17] . Failure to repair these lesions in a timely manner represents a critical threat to the cell because replicative DNA polymerases are impeded by their presence . In such cases , multiple biochemical processes are deployed to promote cell survival . These responses include the recruitment of protein complexes to stabilize stalled replications forks and the recruitment of specialized error-prone DNA polymerases able to bypass these lesions in a process known as translesion synthesis [18 , 19] . Moreover , DNA damage often triggers an arrest or a slowing down of the cell cycle to provide time for repair before the next cell division [20 , 21] . While the role of local adaptation for DNA repair as a response to geographic variation in UVB has received some attention [22–25] , relatively little work [26] has been devoted to the possible influence of UVB on DNA repair variation in the genetic model system , D . melanogaster . Motivated by population genomic evidence for spatially varying selection on DNA repair proteins in D . melanogaster [5] , we focused on early embryo DNA damage response as a possible target of selection for three reasons . First , as a substantial proportion of D . melanogaster eggs are laid during daytime [27] , early embryos are potentially exposed to sunlight [28] . Second , the chorion transmits a significant amount of UV energy [29] . Finally , extremely rapid DNA replication during the early mitotic divisions of Drosophila embryogenesis leads to endogenous replication stress [21] . Thus , additional exogenous DNA damage resulting from exposure to UV during early embryogenesis could have strong fitness effects [30] . Here , we present results from phenotypic analysis , population genomics and transcriptomics supporting the hypothesis that genetic variation in the DNA damage response in D . melanogaster is maintained by spatially varying selection mediated by latitudinal variation in UVB-related DNA damage during early embryogenesis .
We quantified geographic variation in early embryo UV sensitivity in six populations of D . melanogaster spanning 37 degrees of latitude ( Fig 1A , see S2 Fig for sampling locations ) for a total of 111 isofemale lines . For each line , we estimated UVB sensitivity by monitoring egg hatch rate and survival to adulthood of 1-to-3-hours old embryos unexposed to UVB ( control ) or exposed to a standardized dose of UVB . The population-mean embryo UVB sensitivity data strongly support the presence of a latitudinal cline ( Fig 1A; linear regression: R2 = 0 . 94 , p = 0 . 001; see S3 Fig for the scatterplot of UVB sensitivity vs . latitude for all 111 isofemale lines ) . The absence of geographic variation for larval-to-adult survival among the hatched individuals ( linear regression: R2 = 0 . 11; p = 0 . 51 ) provides no support for carry-over viability effects of embryonic UV exposure on later developmental stage . The observed population differentiation in embryo UVB sensitivity corresponds to a 3 . 1% difference in egg hatch rate for every 10 degrees of latitude , which is comparable to previously observed clines in D . melanogaster for phenotypes such as body size and thermotolerance [31–34] . One hypothesis to explain the maintenance of fitness variation under spatially varying selection is genotype-by-environment interactions associated with trade-offs [35–38] . Regression of control hatch rates vs . latitude revealed a significant cline such that low latitude embryos have significantly lower hatch rates than high latitude embryos ( Fig 1B; linear regression: control R2 = 0 . 78 , p = 0 . 019; UV-exposed R2 = 0 . 73 , p = 0 . 029 ) . Thus , control and UV-exposed treatments both show clines , for hatch rate , though with opposite sign slopes . While this is consistent with the idea that traits associated with decreased embryo UVB sensitivity are associated with reduced embryo viability in the absence of UVB exposure , alternative explanations are possible . For example , females heterozygous for chromosome inversions on all four major chromosome arms may have reduced hatch rates due to increased rates of non-disjunction [39] and low latitude populations may be segregating many intermediate frequency inversions [39–41] . Another possibility is that lower latitude females produce lower quality eggs under laboratory conditions . To test this hypothesis we used a subset of density-controlled vials , each having 25–35 eggs from the control experiments , to estimate larval-to-adult survival for each line . Population means were obtained by averaging line means . We found a significant cline for larval-to-adult viability ( R2 = 0 . 78; p = 0 . 02 ) . While this does not rule out the possibility that reduced embryo hatch rates in lower latitude females is genetically correlated with adaptations for greater embryo DNA repair , these observations are also consistent with the hypothesis that lower latitude females produce lower quality eggs , at least under typical laboratory conditions . Importantly , regardless of the explanation for the cline for control hatch rates , these data have no bearing on the conclusion that embryo UV sensitivity varies clinally . Early embryo phenotypes prior to the maternal-to-zygotic transition are likely associated with genetically determined maternal effects [21] . Therefore , to investigate whether the phenotypic variation for early embryonic UVB tolerance is influenced by variation in oocyte DNA repair capacity , we took advantage of the fact that DNA repair proteins derived from maternally provided oocyte transcripts can repair damaged sperm DNA subsequent to fertilization [42 , 43] . Thus , we set out to determine whether lines associated with lower early embryo UVB sensitivity would be associated with higher rates of repair of chemically damaged sperm DNA . To do so we monitored the recovery of MMS- damaged X chromosomes ( FM7a ) in a two-generation crossing scheme ( Fig 2A ) . While the primary types of DNA damage for UVB and MMS ( methyl methansulfonate ) are different ( CPDs and apyrimidic sites , respectively ) , both types of damage may be associated with stalled replication forks and translesion synthesis [44] or double strand breaks [10 , 45] . More generally , alternative DNA repair pathways are likely to exhibit cross-talk and are likely to repair more than one type of DNA damage [46] . For example , nucleotide excision repair genes also contribute to MMS tolerance in Drosophila [47] . The mean recovery rate of the mutagenized FM7a chromosome in F2 descendants was 7% greater ( Mann-Whitney U test: p = 0 . 0017 ) for parental females from embryo UVB-resistant lines vs . those from sensitive lines ( Fig 2B ) . The most parsimonious explanation for these results is greater efficiency of DNA repair in oocytes from females derived from lines with lower embryo UVB sensitivity , though this experiment does not rule out the possibility that other mechanisms ( such as chorion protection ) may contribute to variation in embryo UVB tolerance . For the 10 Panama strains assayed for both DNA-repair capacity and embryo UVB tolerance , there is a nearly significant positive correlation between the two phenotypes ( Spearman correlation: r = 0 . 612; p = 0 . 059 ) despite the fact that the primary types of damage induced by the two treatments are different . Thus , spatially varying selection favoring higher repair rates of UVB-mediated lesions may lead to incidentally greater repair rates for other types of lesions , such as those resulting from laboratory MMS exposure . We speculated that at least part of the latitudinal differences in embryo UVB tolerance might be mediated by geographic variation in maternal loading of DNA damage response mRNAs to the egg/early embryo . To investigate whether high and low latitude populations exhibit differences in early embryo transcript abundance for 211 candidate DNA damage response genes ( see S1 Table for complete list ) , we used RNA-seq to compare the transcriptomes of 1-to-3 hour-old embryos from the Panama and Rhode Island populations . Of the 8602 genes expressed in our samples ( 200 of which were candidate genes ) , 856 ( 9 . 9% ) were differentially expressed ( at a false discovery rate ( FDR ) = 0 . 10; see S2 Table for the complete list ) . Of these 856 differentially expressed genes , 21 were DNA damage response candidates ( Table 1 ) . If DNA repair transcript abundance and repair capacity are positively correlated then differentially expressed candidate genes should tend to be more highly expressed in the Panama population , which exhibits greater embryo UVB tolerance . This prediction was strongly upheld , as 20 of the 21 differentially expressed candidates showed higher expression in Panama embryos , which represents a three-fold enrichment over transcriptome wide expectation ( hypergeometric test: p = 2 . 7 × 10−9 , assuming differential transcript abundance for each of the 21 genes is mechanistically independent ) . While DNA damage response genes expressed in multiple tissue or developmental stages may influence several fitness components , we found that candidate genes showing differential expression between Panama and Rhode Island were 2 . 5 times more likely than non-differentially expressed ones to show an expression bias toward the 0-2h embryo stage in modENCODE data ( likelihood ratio test: p< 0 . 001 ) . This supports the idea that DNA damage response genes specifically affecting early embryo fitness components are the most likely targets of selection . To investigate the possible population genetic basis of geographic differences in embryo UVB tolerance and early embryo transcript abundance for DNA damage response genes , we estimated SNP differentiation between Panama and Maine from pooled population genomic data ( See Methods ) . We constructed a gene-based SNP data set by identifying SNPs located between 500bp upstream of the 5’UTR and 500bp downstream of the 3’UTR for all genes in the genome . After filtering , the dataset contained 853 , 543 SNPs mapping to 15 , 599 genic regions . We then identified all genic SNPs that were differentiated between Maine and Panama populations ( See M&M ) . At an FDR of 0 . 001 , 95% of differentiated SNPs exhibited FST higher than 0 . 081 and the median FST for this set of SNPs was 0 . 14 , corresponding to the top 5% of the whole data set FST distribution . We compared our differentiated SNPs to previously published data on D . melanogaster SNPs correlated with latitude in five populations sampled from Maine to Florida [1] . Before doing so , we re-calculated from the Bergland et al . [1] data , per chromosome arm q-values to account for the heterogeneity in genomic patterns of geographic differentiation across chromosome arms [3 , 4] . For the 582 , 149 SNPs observed in both data sets , we compared our differentiated SNPs ( FDR 0 . 001 ) and their clinal SNPs ( FDR 0 . 05 ) and found that 33 . 5% of our differentiated SNPs were identified as clinal in Bergland et al . [1] ( hypergeometric test: p< 10−10 , assuming SNP independence ) . Thus , as expected , alleles differentiated between Panama and Maine also exhibit correlations with latitude in independent samples . To investigate the possible role of protein variation in embryo UVB tolerance , we searched for significantly differentiated ( FDR 0 . 001 ) non-synonymous SNP ( nsSNPs ) in DNA damage response candidate genes . We found 122 such SNPs distributed across 61 candidate genes ( for the complete list see S3 Table ) and exhibiting FST values between 0 . 42 and 0 . 07 . The 15 genes containing the 20 most differentiated nsSNP are listed in Table 2 . A majority of these genes ( 8 of 15 ) reside on chromosome arm 3R , and four are located in the regions spanned by In ( 3R ) Payne ( a polymorphic chromosome inversion that segregates in many D . melanogaster populations [41] ) , supporting previous results [3 , 4] that genomic latitudinal differentiation is strongly associated with this inversion ( S4 Fig ) . However , among all candidate genes carrying at least one differentiated nsSNP there is no enrichment for any chromosome arm/inversion location ( S5 Fig ) . To identify possible candidate-gene cis-regulatory variants influenced by spatially varying selection , we looked for differentiated SNPs ( FDR 0 . 001 ) located in UTRs or 500bp upstream/downstream of UTRs . We found 260 such SNPs distributed across 108 candidate genes . Of the 21 genes differentially expressed between PC and RI , 10 contained at least one such differentiated SNP ( Table 1 ) , consistent with the idea that adaptively differentiated cis-acting variants contribute to the observed geographic differences in transcript abundance . Integrating this information , we can hypothesize how selection associated with greater UVB damage at lower latitudes could affect multiple DNA damage response pathways ( Fig 3; broader integration S6 Fig ) . Given that syncitial nuclei located at the periphery of the embryo should receive more UV energy , we speculate that most of the selection in nature occurs between mitotic cycles 8 to 14 corresponding to roughly 1 . 5 to 2 . 5 hours after oviposition [21] . Greater amounts of CPD and 6-4PPs would favor increased capacity for damage recognition and nucleotide excision repair , the repair pathway with the highest affinity for UVB photoproducts [48 , 49] . If nucleotide excision repair is overwhelmed , unrepaired CPDs and 6-4PPs would mobilize biochemical resources for stabilization of the resulting stalled replication forks , followed by translesion synthesis . For example , the translesion polymerase DNApol-η , which is especially efficient at CPD bypass [18 , 19] and which is known to influence UV tolerance in flies [50] , shows both nsSNP and expression differentiation , supporting the idea that geographic variation in repair pathways downstream of NER may be influenced by environmental variation in UV exposure . Also noteworthy is the appearance of multiple geographically differentiated Fanconi anemia group protein coding genes . These proteins play an important role in the stabilization of stalled replication forks [51] , which are a byproduct of UV-induced lesions [45 , 52] . Within these pathways are some examples of physically interacting proteins that appear to be influenced by spatially varying selection ( Fig 3 ) . For example , when NER is overwhelmed , mus201 ( ERCC5 ) is replaced by the endonuclease tos ( EXO1 ) [53 , 54] , which results in single-stranded DNA breaks , leading to the activation of a central player in multiple DNA damage response signaling cascades , the mei-41/mus304 ( ATR/ATRIP ) dimer [55] , which itself interacts with mus101 ( TOPB1 ) [56] . All of these proteins show strong evidence of geographic differentiation . Four candidate genes ( RecQ4 , DNApol-ε255 , DNApol-η , and mei-41 ) are noteworthy in that they show significant geographic differentiation for i ) early embryo expression differences , ii ) nsSNPs , and iii ) UTR or flanking SNPs . The mei-41 gene ( orthologous to the human ATR gene ) is particularly unusual: it contains 3 significantly differentiated non-synonymous changes and is geographically differentially expressed . Additionally , two significantly differentiated non-coding SNPs occur in potentially regulatory sequences ( BIOTIFFIN motifs 24 and 92 ) about 50bp upstream from the transcription start site . The MEI-41 kinase plays a central role in the DNA damage response . Once activated in response to DNA damage and replication stress , it activates by phosphorylation a large number of downstream effectors of the DNA damage response from multiple pathways [57] . It also promotes chromatin conformation changes that facilitate repair [58 , 59] . And finally , it signals the presence of DNA damage to the cell cycle checkpoint pathway [60 , 61] . MEI-41 interacts with numerous geographically differentiated proteins , including TEFU , MUS304 , DNAPOL-η , CLASPIN , and MUS101 [57] . While the strong latitudinal cline in UVB incidence is well known [9] , the possible interaction of this variation with selection and genomic variation is not well understood . Here , we have brought together several lines of evidence in the D . melanogaster model system supporting the idea that spatially varying selection associated with UVB-mediated DNA damage during early embryogenesis maintains genetic variation in the DNA damage response . The maintenance of genetic variation under spatially varying selection generally depends on trade-offs such that genotypes favored in some environments are disfavored in others [35 , 36] . Our data show that populations exhibiting lower UVB embryo sensitivity show lower embryo viability when not exposed to UVB . This is consistent with , though does not prove , that there is a viability cost associated with greater DNA repair capacity . While the existence of a trade-off between embryo DNA repair and embryo viability remains to be demonstrated , it is worth speculating on possible mechanisms . One possibility is that there is an energetic cost associated with the apparent increased maternal provisioning of DNA repair associated transcripts to the oocyte . However , given that differentially expressed DNA repair transcripts constitute only a small fraction of the early embryo transcriptome , this possibility seems unlikely . An alternative is that greater DNA repair activity is associated not only with more efficacious repair of DNA lesions , but also with unregulated interactions with the DNA leading to “repair” of undamaged nucleotides . This phenomenon , which is known as gratuitous repair [46 , 62] suggests a possible trade-off . An incidental effect of greater repair capacity in lower latitude genotypes could be that when UVB damage is minimal ( at higher latitudes ) , excess repair capacity might be directed inappropriately to undamaged DNA [46] . Another possible trade-off could be that increased activity of error-prone translesion polymerases leads to the accumulation of somatic mutations during development , resulting in decreased viability . Genotype-by-temperature interactions could also play a role in the maintenance of variation in DNA damage response genes . The evidence that UVB-mediated spatially varying selection on embryo DNA damage is important in this species motivates the investigation of other phenotypes , such as female oviposition behavior [63] , egg shell phenotypes , or genome size [64] that might also be influenced by such selection . Finally , several of the candidate genes mentioned here play a role in germline DNA repair processes . Thus , pleiotropic effects of spatially varying selection on somatic life history components could , in principle , influence variation in meiotic mutation or recombination in natural Drosophila populations .
We studied a total of six Drosophila melanogaster populations . Four of them originate from locations along the east coast of North America: ME in Fairfield , Maine ( latitude: 44°37’N ) , RI in Providence , Rhode Island ( 41°49’N ) , VA in Richmond , Virginia ( 37°32’N ) , and FL in Jacksonville , Florida ( 30°20’N ) ( all sampled in September 2011 ) . An additional population ( PC ) was sampled in Panama City , in Panama ( 8°58’N ) in January 2012 . A set of lines sampled from several locations in Mexico ( mean latitude = 19°45’N ) and obtained from Bloomington fly stock center ( lines number: 14021–0231 . 20 , 21 , 22 , 25–28 , 30–33 , 40 , 41 , 44 ) and constitute the Mexico population ( MX ) . The sampling locations are shown on S2 Fig . The FM7a balancer line with the dominant B1 ( Bar eyes ) marker was obtained from Bloomington stock center ( #785 ) . All stocks were maintained at room temperature ( 23°C ) on a standard yeast-cornmeal-agar food medium . For each isofemale line , we generated experimental animals by allowing groups of 10 to 15 parental flies to mate and lay eggs in a vial for 3–4 days . Those vials , which contain 4ml standard food , were placed into an incubator at 25°C with 12:12 light:dark cycle and 50% humidity . The emerging offspring were anaesthetized with light CO2 and placed in a new empty vial containing a small plastic spoon with dyed standard fly food . The spoon was changed every 24 hours for two days for egg-laying habituation . In the morning of first collection day , a new spoon with a drop of fresh yeast-water paste was placed into the vial . 1 to 2 hours later , this first spoon was removed and replaced by another one to discard long-time retained eggs . Two hours later , groups of 35 eggs were collected with a clean needle and delicately placed on a new spoon with fresh food . All eggs were placed lying on their side on the food surface , not touching any other egg . Potentially damaged eggs or accidentally dechorionated eggs were discarded . The egg collection lasted precisely 1 hour after which the spoons with eggs were separated into two treatments groups: a control group which was left 60s on the bench and an experimental group which was immediately exposed to UV for 60s in a custom made irradiator . The irradiator consisted of a box coated with aluminum foil and built with a UVB G8T5E lamp Ushio ( providing a bell shaped UV light spectrum comprised between 280nm to 350nm ( peaking at 306nm ) and given for 1 . 4W UV output at 306nm ) . The UVB incidence in the irradiator was 201μW/cm2 according to a Solartech 6 . 2 UVB-meter ( spectral response 280-322nm peaking at 300nm ) . The irradiator was built with 2 fans on the top to extract the heat generated by the lamp . The temperature inside the box was monitored and did not deviated from the ambient room temperature ( 23°C ) . As the UV exposures were limited to 60s , and as the eggs were not in the fan airflow , desiccation was negligible . Immediately after exposure , all spoons were transferred to a vial with food ( so that humidity remained high and to provide enough food for the larvae to develop ) . Vials were placed back into an incubator at 25°C with 12:12 light:dark cycle and 50% humidity . 48 hours later the spoons were , one by one , gently taken out of the vials and egg hatch was scored . Each spoon was then cautiously returned into its vial and after 16 days all vials were scored for number of adults . The egg hatch rate and number of surviving adult were calculated for each spoon ( 35 eggs ) and averaged per isofemale line . Population averages were obtained by averaging hatch rates from lines sharing the same geographical origin . Data were then analyzed in JMP v12 . 0 . 1 ( SAS institute , Cary , NC , USA ) Virgin 4-to-5-days old males from FM7a balancer line were starved for 6 hours in an empty vial . Males were then transferred to a regular food vial containing a cotton ball wrapped into a kimwipe soaked with red dyed 2mM MMS in a 1% sucrose solution [42 , 43] . Males were transferred 24 hours later into a new regular food vial for 2 hours to recover . All males were checked for a red shiny abdomen indicating ingestion of the sucrose-MMS solution . Five mutagenized males were then placed with 10 , 4-to-5-days-old parental females ( F0 ) from different wild caught isofemale lines . Parental flies were discarded 48 hours later . F1 offspring were collected for the first 4 days of emergence of a vial . Single virgin F1 females were paired with single F1 males in a new standard food vial . These F2 vials were frozen 16 days later and F2 individuals were sexed and phenotyped for Bar eyes ( i . e . presence or absence of mutated FM7a ) . For each line , we used a total of 20 to 30 parental F0 females . We scored sex and Bar in the offspring ( 63 , 600 F2 individuals total ) from a total of 1060 F1 females . Two flies had Bar eyes of wild-type color suggesting exchange between the balancer and wild type X chromosome , were discarded from analysis . Across all lines , 99 F2 females that produced fewer than 25 offspring individuals were also discarded from the analysis . In the final data set the minimum number of F1 females per line was 16 , and the maximum was 58 for a total of 959 females . The frequencies of the FM7a chromosome were then calculated per sex and per line . Data were then analyzed in JMP v12 . 0 . We sampled embryos from each of the two populations that showed the strongest embryo UV tolerance differences ( RI and PC ) . The embryos were sampled from a random set of 14 isofemale lines from each population . We collected 1-to-3-hours-old embryos using the same procedures as described for the embryo UV tolerance experiments . We pooled embryos from each of the 14 lines from either the RI population or from the PC population . One biological replicate thus consisted of a pool of 56 embryos ( 4 embryos from each of 14 isofemale lines ) . Embryos were collected to prepare 3 biological replicates and were immediately transferred to Trizol for RNA extraction . Poly ( A ) + RNA was prepared using an NEB mRNA isolation module ( E7490S ) . RNA-seq libraries were constructed using NEB kits E7530S ( library prep ) , and E7335S ( Oligos ) . Libraries were constructed following the manufacturer instructions , except we used Aline Bioscence PCR CleanDX beads for the DNA purification steps . Individual libraries were constructed with insert size between 160–190 bp and sequenced by BGI Americas ( Cambridge , MA , USA ) on an Illumina Hiseq2000 platform using paired-ends chemistry and 100 cycles . In total , we generated 80 . 2 million cleaned paired-end reads for 6 libraries ( i . e . an average of 26 . 7 million reads per library ) . Clean reads were deposited to NCBI under the SRA accession ( SRP067364 ) . Data analysis was similar to Zhao et al . [65]: filtered clean reads ( Q > 20 for amino acid and Q > 30 for read ) in each sample or replicate were aligned independently to the D . melanogaster reference genome ( FlyBase 6 . 04 ) using Bowtie-based TopHat [66] program . Our experiment showed high degree of replication , with R2 > 0 . 99 for all 6 pairs of biological replicates . We used HTseq [67] to estimate read count of each gene , and then estimated differential expression using the Bioconductor package ( v . 2 . 14 ) in R , including DESeq2 ( v . 1 . 4 . 5 edgeR ( version 3 . 0 . 8 ) and voom-limma ( version 3 . 20 . 8 ) . The Benjamini–Hochberg procedure was used to control the false discovery rate [68] . Here , we present results from DESeq2 differentially expressed genes because these results showed the greatest consistency with the other two methods . We also verified that overall expression levels were consistent across libraries and across gene classes ( see S4 Table ) . We calculated the development stage expression specificity of candidate genes as follows . Fastq reads from 30 development stages were obtained from modENCODE [69] . High quality reads were mapped to D . melanogaster reference genome r6 . 04 , and uniquely mapped reads used to calculate the FPKM of each gene by Cufflinks . The development stage expression specificity ( tau ) of each gene was calculated using the same method previously used for tissue specificity [70] . To construct a list of candidate genes potentially involved in early embryonic UV tolerance we pooled all the genes contained the following Gene Ontologies categories and subcategories: DNA repair complex ( GO:1990391 ) , DNA integrity checkpoint ( GO:0031570 ) , Response to UV ( GO:0009411 ) , Mitotic cell cycle checkpoint ( GO:0007093 ) , Cellular response to DNA damage stimulus ( GO:0006974 ) , Single stranded DNA binding ( GO:0003697 ) , Damaged DNA binding ( GO:0003684 ) . The gene pool was then manually curated based on the strength of support from the literature for a direct function in DNA damage response . In particular , we excluded a substantial number of genes under the cellular response to DNA damage stimulus ( GO:0006974 ) ontology because of weak support from literature for a role in UV DNA damage response . For the same reasons we excluded the genes from the GO: mitotic spindle assembly checkpoint as well as Tango6 , qjt and Ald . We added Dref , Rbf2 , E2f1 , E2f2 to the list as they are transcription factors with evidence of binding in the regulatory regions of some candidates [71] . We ended up with a list of 211 genes ( S1 Table ) . We generated pooled paired-end Illumina libraries ( NEBNext DNA Library Prep Kit # E6040S ) from flies collected from Panama City , Panama . The sequencing reads are available under the SRA accession ( SRP067441 ) . We used the Maine sequencing data from Reinhardt et al . [2] ( Bioproject #PRJNA237820 ) . 50 females were used in the Panama pool ( daughters of wild-caught females ) and 36 females ( one per isofemale line ) were used to generate the Maine pool . Mean sequencing coverage per pool was 77 . 7× for Panama and 45 . 1× for Maine . Reads were aligned to version 5 of the D . melanogaster reference sequence using Bowtie2 with the–-very-sensitive setting [72] . Variants were called using bcftools ( samtools . github . io/bcftools ) requiring a read quality score of 30 for inclusion . We required a minimum of 20× coverage at a site in both the Maine and Panama populations and at least two observations of an alternate base call between the two populations to consider it in the analysis . We also excluded all triallelic sites . Subsequent to this alignment version 6 of the D . melanogaster reference was released and we used the conversion tool on FlyBase ( www . flybase . org ) to update the positions in our data set to version 6 positions . We considered all positions within the range of our candidate gene list plus or minus 500bp . Within these spans we categorized synonymous and non-synonymous SNPs as well as sites that occur within introns , the 3’UTR , the 5’UTR and flanking sequence . All data used to determine this information was taken from pre-computed files on FlyBase ( www . flybase . org ) . We used two different approaches to examine differences in allele frequencies at each site . First , we generated a two by two contingency table for each site in our analysis and performed the odds ratio test for independence using the ormidp . test function in the epitools package in R ( medipei . com/epitools/ ) . This test is an exact conditional test that approximates an unconditional test , which is preferable in situations with small sample sizes . We then used the p-values generated by our midp tests to calculate the false discovery rate inherent for each chromosome arm using the bioconductor package q-value ( http://github . com/jdstorey/qvalue ) . Second , we calculated FST for each position in our data set correcting for both number of chromosomes contributing to each population pool and local coverage at that site for each pool following the method in [3] . These two measures ( q-value of the midp tests and FST ) gave us similar results with respect to identifying significant differences between our two populations ( log-linear regression: R2 = 0 . 89 , p< 0 . 001 ) .
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Understanding how genetic and phenotypic diversity are generated and maintained in natural populations is a central question in biology . Latitudinal clines in D . melanogaster represent a model system for investigating the biological and population genetic basis for local adaptation . Recent technological and statistical advances in population genomics have opened up new opportunities for investigating the extent and nature of naturally segregating variation maintained by spatially varying selection . Here , we test hypotheses generated from population genomic approaches suggesting that DNA repair is influenced by spatially varying selection in D . melanogaster . We hypothesized that UVB , which causes DNA damage and varies with latitude , could interact with genome replication stress during early embryogenesis , leading to spatially varying selection on DNA repair in this species . Here , we combine phenotypic , genetic , transcriptomic , and population genomic analyses supporting this hypothesis .
|
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"Abstract",
"Introduction",
"Results",
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"Discussion",
"Materials",
"and",
"Methods"
] |
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"invertebrates",
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"population",
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] |
2016
|
The Adaptive Significance of Natural Genetic Variation in the DNA Damage Response of Drosophila melanogaster
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Malaria remains endemic in several countries of South America with low to moderate transmission intensity . Regional human migration through underserved endemic areas may be responsible for significant parasite dispersion making the disease resilient to interventions . Thus , the genetic characterization of malarial parasites is an important tool to assess how endemic areas may connect via the movement of infected individuals . Here , four sites in geographically separated areas reporting 80% of the malaria morbidity in Colombia were studied . The sites are located on an imaginary transect line of 1 , 500 km from the northwest to the south Pacific Coast of Colombia with a minimal distance of 500 km between populations that display noticeable ethnic , economic , epidemiological , and ecological differences . A total of 624 Plasmodium vivax samples from the four populations were genotyped by using eight microsatellite loci . Although a strong geographic structure was expected between these populations , only moderate evidence of genetic differentiation was observed using a suite of population genetic analyses . High genetic diversity , shared alleles , and low linkage disequilibrium were also found in these P . vivax populations providing no evidence for a bottleneck or clonal expansions as expected from recent reductions in the transmission that could have been the result of scaling up interventions or environmental changes . These patterns are consistent with a disease that is not only endemic in each site but also imply that there is gene flow among these populations across 1 , 500 km . The observed patterns in P . vivax are consistent with a “corridor” where connected endemic areas can sustain a high level of genetic diversity locally and can restore parasite-subdivided populations via migration of infected individuals even after local interventions achieved a substantial reduction of clinical cases . The consequences of these findings in terms of control and elimination are discussed .
Malaria elimination is a public health priority . Yet , regardless of a notable reduction in the global malaria burden , more than 40% of the world’s population remains at risk of infection [1] . Although endemic areas of South America historically have had low to moderate malaria transmission [2] , they are still a significant challenge to control efforts . Indeed , malaria in South America shows idiosyncratic epidemiologic complexities that involve human behavior ( e . g . , regional movements ) , vector ecology , the high prevalence of asymptomatic-subclinical infections , and the presence of Plasmodium vivax , a resilient parasite that requires a demanding treatment to eliminate dormant liver parasite forms [3–8] . Moreover , the uncontrolled surge of malaria cases from Venezuela , followed by mass migrations from its underserved endemic areas to neighboring countries , such as Brazil , Colombia , and others , are putting the continent at further risk , threatening to “roll-back” the regional efforts and progress toward elimination [9 , 10] . Considering all these factors , a characterization of the genetic makeup of malarial parasites in South America will not only provide feedback to the control program , but it also produces a baseline genetic profile of the parasites to further assess the effects of uncontrolled human migration and relocation on malaria incidence . Parasite genetic studies have focused on detecting deviation from random mating ( population structure ) , a pattern that results from several processes including inbreeding , population expansions , and geographic differentiation due to limited migration [11–15] . Beyond characterizing such patterns , parasite genetic investigations must focus on their correct interpretation in the context of specific questions such as understanding whether movements of infected individuals may connect endemic areas and therefore contribute to monitoring malaria transmission and the persistence of the disease in a given region . Here four Plasmodium vivax populations are studied . The sampling sites are located along an imaginary transect line of approximately 1 , 500 km from the northwest to the south Pacific Coast of Colombia . Those four human populations showed different levels of P . vivax prevalence [7] , and they are separated by a minimal distance of 500 km by road between any two of those populations . Given the noticeable ethnic , economic , and ecological differences between these communities [6–8] , parasite migration via human movements between communities is expected to be limited . Thus , the hypothesis that these parasite populations were relatively isolated from each other was tested .
We selected four localities with high malaria prevalence but different average annual parasite incidence ( API ) [7 , 16] . In all four localities ( see Fig 1 in [16] ) , P . vivax has shown to be a resilient parasite . These localities are: ( i ) Tierralta from the Department of Córdoba in the northern area ( API ~10 . 7 ) ; ( ii ) Quibdó ( Department of Chocó , API ~25 ) , ( iii ) Buenaventura ( Department of Valle del Cauca , API ~3 . 1 ) , and ( iv ) Tumaco ( Department of Nariño , API ~6 . 9 ) in the southeast area of the Pacific Coast . In Tierralta ( ~90 , 000 inhabitants ) and Buenaventura ( ~350 , 000 inhabitants ) , the predominant malaria parasite species is P . vivax ( ~85% and 75% respectively ) while in Quibdó ( ~100 , 000 inhabitants ) and Tumaco ( ~160 , 000 inhabitants ) most of the malaria cases are caused by Plasmodium falciparum with P . vivax having a clearly lower prevalence ( ~30% and 21% respectively ) . The samples were collected between 2012–2013 in all areas with Buenaventura having an extended sampling ( from 2011 to 2015 ) . A complete description of these areas can be found elsewhere [16] . A total of 1 , 328 symptomatic volunteers were passively recruited when visiting the health posts for malaria diagnosis [16 , 17] . Patients with malaria infection as determined by microscopic examination of Giemsa-stained thick blood smears ( TBS ) received oral and written explanations about the study and , after free willingness to participate , were requested to sign an informed consent ( IC ) form previously approved by the Institutional Review Board ( IRB ) affiliated to the Malaria Vaccine and Drug Development Center ( MVDC , Cali-Colombia ) . The IC from each adult individual or informed assent ( IA ) from the parents or guardians of children <18 years of age was obtained . Individuals between seven and 17 years of age were asked to sign an additional IA . A trained physician completed a standard clinical evaluation and a physical examination in all symptomatic malaria subjects . All individuals were treated by the local health provider as soon as the blood sample was drawn , using the national antimalarial therapy protocol of the Colombian Ministry of Health and Social Protection ( MoH ) [16] . Everyone received a unique code number to simplify data collection and identification . Out of the 1 , 328 patients , 624 samples ( 47% ) from patients infected with P . vivax were included in this study , and the parasites were genotyped as described below . Total DNA was extracted from the whole blood samples using the PureLink Genomic DNA kit ( Invitrogen , USA ) . Parasite species was confirmed by a Real-time PCR ( RT-qPCR ) as described elsewhere [18] . Standard P . vivax DNA positive and negative controls were included in each batch of tests . Genomic parasite DNAs were genotyped by using fluorescently labeled PCR primers that target microsatellite loci ( STRs ) . A set of eight standardized STRs for P . vivax was used; these loci were selected out of the pool that has been previously explored [11] . In particular , loci MS2 , MS5 , MS6 , MS15 [19] and 14 . 185 , 8 . 332 , 2 . 21 , 3 . 35 [20] . All PCRs were performed in 15 μL reactions with 2 μL of total genomic DNA , 0 . 25 mM of each primer , and 7 . 5 μL of PCR Master Mix ( Promega , USA ) ( it includes 0 . 05 U/μL of Taq DNA polymerase , 2X reaction buffer , 0 . 4 mM each dNTP , and 3mM MgCl2; Promega , USA ) . A negative control ( nuclease-free dH2O ) and positive control ( a P . vivax infected blood sample confirmed to be positive by thick blood smear examination ) were used . Amplification conditions for PCRs were reported elsewhere depending on the set of primers [19 , 20] . Fluorescently labeled PCR products were separated on an Applied Biosystems 3730 capillary sequencer and scored using GeneMarker v2 . 6 . 7 ( SoftGenetics LLC ) . After the microsatellite pattern was identified across samples in the four populations , all alleles were scored at a given locus if minor peaks were more than one-third the height of the predominant peak . A sample was considered as a single infection if it had only one allele per locus at all the genotyped loci as previously described [11–15 , 19 , 20] . The finding of one or more additional alleles at any locus was interpreted as a multiple ( polyclonal ) infection with two or more haploid genotypes in the same isolate ( transmitted by one or several mosquitoes ) . Missing data ( no amplification ) were reported by locus but not considered for analyses that require multilocus genotypes , such as haplotype networks . One of the approaches typically used to compare populations with different transmission intensities is to estimate the average multiplicity of infection ( MOI ) [21] . MOI is defined as the average number of distinct parasite genotypes concurrently infecting a patient . In this study , MOI was estimated ( per loci–ignoring missing data—and on average per population ) as the average number of super-infections ( neglecting co-infections ) using a maximum-likelihood ( ML ) method that allows estimating profile-likelihood confidence intervals [21] . MOI calculations were stratified by geographical region , where the sample from Buenaventura was split into two groups of samples taken before April 2013 and after April 2013 . In addition to MOI , the ML method [21] was employed to estimate allele frequencies at each locus . MOI and allele frequencies were calculated using the R-script provided in [22] . Typically , the MOI estimates from this method have little bias , and the frequency estimates are almost unbiased [22] . To compare the MOI estimates of Tumaco with the other locations a two-sided bootstrap test with the null hypothesis of equality of MOI was performed as follows . For B = 10000 bootstrap samples ( consisting of subsamples from Tumaco and the other location of corresponding sample size ) the difference in the MOI estimates of Tumaco and the other location were calculated , and p-values were obtained by using a bootstrap method [23] . Holm’s multiple correction was applied . Tests were not performed for locus 3 . 35 as the data for this marker in the Tumaco population was not informative [21 , 22] . Under the assumption that patterns in the parasite genetic variation are driven , at least in part , by the regional movement of infected individuals and the local epidemiology [11–13 , 15] , a suite of approaches was used to characterize the genetic variation in the circulating P . vivax within each population and the differentiation of the parasite populations among the four localities . The genetic diversity within each sampled population was estimated using a series of summary statistics implemented in the Haplotype Analysis software v1 . 05 [24] where the number of different sampled multilocus genotypes ( SMG ) , the number of unique genotypes ( G ) , the number of private genotypes ( PG ) , and the Nei’s index of genetic diversity ( He ) were estimated [25] on all the multi-locus genotypes that we could unambiguously identify . As usual , He was defined as He=[n/ ( n−1 ) ][1−∑i=1Lpi2] where n is obtained by taking the sum of identifiable genotypes ( phased ) over all samples , and pi is the relative frequency of the i-th haplotype ( i = 1 , … , L ) in all sampled haplotypes . He gives the average probability that a pair of alleles randomly selected from the population is different . For this analysis , complex infections with differences at more than two loci were not included because the haploid genotypes could not be inferred . However , He was also calculated per locus . In this case , pi is the frequency of allele i , and He is the average probability that a pair of alleles randomly selected from the population is different . For the allele frequencies , the ML estimates [21] were used . Furthermore , non-parametric bias-corrected and accelerated bootstrap confidence intervals were estimated based on 1 , 000 bootstrap replications using the jackknife estimate for the acceleration factor as described in [23] . To assess the parasite population differentiation between localities , normalized fixation index ( Fst ) were also estimated and their significance assessed using a randomization test . A limitation in this kind of analysis is that samples with multiple alleles at more than two loci were not included because the haploid genotypes could not be inferred . Then , an analysis of molecular variance ( AMOVA ) [26 , 27] was performed as implemented in GenAIEx version 6 . 5 [28] . The AMOVA allows comparing the proportion of the parasite genetic variance within and between populations using the PHIPT statistic ( analog of the Fst ) . Probabilities for the AMOVA statistics were calculated based on individual randomizations . In order to test that changes in malaria incidence , particularly after deploying interventions , were driven by the introduction or residual presence of a few parasite lineages , pairwise measurements of linkage disequilibrium ( LD ) were estimated to detect potential clonal expansions . Since many pairs of loci have a different number of alleles , standard measurements of LD will be essentially biased and cannot be adequately compared across loci and populations [29] , this is particularly common in microsatellite loci . Thus , conditional asymmetric linkage disequilibrium ( ALD ) measurements were used since they consider such differences in the number of alleles in each pair of loci [29] . Comparable to other association statistics commonly used to detect LD , ALD estimates go from 0 to 1 with 0 implying total independence and 1 complete linkage . ALD is a measure to compare pairs of loci and requires frequency estimates of two-locus haplotypes of alleles at both loci . To do so , samples with multiple alleles at both loci were disregarded ( these were only very few for each two-locus comparison ) . As a result , it was possible to phase two-locus haplotypes and estimate their frequency using the ML method of [21] , as it was done for the allele frequencies of both loci . The estimates were calculated using the R-script provided in [22] . To identify clusters of parasites that could separate the four localities , two methods were used , ( i ) a Bayesian model-based clustering algorithm that considers admixture as implemented in the Structure v2 . 3 . 4 software [30] and ( ii ) a principal component analyses ( PCA ) that does not use explicit admixture model ( but that is not assumption free ) . The Bayesian clustering approach assigns genotypes to K populations or clusters characterized by a set of allele frequencies at each locus . The observed genetic diversity was evaluated under different K values ( K = 2 to 15 ) , and each K value was run independently 15 times with a burn-in period of 100 , 000 iterations followed by 100 , 000 iterations . The admixture model that allows for the presence of individuals with ancestry in two or more of the K populations was used in all the analyses [30] . Structure Harvester was used to compute Delta K values from Structure [31] . CLUMPP ( Cluster Matching and Permutation Program ) was used to facilitate the interpretation of population-genetic clustering results [32] , and then , distruct v1 . 1 was used to graphically display the clustering results [33] . The posterior probability for each number of populations or clusters ( K ) was computed , and the K-value that better explains the genetic data was an estimate of the number of circulating clusters or populations . Complex infections with differences at more than two loci were not included in this analysis . Then , PCA was estimated using R on all the samples , but also eliminating alleles that appear less or equal than 5 and 10 times in the whole sample ( across all regions ) to explore if there was an effect driven by alleles in low frequency . For the PCA , alleles at each locus were coded by 0–1 variables , indicating the absence and presence of alleles , where the number 0–1 variables for each locus coincides with the number of alleles at that locus . This allowed including samples with missing data and samples with multiple infections . Concerning of alleles at low frequencies , alleles that occurred less or equal than 5 or 10 times , were not considered in some of the PCA analyses from the data set by deleting the respective 0–1 variables . This allowed exploring the effect of potential amplification errors without eliminating samples completely . For the PCA only data from Buenaventura collected before April 2013 was included as only this time range of collection is comparable with that of the other regions . Given that , alleles in low frequency could affect MOI , heterozygosity , linkage disequilibrium , and PCA analyses; those calculations were repeated eliminating those alleles that were in low frequency ( less than 2% ) . Although no differences were observed , those results were provided . Haplotype genealogies found in malaria cases for each locality were inferred for eight microsatellites by using the Global Optimal eBURST algorithm [34] , as implemented in PHYLOViZ [35] . Using an extension of the goeBURST rules up to n-locus-variants-level ( nLV , where n equals to the number of loci in our dataset: eight ) , a Minimum Spanning Tree-like structure was drawn to cluster the 386 sequence types ( STs ) into a clonal complex ( CC ) based on their multilocus genotypes . This analysis only included single infections and complex infections with differences at only one locus given that the haploid genotypes can be inferred .
The number of isolates genotyped from each population varied between locations: 258 from Tierralta , 65 from Quibdó , 235 from Buenaventura , and 66 from Tumaco . A description of the samples in terms of single and multiclonal infections , samples with incomplete data , and how they were used on different analyses is reported in S1 Table . Regardless of the differences in sampling , the prevalence of multiple infections showed a reduction north to south . In Tierralta , 84 samples ( 32 . 6% ) have more than one distinct parasite genotypes concurrently infecting a patient . In the case of Quibdó , Buenaventura , and Tumaco , the number of patients that had infections with more than one lineage in at least one locus was 27 ( 41 . 5% ) , 97 ( 41 . 3% ) , and 31 ( 47 . 0% ) respectively . However , many of those multiple infections were the result of having more than one allele at only one locus so the lineage-specific genotypes could be easily inferred ( S1 Table ) . The estimated MOI parameter was relatively low and consistent across all populations sampled ( S2 , S3 and S4 Tables , S1 Fig ) . Since samples from Buenaventura encompass a longer period , the MOI between 2011 and 2013 was compared to 2013–2015 showing a slight decline; however , 95% confidence intervals of MOI estimates per locus typically overlapped ( see S2 Table ) . Likewise , there is a slight tendency that MOI was lower in Tumaco when compared to the other localities , but it was also not statistically significant except compared with Buenaventura at markers MS2 and MS15 and with Quibdó at marker 15 . A qualitative inspection of the allele-frequency spectra , as estimated from the maximum likelihood approach ( S2 Fig ) , can provide information on the evolutionary forces acting on the observed variation in the parasite populations; e . g . , the patterns suggest that strong bottlenecks did not occur . Here we found that the alleles were shared across all parasite populations; an observation that is consistent with a high degree of relatedness . When examining each parasite population in detail , there was a change in the spectra in Buenaventura between the two periods were compared ( 2011–2013 versus 2013–2015 ) . Furthermore , Tumaco had the most distinct distribution . This qualitative examination of the allele frequency-spectra provided the first view of patterns that were later identified with more statistically suitable methods ( see below ) . The P . vivax mean genetic diversity and the relative heterozygosity ( per locus and genotype-based ) are high in all the sampled populations ( Mean He: 0 . 978; S3 Fig , Table 1 ) . This is evident in other summary statistics as well , such as the number of sampled multilocus genotypes ( SMG ) from the human specimens , the number of distinct genotypes ( G ) , the number of private genotypes ( PG ) , and the Nei’s index of genetic diversity ( He ) ( Table 1 ) . This observation seems relatively common in P . vivax even when the actual loci used to sample the parasite genetic variation differ among studies [15 , 36–40] . The number of private multi-locus genotypes for P . vivax in terms of their geographic origin was also very high ( Table 1 ) with only a few multi-locus genotypes being shared between the populations ( one genotype shared between Tierralta and Buenaventura ) . All these observations are consistent with no evidence of a population bottleneck . Likewise , LD is low between loci ( S4 Fig ) within populations . Because the loci used are physically unlinked , low LD is contrary to the expectations under a bottleneck or clonal expansion scenarios . Tumaco showed the highest LD; this finding was also confirmed by the PCA ( Fig 1 ) where the second principal component divides the Tumaco population into two parts . The other populations have no indication of such a subdivision . Finally , it is worth noting an increase in LD in Buenaventura from 2011–2013 to 2013–2015 ( S4 Fig ) . The normalized Fst values were significant and relatively high , between 0 . 10 and 0 . 23 with Buenaventura and Tierralta being the two populations less differentiated and Tumaco showing the highest Fst values when compared to the others ( Table 2 ) . This result indicates that Tumaco is the most isolated P . vivax population , which is consistent with geography . Considering that most alleles were shared between populations and that many of those were at low frequency , the interpretation of these Fst values as strong evidence of population structure requires additional scrutiny . The genetic differentiation between populations was then further explored using an AMOVA ( Table 3 ) . Only 5% of the variance was explained by the differences between populations , with 95% within populations indicating that those high Fst values could be the result of sampling alleles in low frequency . In the case of the PCA , the first two PCs explained around or less than 10% of the variance ( 6 . 5% for all data , 8 . 5% of alleles with count < = 5 , 10 . 7% of alleles with count < = 10 excluded , Fig 1 ) if alleles with an absolute frequency less or equal to 10 across all populations were neglected . These differences are expected , e . g . , including more alleles with low frequency increases the dimensions of the dataset resulting in less explained variance by the first two principal components . This finding suggests a high relatedness among the populations that were not separated . The first PC was kind of a north-south cline; it separated Tierralta from Buenaventura with Quibdó in the middle , corresponding roughly to the geography . The second PC separated Tumaco from the other population , dividing it into two parts . The Bayesian clustering using the Structure v2 . 3 . 4 software [30] identified four clusters for the four P . vivax populations ( Fig 2A ) . However , there were not clusters linked to a specific locality or population for all the years that were included in this study ( 2011–2015 ) . Then , the analysis was repeated using only the samples from 2012 and 2013 , and three clusters were identified . However , likewise , no specific clusters were linked to a population indicating admixture ( Fig 3A ) . Interestingly , in both Bayesian cluster analyses , a different pattern in the clusters was identified in Buenaventura after 2013 ( Figs 2A and 3A ) . To further explore this pattern , Structure was run using only samples from Buenaventura from May 2011 to March 2015 , and only two clusters were identified ( Fig 4A ) . This analysis also confirmed a change in the clusters in 2014 . Finally , to further examining the relationships between genotypes , haplotypes networks were estimated by the Global Optimal eBURST algorithm [34] using haploid genotypes that could be reconstructed for single infections or those multiclonal infections with highly related multilocus genotypes that differed at one locus only . Three analyses were performed , one included 586 P . vivax genotypes out of the 624 samples from infected patients from the four locations and all the years included ( Fig 2B ) . Second and third analyses included 447 genotypes from 2012 and 2013 for all localities ( Fig 3B ) , and 248 genotypes from Buenaventura ( Fig 4B ) respectively . Consistent with the cluster methods , the genotypes did not show clear geographic boundaries .
Malaria parasite populations are expected to exploit habitats that are fragmented . Usually , a mosaic of communities with different levels of malaria prevalence is observed in areas that ecologically and epidemiologically allow transmission . Whereas the mobility of infected individuals is possible , traveling by underserved populations is generally motivated by economic [41] and social factors [42] . In this investigation , the sampled communities are not only separated geographically but are distinct in terms of economic activities and ethnicity [16] . Although towns that are in endemic regions ( e . g . , Quibdó , Buenaventura , and Tumaco ) have urban foci that maintain transmission [43–45] , major urban areas ( e . g . , Bogotá , Cali , and Medellín ) that act as economic attractors to the inhabitants from all these sites do not sustain malaria transmission [43] . Thus , a limited level of direct human migration between the sites was expected ( e . g . , infected individuals moving between any pair of the sampled sites ) . Considering all these elements , the genetic effects of the parasite population fragmentation between sampled sites were expected to be observed at this geographic scale as result of genetic drift further accelerated by a reduction in the parasite population driven by local deployments of control measurements [11–12 , 15 , 38 , 43 , 46] . Furthermore , if parasites were re-introduced in a given area or parasite populations were recovering after being diminished by an intervention , clonal expansions resulting from such events would yield significant linkage disequilibrium [11–12] . Contrary to expectations , this study showed that all sampled P . vivax populations harbored high genetic diversity , a moderate level of genetic differentiation between populations but with several shared alleles , and relatively low linkage disequilibrium within most of the sampled populations . Since the loci sampled are physically unlinked , and contrary to the scenario of a parasite population expansion after a significant reduction in its size , the limited LD observed indicates a low level of inbreeding . This contrasts with previous observations made on P . falciparum in South America and other P . vivax populations in the region with high LD ( e . g . , [11 , 40 , 47 , 48] ) . Measuring LD using ALD [29] was appropriate since loci exhibited differences in the number of alleles and samples sizes . Furthermore , considering the small number of samples with multiple infections at two loci in every two-locus comparison , the use of ALD allowed incorporating most of the complex infections into the analysis . In addition , using only samples with evident multi-loci genotypes information ( e . g . , eliminating any sample with multiple infections in two or more loci ) biases the estimates . Fst values were relatively high ( 0 . 10–0 . 25 ) . However , part of the observed differentiation could be a sampling effect of alleles in low frequency . Indeed , the AMOVA indicated that most of the variation was explained within populations . This is a similar pattern detected in a substantially smaller geographic area in Peru [15] . Nevertheless , in the Peruvian Amazonia , this observation was somehow predictable since the sampled populations shared an economic center ( Iquitos ) and were interconnected by road separated by a few km ( <20 km ) [15] . Thus , finding a similar pattern among populations that are separated by at least 500 Km is evidence of an important level of parasite gene flow via human migration . A different approach that allows appreciating these patterns of low differentiation is provided by the PCA and the Structure analyses where individual samples were dispersed , and there were not cluster matching geographic patterns . Furthermore , no evidence of clonal expansions was found ( e . g . , low LD ) indicating a limited effect of the deployed interventions on the parasite genetic diversity . Although some genetic structures were observed whenever parasite populations were closely examined ( e . g . , a temporal structure in Buenaventura and two groups within Tumaco ) , the data available did not support strong geographic isolation between these parasite populations . The temporal changes in the genetic structure in Buenaventura may indicate the introduction of new parasites at the time . However , the low level of LD also indicates that this replacement of parasites was not driven by a few lineages that expanded locally but by the influx of a diverse group of parasites . In the case of Tumaco , it showed the highest Fst values , and it differs from the other three parasite populations in terms of average LD and genetic diversity . All these observations are consistent with the fact that Tumaco is the most geographically isolated population with the lowest P . vivax prevalence . However , regardless of these differences , Tumaco shared parasites with the others as indicated by the clustering methods and the haplotype networks ( Figs 2–4 ) . The results presented here are consistent with the existence of a P . vivax malaria corridor that likely facilitates the persistence of these parasite populations across the sampled imaginary transect line of 1 , 500 km . In this context , a malaria corridor is defined as spatially connected endemic areas that sustain a high level of genetic diversity regionally and can restore parasite-subdivided populations ( “rescue effect” ) even after interventions succeeded in reducing transmission locally [49] . Indeed , parasite replacements such as the one documented in Buenaventura after 2013 may be facilitated by human movements and could be common in settings such as the Pacific Coast of Colombia . The identification of the contact zones that effectively sustain the malaria corridor is critical for the long-term success of interventions in Colombia . This is likely the case in other endemic areas of the world where communities are interconnected . The lack of a suitable temporal and spatial sampling across this transect did not allow describing the dynamic of the parasite dispersion and the identification of the contact zones in the context of the proposed malaria corridor model . However , on the positive side , almost no linked multilocus P . vivax genotypes were shared between the sampled populations . This suggests a multi-generation gene flow where genotypes are broken by recombination ( as detected by microsatellite loci ) rather than recently introduced parasite lineages expanding locally . This pattern indicates that regionally coordinated control efforts could increase the fragmentation of the P . vivax populations making them more susceptible to local extinction ( elimination ) . Furthermore , if it is identified , deploying interventions in such contact zones could be a cost-effective strategy for achieving the elimination goal . A possibility to explore is assessing the effect of mining communities since those may act as catching areas for individuals moving from distant places [41] . It could be speculated that , if these mining areas act as contact zones , deploying interventions there will affect malaria regionally by making parasite populations more fragmented and vulnerable to local elimination if interventions were implemented at a proper temporal and spatial scales . This hypothesis can be tested by genotyping parasites prior to and after interventions in the mining areas . It is worth noticing that patterns suggesting connectivity in South America across long geographic distances have been observed in P . falciparum , particularly in the Pacific Coast of Colombia and Peru [48 , 50] . Unlike P . vivax , P . falciparum shows a strong LD across populations indicating that such connectivity allows for stable inbreed clones to expand [48] . Nevertheless , the spatial and population structure differences between these two parasite populations in the context of low transmission areas is a matter that should be investigated . The samples included in this study were mostly from symptomatic patients . However , the importance of asymptomatic carriers in these dynamics can be anticipated [45 , 51] . Even though genotyping submicroscopic specimens from South America for an extended set of microsatellites is technically difficult with the limited amount of blood collected during malaria surveillance , the handful studies already published with as little as three microsatellites seem to indicate that these patients maintain high genetic diversity [45 , 52] . Furthermore , there is evidence that they can infect the local vectors [53] . Indeed , asymptomatic individuals could account , at least in part , for the similar level of MOI for P . vivax infections across settings that indicate a comparable low to moderate transmission in all sites , regardless of differences in malaria morbidity [21] . It is worth noticing that the lack of association of MOI with transmission intensity is consistent with other reports [54] . In addition to asymptomatic patients affecting MOI , other factors should be considered such as the spatial connectivity of an endemic area with unsampled parasite populations and the demographic history of the parasites that determines the detectable lineages given a set of loci ( 11–16 ) . Here we provide a static but valuable picture of a complex process critical for malaria control and elimination in a P . vivax endemic region . Although important in terms of providing a partial description of how parasites disperse in space , studies as the one reported here ( as many others ) lack the spatial and epidemiological detail required for accurately describing such spatiotemporal dynamics . Nevertheless , this study highlights the need for carefully planned epidemiological investigations that , together with population genetic tools , can accurately model these complex dynamics at an actionable time scale , such as; make inferences from one transmission season to the next .
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The regional movements of infected individuals that connect suitable transmission areas make malaria resilient to control efforts . Those movements are expected to leave genetic signatures in the parasite populations that can be detected using analytical tools . In this study , the genetic makeups of Plasmodium vivax populations were characterized to assess whether the most endemic areas in Colombia were connected . Samples were collected from passive surveillance studies in four locations across an imaginary transect line of 1 , 500 km from the northwest to the south Pacific Coast of Colombia ( South America ) . Considering the distance , and contrary to expectations , we found weak levels of genetic differentiation between these parasite populations with no evidence indicating that their genetic diversity has been eroded as expected whenever the prevalence of the disease is successfully reduced , e . g . , through control programs or environmental changes . Although the sampling lacks the geographic and temporal detail to describe how the dispersion of parasite lineages occurred , the observed patterns are consistent with a series of infected populations that are connected in space by human movements allowing the parasite to diffuse across this 1 , 500 km transect . This malaria corridor needs to be characterized to achieve elimination .
|
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2019
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Limited differentiation among Plasmodium vivax populations from the northwest and to the south Pacific Coast of Colombia: A malaria corridor?
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The flagellated protozoan Giardia duodenalis is a common gastrointestinal parasite of mammals , including humans . Molecular characterizations have shown the existence of eight genetic groups ( or assemblages ) in the G . duodenalis species complex . Human infections are caused by assemblages A and B , which infect other mammals as well . Whether transmission routes , animal reservoirs and associations with specific symptoms differ for assemblage A and assemblage B is not clear . Furthermore , the occurrence and clinical significance of mixed ( A+B ) infections is also poorly understood . To date , the majority of PCR assays has been developed to identify all G . duodenalis assemblages based on the use of primers that bind to conserved regions , yet a reliable identification of specific assemblages is better achieved by ad hoc methods . The aim of this work was to design simple PCR assays that , based on the use of assemblage-specific primers , produce diagnostic bands of different lengths for assemblage A and B . We first generated novel sequence information from assemblage B , identified homologous sequences in the assemblage A genome , and designed primers at six independent loci . Experiments performed on DNA extracted from axenic cultures showed that two of the six assays can detect the equivalent of a single cyst and are not negatively influenced by disproportions between DNA of each assemblage , at least up to a 9∶1 ratio . Further experiments on DNAs extracted from feces showed that the two assays can detect both assemblages in single tube reactions with excellent reliability . Finally , the robustness of these assays was demonstrated by testing a large collection of human isolates previously typed by multi-locus genotyping .
Giardia duodenalis is an important cause of diarrhea in humans worldwide and is responsible for an estimated 2 . 8×108 cases per year [1] . The infection is transmitted by the fecal-oral route through ingestion of infective cysts . The prevalence of the infection is higher in developing countries as the poor sanitary conditions favour the contamination of water and food with cysts . Approximately 200 million people have symptomatic giardiasis in Asia , Africa and Latin America and about 500 , 000 new cases are reported each year [2] . The parasite has been included in the Neglected Diseases Initiative of the World Health Organization ( WHO ) due to its diffusion among children in these regions of the world [3] . A considerable amount of data has shown that Giardia duodenalis should be considered as a species complex that comprises at least eight distinct genetic groups , referred to as assemblage A to H [4] . Isolates from the different assemblages show little , if any , morphologic variation , thus their identification is currently based on molecular characterization . To date , only assemblages A and B have been associated with human infections , but are also found in a number of other mammalian hosts [5] . The clinical manifestations of giardiasis in humans are highly variable and range from the absence of symptoms to acute or chronic diarrhoea often associated with dehydration , weight loss , abdominal pain , nausea and vomiting [6] . The severity of disease is likely determined by the interplay between the host status ( e . g . , age , nutritional and immunological conditions ) and intrinsic features of the parasite ( e . g . , assemblage and genotype ) . However , genetic traits that influence the virulence and other aspects of the infection are unknown and efforts to correlate the parasite genetic make-up and the clinical symptoms in the host have generated controversial results [7] . It is also conceivable that the two assemblages have different epidemiological features as infectivity , zoonotic potential and route of transmission , but definite differences are not yet established . Until recently assemblages A and B have been considered as genetic variants of the same species . Nowadays , the bulk of the genetic data indicates that the two assemblages A and B are probably two independent species , despite the morphological similarity . The genome of the assemblage A ( strain WB ) has been sequenced [8] and , more recently , a draft of the genome of the assemblage B ( strain GS ) has been published [9] . The comparison of the genomes , which show only the 77% of identity at the nucleotide level , supports the hypothesis that assemblage A and B represent different species [9] . Nevertheless , the taxonomy of the G . duodenalis species complex remains a matter of discussion [4] , [10] . The aim of this work is the development of PCR assays that , through the use of assemblage-specific primers generating amplification products of different size , allow the detection of assemblage A and B in human fecal samples .
The human samples from Italy have been collected by various Italian health services and have been received at ISS on different times for a confirmation of the diagnosis of giardiasis . All samples were received in an anonymous form . The human samples from Sweden are from a study approved by the Regional Ethics Committee of Karolinska Institutet , Stockholm , Sweden . Written consent for being part of the study was obtained from each patient and parents or primary caretakers signed for their children . The recombinant and microbiological techniques , media , the preparation of plasmid DNA and the isolation of restricted fragments all followed standard procedures [11] . DNA sequencing was performed using Sanger's procedure [12] . The G . duodenalis isolate Ad-28 [13] has been used as a source of assemblage B genomic DNA . 1 µg of DNA was digested with BstYI ( New England Biolabs ) and ligated using the Quick ligation kit ( New England Biolabs ) to the plasmid pBLUESCRIPT , previously linearized using Bam HI ( New England Biolabs ) . Escherichia coli XL1-Blue competent cells were used as a recipient strain and transformed with 1 . 2 µg of ligated DNA and plated on LB agar plates containing 1 mM IPTG , 30 µg/ml X-Gal and 100 µg/ml ampicillin to select the white recombinant colonies . Approximately 100 recombinant clones from this library were randomly chosen and replicated from the original plates on 3 square plates with grid ( 6×6 cells ) , and each clone was named with the coordinates of its position . From each clone plasmid DNA was prepared using Qiaprep miniprep ( Qiagen ) following the manufacturer's protocol . Plasmidic DNAs from recombinant colonies were used to amplify and sequence the exogenous inserts using M13-for ( CGCCAGGGTTTTCCCAGTCACGAC ) and M13-rev ( GTCATAGCTGTTTCCTGTGTGA ) vector-specific primers using GoTaq Green Master Mix ( Promega ) under these conditions: 2 min at 94°C , 35× ( 30 sec at 94°C , 30 sec at 62°C , 1 min at 72°C ) , 7 min at 72°C . Amplification products were analyzed on 1 . 5% agarose gel stained with ethidium bromide and suitable fragments ( 200–500 bp in length ) were purified with Qiaquick PCR purification kit ( Qiagen ) and sequenced . Sequences of recombinant inserts were assembled using the software Seqman II ( DNASTAR ) . The homologous genes in the assemblage A genome were identified by BLAST both at http://blast . ncbi . nlm . nih . gov/Blast . cgi and at www . giardiadb . org . Alignments were obtained using CLUSTAL W [14] . Assemblage-specific PCR assays were performed on a Veriti 96 Well Thermal-cycler ( Applied Biosystem ) using the GoTaq Green Master Mix ( Promega ) or the HotStarTaq Master Mix ( Qiagen ) . Concentration of primers in PCR reactions was 10 pM each and sequences of primer pairs are reported in Table 1 . All reactions started with an activation-denaturation step at 94°C for 5 min and then were carried out for 40 cycles , each consisting of 94°C for 30 sec , an annealing step of 30 sec at a temperature that varied depending on the marker ( see below ) , and of 72°C for 30 sec . A final extension step was carried out at 72°C for 7 min . Optimal annealing temperatures were: 52°C for 3B4-HP , 56°C for 4E1-HP and 5C1-P21 , 58°C for 1A3-HCP , 60°C for 4F1-HP and 5A2-VSP . PCR products were electrophoresed on ethidium bromide-stained 2% agarose gels in TAE 1× at 60 Volt for 90 min and photographed with Geliance 600 imaging system ( Perkin Elmer ) . The DNA molecular weight marker XIII ( Roche ) was used as size standard in gel electrophoresis . The panel of 15 human samples from Italy has been previously described [15] as well as the panel of 51 human samples from Sweden [16] . All those samples have been genotyped at the level of assemblage using a multi-locus typing scheme based on the sequence analysis of the beta-giardin ( ß-giardin ) , triose phosphate isomerise ( TPI ) and glutamate dehydrogenase ( GDH ) genes .
To identify novel sequences of G . duodenalis assemblage B , 29 randomly cloned fragments from a plasmid library of the Ad-28 strain were fully sequenced . The comparison of these sequences with the genome of assemblage A identified sequences with high levels of homology , most likely representing true orthologs . The genomic localization and the relative annotation of these sequences , as found in the GiardiaDB , including the assemblage E homologs [17] , are given in the Table S1 . A high level of inter-assemblage variability was found when comparing the homologous sequences of WB and GS strains , with identity values at the DNA level between 63 and 81% . Noteworthy , all the clone sequences corresponded to coding sequences , even if two of them ( 1A3 and 3B4 ) were annotated only in the assemblage A genome but not in the assemblage B draft genome ( Table S1 ) . For the design of PCR assays , a further selection of sequences was based on the following criteria: i ) sequences showing full specificity for G . duodenalis; ii ) unambiguous identification of the orthologs in assemblage A; iii ) sufficient variability to design specific primer pairs for each assemblage . Six sequences were selected for further analysis following these criteria ( Table 2 ) . Primers were designed to amplify fragments of different size from each assemblage at each locus , thus allowing the identification of each assemblage after electrophoretic separation of PCR products ( Table 1 ) . An example of primer design for the 4E1/HP marker is shown in Figure 1 , whereas information for the remaining markers is given in Table S2 . The amplicons produced by these PCR assays on the reference DNA of assemblage A and B are shown in Figure 2 ( throughout the manuscript , PCR assays are named combining the name of the clone and the acronym of gene name as reported in GiardiaDB , for example: 4E1-HP ) . All assays produced the expected bands only when the specific template and primers were used , and no amplification was observed when assemblage B specific primers were tested on assemblage A template or when assemblage A specific primers were tested on assemblage B template ( see Figure 2 ) . To select the most appropriate assays for use on clinical isolates , 15 DNA isolates extracted from human fecal samples , previously genotyped as assemblage A ( 5 isolates ) , assemblage B ( 5 isolates ) and mixed A+B assemblages ( 5 isolates ) , were tested using the six assays . To select for reliable single-step PCR assays , the reactions were run in the presence of primers for the two assemblages in the same tube . Three assays ( 4F1-HP , 3B4-HP and 5A2-VSP ) yielded the expected bands on DNAs from either assemblage A or B when tested separately , but the interpretation of results was more difficult in case of mixed A+B infections . This was probably due to the different sensitivity of these assays in the amplification of one of the two templates ( data not shown ) . The 1A3-HCP assay produced bands of similar size from each assemblage ( i . e . 326 bp for A assemblage and 347 bp for B assemblage ) , therefore requiring a high resolution gel to detect mixed infections ( data not shown ) . However , the 4E1-HP and 5C1-P21 assays , under these conditions , yielded excellent results in terms of sensitivity for both assemblages and were in total agreement with the genotyping results obtained previously for these isolates ( Figure 3 ) . To obtain a more accurate evaluation of the sensitivity , serial dilutions of reference DNAs from both assemblages were amplified using the 4E1-HP and 5C1-P21 assays . As shown in Figure 4 ( panel A ) , both assays were able to detect Giardia DNA with a theoretical limit of less of one cyst ( corresponding to 16 copies of the target ) . In extremely diluted samples , both assays appeared to be more sensitive on assemblage B ( detection of 0 . 15 cyst or 2 copies ) compared to assemblage A ( detection of 0 . 75 cyst or 12 copies ) . Since the number of cysts of each assemblage that can be found in mixed clinical samples can be disproportioned , we tested our assays with variable proportions of DNA of the two assemblages to simulate unbalanced assemblage A+B infections . The experiments were performed with calculated ratios of 6∶4 , 7 . 5∶2 . 5 , and 9∶1 , starting from a total amount of genomic DNA equivalent to 10 cysts . As shown in Figure 4 ( panel B ) , the two assays yielded the expected bands in all cases , indicating that the detection of a single cyst from one assemblage is possible in the presence of nine cysts of the other assemblage . Therefore the two assays could detect mixed A+B even if the less abundant assemblage constituted 10% of the total amount of Giardia DNA . The 4E1-HP and 5C1-P21 assays were used to amplify a panel of 51 DNAs from human cases of giardiasis , which have been previously characterized by multi-locus genotyping ( MLG ) at the ß-giardin , TPI and GDH genes [16] . The results , summarized in Table 3 , showed an excellent agreement between the two sets of results . A few discrepancies were , however , noted . Thus the sample G098 was typed as assemblage A by 4E1-HP and 5C1-P21 assays , but MLG identified it as mixed A+B infection , whereas sample G162 was typed as a mixed A+B infection , but as assemblage A by MLG . Similarly , the 5C1-P21 assay detected a mixed A+B infection in sample G109 , which was typed as assemblage A by MLG , whereas sample G162 was typed as a mixed A+B infection , but as assemblage A by MLG ( Table 3 ) . Of note , four samples ( G077 , G093 , G127 and G161 ) could not be amplified by the 5C1-P21 assay , whereas all samples were amplified by the 4E1-HP assay . Importantly , this experiment showed that the 4E1-HP and 5C1-P21 assays also detect the A2 and A3 genotypes of assemblage A , which differ from the A1 genotype used for primer design , and which are by far the most common genotypes found in humans .
Human giardiasis is caused by two distinct genetic groups of Giardia duodenalis , known as assemblages A and B , which are likely to represent distinct species [4] , [10] , [18] . Both assemblages are found associated with human infection globally , and have been also detected in various animals [19] . At present , various molecular methods are available to distinguish these assemblages , mainly by nested PCR followed by RFLP or DNA sequencing , or by real-time PCR [19] . The majority of these assays are based on the amplification of a gene fragment with primers that bind to DNA sequences that are conserved in the two assemblages ( or conserved in all G . duodenalis assemblages or in Giardia species ) . Molecular typing techniques have been extensively used to study the complex epidemiology of giardiasis , including controversial aspects like the extent of zoonotic transmission , the occurrence of mixed infection in humans , the potential for genetic exchanges between parasite isolates , and the correlation between clinical symptoms and the type of assemblage [20] . In the course of these studies , it has been observed that a multi-locus typing scheme is needed to address those issues [5] , [15] , [16] . In this work , we have developed six novel assays that detect and discriminate G . duodenalis assemblages A and B using a conventional PCR procedure . Based on fixed differences at these loci between the two assemblages , specific primers were designed to generate amplicons of diagnostic size for each assemblage at each locus . The loci targeted by these assays are highly specific for G . duodenalis as no significant homology with DNA sequences of other organisms were detected by a BLAST search of the GenBank database ( update date: 2012/03/15 ) . Therefore , these assays represent reliable tools to identify G . duodenalis assemblages in human samples and can be used in combination to perform a multi-locus genotype ( MLG ) analysis by means of a simple PCR protocol that requires only cheap equipments . We further showed that two of the six assays ( 4E1-HP and 5C1-P21 ) are particularly well suited to allow for an effective diagnosis of the parasite in human stools with a simultaneous identification of the assemblage . We demonstrated that these two assays work efficiently in the same reaction tube , producing results that are easy to interpret ( Figure 2 ) , are highly sensitive , detecting the DNA equivalent of a single cyst ( Figure 4 ) , and appropriate to detect mixed assemblage A+B infections even if the ratio between the assemblages is strongly unbalanced ( Figure 4 ) . The two targeted loci correspond to single copy genes that are located on different chromosomes in the WB genome , namely chromosome 4 for 4E1-HP and chromosome 1 for 5C1-P21 , and these chromosomal regions are syntenic in assemblage B ( data not shown ) . The robustness of the 4E1-HP and 5C1-P21 assays was confirmed by testing a panel of 51 human isolates from Sweden to compare the rate of amplification and the genotyping results with those obtained using MLG analysis in the original publication [16] . The excellent agreement between the two sets of results ( Table 3 ) indicates that the two PCR assays have sensitivity and accuracy comparable with the most commonly used genotyping tools . We therefore believe that these assays will find application in studies aimed at understanding some intriguing aspects of giardiasis , the most important of which are briefly discussed below . The occurrence of mixed A+B infections in human cases of giardiasis appears to be more common than previously believed [21]–[22] . Thus , the proper detection of both assemblages is an important aspect for molecular epidemiology studies and for routine screening in clinical settings . The reliable detection of cases of mixed infections is influenced by several factors , including the proportion of each assemblage in the specimen and the extent of preferential amplification of one assemblage over the other . The assays presented in this work can detect the least abundant assemblage even when the other assemblage is 9 times more represented in the sample , at least using deliberately mixed genomic DNAs from axenic cultures ( Figure 4 ) . Moreover , the assemblage-specific primers do not compete for the same template , as they bind to different portions of the genes , and this should help to prevent the amplification of the most abundant template when more than one template is present . It is interesting to note that the differences among the various tests , except for false negatives , always involve samples where at least one of the assay indicates a mixed A+B infection . This fact suggests that assays with different sensitivity for one of the two assemblages not always detect correctly mixed infections . In this respect , the 4E1-HP and 5C1-P21 PCR have a proved reliability also in case of strongly unbalanced templates and demonstrated to be sensitive enough to detect mixed assemblages in the presence of a few copies of the Giardia genomes . Although these novel assays may be less sensitive than nested PCR in absolute terms , they are better suited to identify mixed infections and , along with previously published procedures [21]–[23] , will consent to address this important aspect in future studies . Understanding the relative contribution of the parasite's genetic variability and host factors in the establishment of clinical giardiasis has been the subject of a number of studies in both developed and developing countries [20] . So far , these studies have reached controversial conclusions . Assemblage A was associated with diarrhea ( and other symptoms ) in studies in India , Spain and Turkey , whereas an association with assemblage B was reported in Malaysia , The Netherlands and Ethiopia . No association with either assemblage was found in Cuba , The United Kingdom , Brazil and Albania . Whereas those conflicting results can be explained by differences in the study design , in the population considered ( adults versus children ) , and in the definition of symptoms , is presently unknown . However , since generic PCR assays have been used in those studies , the contribution of mixed infections has not been taken into account . Albeit the assays have been designed and tested on G . duodenalis isolates from humans , they can be used to detect assemblages A and B in animal samples . It is important to recall that mixed infections involving both host-specific and zoonotic assemblages of G . duodenalis have been frequently identified in fecal samples from livestock and domestic animals [23]–[24] . We noted that the primer pairs used in our assays do not match with the homologous sequences of the assemblage E genome [17] , at least as inferred by an in silico analysis ( data not shown ) . Therefore , it should be possible to use these assays to detect assemblages A and B in livestock , albeit a direct testing will be required to rule out unspecific reactions . In conclusion , two PCR methods are proposed as valuable tools for the molecular diagnosis of human infections caused by Giardia duodenalis . Since these assays do not require particular expertise or expensive instruments , they can be used in laboratories with basic molecular equipments .
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Giardia duodenalis is an important cause of diarrhoea in humans worldwide , even if the burden of infection is higher in developing countries where the poor sanitary conditions favour the contamination of water and food with infective cysts . The parasite is considered as a species complex that comprises at least eight distinct genetic groups , referred to as assemblage A to H . Humans are infected only by assemblages A and B , which can only be distinguished by molecular methods . The clinical manifestations of giardiasis in humans are highly variable and range from the absence of symptoms to acute or chronic diarrhoea . Since genetic traits that influence the virulence are yet unknown , the identification of the assemblage is considered a primary element in the study of human giardiasis . Current methods are time consuming and/or require expensive instruments . Here , we describe the development and application of single step PCR methods that allow to detect and distinguish assemblages A and B from human fecal specimens simply by gel electrophoresis of the amplification products . The novelty of the assays described in our manuscript is the reliability in detecting mixed infections and the applicability of the methodology in laboratories with basic molecular equipment .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"giardiasis",
"neglected",
"tropical",
"diseases",
"biology",
"microbiology",
"parasitic",
"diseases",
"parasitology"
] |
2012
|
Detection of Giardia duodenalis Assemblages A and B in Human Feces by Simple, Assemblage-Specific PCR Assays
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The thick-tufted layer 5b pyramidal cell extends its dendritic tree to all six layers of the mammalian neocortex and serves as a major building block for the cortical column . L5b pyramidal cells have been the subject of extensive experimental and modeling studies , yet conductance-based models of these cells that faithfully reproduce both their perisomatic Na+-spiking behavior as well as key dendritic active properties , including Ca2+ spikes and back-propagating action potentials , are still lacking . Based on a large body of experimental recordings from both the soma and dendrites of L5b pyramidal cells in adult rats , we characterized key features of the somatic and dendritic firing and quantified their statistics . We used these features to constrain the density of a set of ion channels over the soma and dendritic surface via multi-objective optimization with an evolutionary algorithm , thus generating a set of detailed conductance-based models that faithfully replicate the back-propagating action potential activated Ca2+ spike firing and the perisomatic firing response to current steps , as well as the experimental variability of the properties . Furthermore , we show a useful way to analyze model parameters with our sets of models , which enabled us to identify some of the mechanisms responsible for the dynamic properties of L5b pyramidal cells as well as mechanisms that are sensitive to morphological changes . This automated framework can be used to develop a database of faithful models for other neuron types . The models we present provide several experimentally-testable predictions and can serve as a powerful tool for theoretical investigations of the contribution of single-cell dynamics to network activity and its computational capabilities .
Neocortical pyramidal cells in layer 5 ( L5 PCs ) are important input-output units in the cortical column . Their dendrites span the entire column , thus receiving input from all six layers , and the cells provide the major output from the column to various parts of the brain . These cells are divided into two main classes that differ in dendritic morphology , electrical properties , axonal projections that they typically make [1]–[2] , thalamocortical input they receive [3] , and location of their soma within layer 5 . L5b PCs are pyramidal cells of the deeper part of the layer ( L5b ) . These thick-tufted cells project to subcortical targets such as the tectum , brainstem and spinal cord , and they tend to discharge a short burst of spikes in the beginning of a spike train . By contrast , the thin-tufted pyramidal cells of the superficial part of the layer ( L5a ) discharge spikes with no adaptation , and project to other parts of the cortex [4] . Due to the large diameter of their apical dendrites , L5b PCs are readily available for intracellular dendritic recordings and as such they have been extensively studied over the past few decades . Previous works characterized numerous active properties of the cells apical dendrites [5]–[8] and recently also the basal dendrites [9]–[11] , as well as the ionic currents involved and partially also the spatial distribution of the underlying ion channels over the dendritic surface [12]–[13] . Such active dendritic properties are suggested to play a key role in information processing [14] , non-linear computations [15]–[16] and synaptic integration [17]–[18] . Recent experiments have also highlighted the impact of L5 PCs on sensation and action in anaesthetized [19] and in behaving [20] animals . The key active properties of L5b PCs involve two main spiking zones . Na+ action potentials ( APs ) are initiated at the perisomatic region with a typical frequency-current ( f–I ) relation and firing response to a prolonged suprathreshold step current ( perisomatic step current firing ) [4] . The second spiking zone is located at the distal apical dendrites [7] , [21]–[22] , where Ca2+ spikes are generated in response to an intense dendritic [22] or somatic [23] stimulation in vitro . Recent in vivo studies demonstrate a correlation between dendritic Ca2+ signals and sensation [24] or wakefulness [25] . Importantly , in vitro studies have demonstrated that the two spiking zones interact with each other , whereby the coincidence of the back-propagating action potential ( BAP ) and local excitatory postsynaptic potential ( EPSP ) at the distal dendrites triggers a dendritic Ca2+ spike , which in turn triggers one or more additional perisomatic Na+ APs [6] . This BAP-activated Ca2+ spike ( BAC ) firing highlights the interplay between the two spiking zones , and may be involved in coincidence detection of EPSPs and APs [26] , and in spike timing-dependent plasticity [27]–[28] . While there are various conductance-based models of L5b PCs , they are either hand-tailored to capture only a few particular properties of these cells , such as the back propagation of action potentials [29] or the effect of Ih on input integration [13] , or they are meant to explore a general idea rather than faithfully replicate specific dynamical properties [30] . A notable existing model was developed by Schaefer et al . [31] and is capable of replicating the BAC firing; however it does not produce the typical f–I curve and perisomatic step current firing behavior of these cells . Kole et al . [32] developed a model that fits the shape of a single spike at the axon , soma and dendrites , but is incapable of generating apical Ca2+ spikes and does not capture the f–I curve or perisomatic step current firing . Another significant modeling effort did manage to reproduce some features of the response to suprathreshold current steps recorded at the soma and apical dendrites [33] , or only features of the Na+ excitability in the apical dendrites [34] . However , these models do not attempt to reproduce dendritic Ca2+ spikes or BAC firing , and the relevant channels are missing from the apical dendrites . Presently , there is still no model for L5b PCs that faithfully replicates both of the two basic firing behaviors , one at the soma and the other at the apical dendrite ( but see an important effort in this direction for hippocampal CA1 PCs [35] ) . In this work we extended our theoretical framework [36] of utilizing an automated fitting method , the multi-objective optimization ( MOO ) algorithm combined with an evolutionary algorithm , to constrain the features of the firing and shape of spikes ( for a review on automated fitting of models to experiments , see [37]–[38] ) . We targeted local spikes both in the soma and distal apical tree , as well as the interaction between the two zones . We arrived at models that faithfully capture the main active properties of mature L5b PCs , including their experimental variability , as quantified by feature statistics over this cell type . We also demonstrate that an inspection of parameter value ranges in our sets of models provides useful insights into the key ionic conductances that underlie the active properties of L5b PCs , as well as mechanisms that are sensitive to morphological changes . We propose that our MOO framework can be used to extend these models for both L5b PCs as well as other neuron types when further experimental data becomes available . Eventually , this approach will provide a database of neuron models that capture the key properties of all neuron types , including their experimental variability . Our realistic models can serve as a powerful tool for theoretical investigations of the contribution of single-cell dynamics to the overall network dynamics and its emergent computational capabilities .
We defined the features of the perisomatic and dendritic firing behaviors that we intended our models to replicate ( the “target” firing behaviors ) and quantified their mean and standard deviation ( SD , see Table 1 ) using experimental voltage traces recorded in several mature L5b PCs or data reported in the experimental literature ( see Methods ) . For the target of perisomatic step current firing , we used ten somatic features of the average response to three normalized ( see Methods ) current step amplitudes ( Table 1 , leftmost four columns ) . Evidently , some perisomatic firing features such as the AP half-width , AP peak and inter-spike interval ( ISI ) adaptation did not differ significantly over different step amplitudes , in contrast to features such as the first spike latency and ISI-CV for the spike train . With increasing current , the spike train became more regular ( as quantified by the ISI-CV feature ) , the latency of the first spike decreased and the initial burst's ISI became less variable ( see also [4] ) . For the target of BAC firing we used ten dendritic and somatic firing features ( Table 1 , rightmost two columns ) . The experimental traces indicated a rather robust Ca2+ spike height and width during BAC firing , and a more variable ISI for the resulting burst of perisomatic Na+ APs . The experimentally observed amplitude and variability of the BAP at two distal apical locations also served to constrain the model BAC firing . We first fitted , separately , either the BAC firing target or the perisomatic step current firing target and explored their respective conductance mechanisms . We selected key conductance mechanisms found in L5b PCs or generally in neocortical neurons [39] and well-characterized experimentally ( see Methods ) . In optimizing either target , we used the same set of 22 free parameters ( Table 2 ) , primarily the densities of the conductance mechanisms . The density of Ih conductance was not a free parameter but rather distributed similarly in all optimizations based on previous studies ( see Methods ) . This distribution of Ih ensured that all our models exhibited key subthreshold properties of L5b PCs such as Ih related effects on EPSP summation [40]–[43] , as well as a resting membrane potential gradient along the apical tree , with a slope of 10 mV/mm [7] , [13] . We first constrained models only by features of the BAC firing ( Table 1 , rightmost two columns ) , generating a set of 899 acceptable models ( see definition in Methods ) . Figure 1 depicts the firing behavior of one example model from the set . An EPSP-like current with 0 . 5 ms rise time , 5 ms decay time and amplitude of 0 . 5 nA injected at the model distal apical dendrites ( 620 µm away from the soma ) resulted in a local EPSP of 14 mV and a somatic EPSP of 2 . 5 mV ( Figure 1B ) . A brief 5 ms , 1 . 9 nA suprathreshold current injected in the model soma yielded an AP that back-propagated to the dendrites ( Figure 1C ) , decaying to yield the BAP amplitude at the main bifurcation which is within the experimental range [44] . When both somatic and dendritic stimuli coincided within 5 ms , the model neuron generated BAC firing with a large and broad Ca2+ spike followed by a burst of two additional somatic Na+ APs ( Figure 1D ) . Intense stimulation of the distal dendrites alone ( Figure 1E ) was sufficient to produce a dendritic Ca2+ spike and two somatic Na+ APs . These behaviors are in full agreement with the experimental literature [6] . The model Ca2+ spike peak and width were within 0 . 86 SD from the experimental mean; the BAP amplitudes were within 1 . 4 SD from the experimental mean; and the perisomatic AP ISI was within 1 . 1 SD from the experimental mean . Models in the set had feature values ranging within 2–3 SD ( our designated cut-off for acceptable models ) from the experimental mean , thus exhibiting the experimental variability . Importantly , acceptable models that were constrained to only replicate the BAC firing were not guaranteed to faithfully replicate the firing response to prolonged somatic depolarizing current step ( Figure 1F ) . In the example model described above , the frequency of Na+ APs at the soma was too low ( Figure 1F , top ) and the shape of individual spikes within the train did not resemble that of the experimental spikes ( Figure 1F , bottom ) . Hence , fitting the BAC firing target behavior did not ensure good performance on our other target behavior ( see also [45] ) . Next , we constrained models only by features of the perisomatic step current firing ( Table 1 , leftmost four columns ) , and arrived at a second set of 52 acceptable models . For an example model from the set , a qualitative comparison of the experimental and simulated model response to depolarizing current step is shown in Figure 2A , demonstrating the similarity in spike train features ( frequency , latency , initial burst , ISI-CV and adaptation index ) and spike shape features ( spike height , after-hyperpolarization , and spike width ) . The values of all features in that model were within 1–2 SD from the experimental mean , except for the first spike latency that was within 3 SD from the experimental mean . In addition , the whole f–I curve of the model fell within the range of the experimental f–I curves ( Figure 2B ) , demonstrating that matching model parameters to only three selected points in the f–I curve was sufficient to constrain the entire f–I curve . Models in the set had feature values ranging within 2–3 SD from the experimental mean , thus exhibiting the experimental variability . As expected , these models were not guaranteed to capture the active dendritic properties , despite having the same free parameters on the apical dendrites as those used for fitting BAC firing ( Figure 1 ) . In the example model shown in Figure 2 , dendrites were only weakly excitable , resulting in a strongly attenuated , essentially passive , BAP ( Figure 2C , top ) , and failure to produce a Ca2+ spike even under intense distal apical stimulation ( Figure 2C , bottom ) . To highlight mechanisms that provide acceptable models for BAC firing or for perisomatic step current firing , we compared the range of each model parameter in the two corresponding sets of models ( Figure 3 ) . Evidently , the ranges of most apical parameters ( shaded area in Figure 3 ) were markedly smaller when constraining for BAC firing alone ( Figure 3 , red lines ) than when constraining only for perisomatic step current firing ( Figure 3 , black lines ) . Thus , faithful models for BAC firing required the density of Ca2+ , Na+ and K+ ion channels in the apical tree , as well as the parameters of Ca2+ dynamics , γ and τdecay ( see Methods ) , to be within a rather tight range ( Figure 3 , red lines in shaded area ) . For example , the range for the density of apical Nat was 101–133 . 5 pS/µm2 when constraining for BAC firing ( Figure 3 , red lines ) , and 51 . 5–200 pS/µm2 ( almost spanning the entire free parameter range ) when not constraining for BAC firing ( Figure 3 , black lines ) . The ranges of Im , Kv3 . 1 , CaHVA , CaLVA , SK , γ and τdecay in models for BAC firing were 0–1 . 155 , 0–3 . 05 , 0–8 . 65 , 0–211 , 25–29 . 5 pS/µm2 , 5e-4–5 . 85e-4 and 24 . 6–175 ms , respectively . Conversely , in models for perisomatic step current firing the ranges of somatic parameters such as CaHVA density , Ca2+ dynamics γ and τdecay were smaller , and the ranges of SK and Nap densities were shifted , compared to models for BAC firing . Other somatic parameter ranges in both sets of models were similar ( see Discussion ) . We note that the range of acceptable density of apical Nat given above agrees with previous experiments [12] , and that the densities of the other dendritic channels in acceptable BAC firing models are in agreement with previous theoretical estimates [30]–[31] . In order to understand why the models shown in Figures 1 and 2 failed in the targets they were not constrained with , we related the particular parameter values of a model for one target to the ranges delineated by the set of acceptable models for the other target . The red and black circles in Figure 3 correspond to the normalized parameter values of the models shown in Figures 1 and 2 , respectively . First , we looked for dendritic parameter values of the model for perisomatic step current firing ( Figure 3 , black circles ) that were outside the parameter ranges delineated by the set of acceptable models for BAC firing ( Figure 3 , red ranges ) , and therefore might underlie the failure of the model to generate a Ca2+ spike or properly back-propagate APs ( Figure 2C ) . For example , the model's apical Nat density ( 82 . 5 pS/µm2 ) was below the acceptable range for BAC firing ( Figure 3 , red range , corresponding to 101–133 . 5 pS/µm2 ) , and its apical Kv3 . 1 density ( 119 pS/µm2 ) was above the acceptable range ( 0–3 . 05 pS/µm2 ) . Either of the deviant densities was therefore likely to be the reason for the model's failure to properly back-propagate APs to the apical tree [12] . We verified this hypothesis either by reducing the apical Nat density in the acceptable model for BAC firing ( Figure 3 , red circles , corresponding to 105 . 5 pS/µm2 ) to this low value , which resulted in a similar failure to back-propagate APs; or by increasing the value of this channel density in the model for perisomatic step current firing to acceptable values ( Figure 3 , red range ) , which resulted in a more acceptable BAP ( not shown ) . Several parameters may underlie the failure of the model to generate a Ca2+ spike . Its apical Im and Kv3 . 1 densities ( 3 . 9 and 119 pS/µm2 , respectively ) as well as the apical γ value ( 7 . 59e-3 ) were above the acceptable ranges for BAC firing ( 0–1 . 155 , 0–3 . 05 pS/µm2 , and 5 . 00e-4-5 . 85e-4 , respectively ) . We tested this hypothesis by shifting the values of these three parameters in the model to those of the acceptable model for BAC firing , and indeed observed Ca2+ spike under distal apical stimulation ( not shown ) as in models for BAC firing ( Figure 1E ) . A similar examination of the parameter values of the model for BAC firing ( Figure 3 , red circles ) showed that the model had apical Im and Kv3 . 1 densities ( 0 . 3 and 1 . 49 pS/µm2 , respectively ) below the acceptable ranges for perisomatic current step firing ( Figure 3 , black ranges , corresponding to 0 . 93–5 and 36 . 2–200 pS/µm2 , respectively ) , and somatic Ca2+ dynamics τdecay ( 486 ms ) above the acceptable range ( 197–395 ms ) . These aberrant values may have increased the somatic and dendritic excitability as suggested by the bursts seen in the response to somatic step current ( Figure 1F ) . We tested the model when shifting the apical Kv3 . 1 and Im densities and somatic τdecay to the values of the acceptable model for perisomatic step current firing ( Figure 3 , black circles ) and managed to significantly improve the response ( not shown ) . We found further evidence that active dendritic mechanisms did not play a significant role in perisomatic step current firing , by comparing somatic parameters in our models for this target behavior to models that optimized the same target but without the free apical parameters . The dendrites in the latter models were therefore essentially passive , having only Ih and leak conductance . We found that the somatic parameter ranges in both sets of models were quite similar ( Figure S1 ) , with no range shifts and only a relatively moderate change in range size for Nap and Kv3 . 1 densities ( see Discussion ) . Having successfully fitted the perisomatic step current firing and BAC firing features separately , we next attempted to do the same for their conjunction . Trying to simultaneously fit all 20 features for the two targets ( Table 1 ) in a single MOO with high resources for the evolutionary algorithm ( population size of 5000 and 2000 generations ) did not yield satisfactory models for both targets , but rather generated models with large errors in one or several key features . This was to be expected with the large number of objectives , which requires a much larger population for convergence [46] ( see Discussion ) . We therefore conducted the optimization process in two stages . The first stage was the fitting of BAC firing target , which resulted in the set of acceptable models shown in Figure 3 ( red lines ) . We then tested this set of models on the features of perisomatic step current firing . Some models performed poorly , as demonstrated in Figure 1 , whereas others performed somewhat better , although not as satisfactorily as the acceptable model for perisomatic step current firing shown in Figure 2 . We thus selected the one acceptable model for BAC firing that performed best on the other target behavior as well , and used its specific dendritic parameter values in a new MOO on the perisomatic parameters alone , now constraining with both target behaviors . The fixation of dendritic parameters was based on the assumption that acceptable apical conductance densities do not influence the perisomatic step current firing significantly , as suggested in Figure S1 . Indeed , this two-step method resulted in a successful fit , yielding acceptable models that faithfully reproduced the features of both target behaviors . An example of such a model is shown in Figure 4 . This model was selected for having feature values closest to the experimental mean . The BAC firing that it produced ( Figure 4A ) was similar to that of the models exemplified in Figure 1 , and the values of all BAC firing features were within 1–2 SD from the experimental mean , except that the average AP ISI was slightly longer ( 15 ms , ∼4 SD from the experimental mean ) . However , since we observed such a value in the experimental recordings data , we considered it acceptable . The model faithfully reproduced all the perisomatic step current firing features ( Figure 4B ) to within 1–2 SD from the experimental mean , except that its initial burst response was slightly stronger ( comprising 3–4 spikes instead of 2–3 ) . The model's f–I curve was within the experimental range ( Figure 4C ) . Models in the set had feature values ranging within 2–3 SD from the experimental mean , thus exhibiting the experimental variability . The f–I curve of the resulting models did , however , saturate at lower frequencies than average , so that the models captured the f–I curve of cells from the margins of our experimental set ( Figure 4C ) . To explore the reason for this , we compared the somatic parameter ranges in the set of models for both BAC firing and perisomatic step current firing to the set of models fitting only perisomatic step current firing ( Figure 4D ) . Apart from several decreases in range size ( Nap , Nat , Kp , and Kv3 . 1 densities ) , which may be attributed to the small data set of dually-constrained models , we noticed a marked increase in intracellular Ca2+ dynamics τdecay ( 363–616 vs . 197–395 ms ) in models that were capable of producing acceptable BAC firing , possibly due to the constraint of three APs in the perisomatic burst response during BAC firing ( see Methods ) . τdecay seems likely to underlie the stronger saturation of f–I curve since it is active on long time scales . We tested this hypothesis by lowering the τdecay value in the model ( 460 ms ) to be within the range of models for perisomatic step current firing alone ( 300 ms ) . We found that the f–I curve of the modified model indeed shifted to lie on the average ( Figure S2 , top ) . However , as expected , this modified model produced only two perisomatic APs in BAC firing ( Figure S2 , bottom ) . Hence , the constraint of three APs during BAC firing is likely to have clashed with the f–I curve constraint . We suggest that a more complex Ca2+ dynamics mechanism might improve the fit ( see Discussion ) . The model shown in Figure 4 had a membrane time constant of 10 ms , and we measured the input resistance at the soma to be 41 . 9 MΩ . Both values are within the experimental range [4] . The parameter values of that model are given in Table 3 , and parameter values of the three additional acceptable models that agree with both targets are given in Table S1 . As an indication that our model densities are biologically plausible , we observed that the dendritic Nat density ( 107 pS/µm2 ) and the measured peak somatic Nat current ( 50pA/µm2 ) are in the same order of magnitude of experimental estimates [12] , [32] . As a demonstration that a feasible Ih is present in the apical dendrites , we verified that the model exhibits experimental findings [47] regarding the effect of Ih in attenuation of voltage along the dendrites ( Figure S3 ) . The slope of the curve when Ih is blocked ( Figure S3B ) is slightly less steep than seen experimentally , either due to the difference in dendritic morphology or the difference between a complete blocking of Ih in simulation compared to the limited extent of pharmacological blockade experimentally . We examined how well the dually-constrained model ( shown in Figure 4 ) performs on a target behavior with which it was not explicitly constrained . Larkum et al . [23] have shown that when a train of APs is generated at the soma by a series of brief somatic pulses there is a critical frequency of somatic APs whereby the summated BAPs in the distal apical dendrites reach threshold for a regenerative dendritic Ca2+ spike . This critical frequency ranges in different L5b PCs from 50 Hz up to 200 Hz , with an average around 100 Hz . In Figure 5 we replicated this experiment by injecting a train of five brief suprathreshold current pulses to the soma of the dually-constrained model depicted in Figure 4 . At frequencies below 100 Hz ( Figure 5A , left ) a dendritic Ca2+ spike was not elicited , whereas above 100 Hz a dendritic Ca2+ spike was generated ( Figure 5A , right; and Figure 5B ) , in close agreement with the experimental results . This result strengthens our confidence in the model , as its good performance generalizes to novel experimental stimuli . Next , we investigated the influence of the dendritic morphology on the results that we obtained for cell #1 ( shown in Figure 1A ) . We selected two other L5b PC morphologies of the same age . One cell ( cell #2 ) was generally similar to cell #1 , while the other cell ( cell #3 ) was more different than cell #1 , in terms of input resistance ( Rin ) and dendrite-to-soma conductance ratio [48] ( ρ or “dendritic load” ) , or in the distance between the main apical bifurcation and the soma . First , we used cell #2 ( Figure 6A ) with the same parameters ( Table 3 ) as in the model shown in Figure 4 . The main bifurcation in cell #2 was slightly more distal as compared to cell #1 ( 750 vs . 650 µm ) although within the Ca2+ “hot” zone of cell #1; The Rin of cell #2 was smaller by 18% ( 34 . 4 vs . 41 . 9 MΩ ) and its ρ was larger by 9% ( 16 . 1 vs . 14 . 7 ) . The perisomatic firing response to step current ( with amplitudes adjusted to the new Rin ) was acceptable and similar to that observed with the original morphology ( Figure 6B ) . However , the dendritic tree was evidently more excitable , producing BAC firing upon a brief suprathreshold stimulation of the soma alone ( Figure 6C ) . As is evident in that figure , the BAP amplitude at the distal apical dendrites was quite large in cell #2 as compared to cell #1 ( Figure 1C ) , and was sufficient to trigger a Ca2+ spike ( and consequently BAC firing ) . Based on previous studies [49]–[50] we measured the transfer impedance between the soma and the apical dendrite “hot” zone ( 700 µm away from the soma ) in cells #1 and #2 . For steady state , the transfer resistance was similar in the two cells ( 13 . 1 vs . 12 . 5 MΩ ) , however for a brief ( 5 ms ) somatic pulse ( corresponding to the duration of an AP ) the transfer impedance was 1 . 4 times larger in cell #2 ( 1 . 714 vs . 1 . 2 MΩ ) , which explains why the somatic AP was less attenuated in cell #2 compared to cell #1 . We were interested in discovering ionic currents that compensate for the difference in transfer impedance . We therefore repeated the MOO fitting procedure of the BAC firing using the second morphology . We maintained the apical “hot” zone of Ca2+ channels at the same distance from the soma as for cell #1 ( see Methods ) , since the main bifurcation differed only by 100 µm and was within the “hot” zone . We thus generated acceptable models for BAC firing in cell #2 , and compared the ranges of their parameters to the set of acceptable models for cell #1 ( Figure 6E ) . We found that acceptable models for cell #2 had a strictly lower apical dendritic Nat density as compared to acceptable models for cell #1 ( 84–95 . 5 vs . 101–133 . 5 pS/µm2 ) . This finding is in agreement with a previous study [50] that showed an inverse correlation between dendritic transfer impedance and the apical Nat density required for the active BAP seen experimentally . Hence , when we used the parameters of the model shown in Figure 4 with cell #2 , the apical Nat was too high for that morphology to faithfully replicate BAC firing . In addition , we checked if the successful transfer of somatic parameters between the two cells in terms of perisomatic step current firing was reflected in a similarity of somatic parameter ranges when cell #2 was fitted anew . We therefore repeated the MOO fitting procedure of the perisomatic step current firing using the second morphology . We used only leak and Ih conductance in the dendrites ( as in generating the set of models for cell #1 shown in Figure S1 , purple lines ) to facilitate the comparison , and adjusted the step amplitudes according to Rin . We thus generated acceptable models for perisomatic step current firing in cell #2 . We found a tight overlap between their somatic parameter ranges and those of the corresponding models for cell #1 ( Figure 6D ) , explaining the successful transfer from the cell #1 model to cell #2 in the case of perisomatic target behavior . The extreme effect of morphology on model performance is demonstrated by a third morphology , that of cell #3 ( Figure 7A ) . Its main bifurcation was only 200 µm from the soma , its Rin was less than half that of cell #1 ( 18 . 8 vs . 41 . 9 MΩ ) and its ρ was nearly 50% larger ( 20 . 2 vs . 14 . 7 ) . When simulated using the parameters of the dually-constrained model for cell #1 shown in Figure 4 , it fired incorrectly in response to somatic step current ( Figure 7B ) . We observed similar poor transfer even for models with only leak and Ih conductance in the dendrites , which implied that the difference in the dendritic load underlies the poor transfer of parameters between the two modeled cells . While fitting anew for the perisomatic step current firing using cell #3 was successful ( Figure 7C ) , acceptable models using cell #3 differed markedly from acceptable models that used cell #1 , in that Nat and Kv3 . 1 density ranges were shifted upwards ( Figure 7D , 23 , 500–32 , 300 vs . 17 , 600–26 , 600 , and 3 , 540–12 , 200 vs . 2 , 410–5 , 060 pS/µm2 , respectively ) . Hence , these two conductances are likely to be important in adjusting for the dendritic load difference . A parameter range comparison between models for BAC firing was not possible , however , since the optimization of BAC firing with cell #3 did not converge well , regardless of whether assigning the apical Ca2+ channels high density using the distance rule ( see Methods ) or starting at the main bifurcation . This result suggests that there is a different channel distribution or BAC firing behavior for morphologies with such proximal main bifurcation . Overall , these results demonstrate some of the limitations of transferring models across L5b PC morphologies based on our current knowledge ( see Discussion ) . We used our model to test the expected effect of the Up state as seen in vivo [51]–[52] on the BAC firing . Previous experimental studies [53] explored some of the properties of dendritic Ca2+ spikes and perisomatic Na+ spikes under noisy “high conductance” state . We emulated the Up state simply by applying a DC current of 0 . 42 nA for 200 ms at the proximal apical dendrite , 200 µm from the soma . Under this condition , a brief square pulse ( 5 ms , 0 . 5 nA ) at the soma resulted in a perisomatic Na+ AP ( Figure 8A ) . An EPSP-like current , similar to that used in our BAC firing simulations ( 0 . 5 nA ) , injected at the distal apical dendrite 700 µm from the soma ( Figure 8B ) resulted in a perisomatic Na+ AP , due to the proximity of the membrane potential to firing threshold in the Up state . When the EPSP input preceded or followed the somatic stimulus by 20 ms ( Figure 8C and Figure 8E , respectively ) the model cell fired two Na+ APs , with no spikes at the dendrite . However , when the two inputs were applied simultaneously , the cell BAC fired , with a burst of four additional Na+ APs as well as Ca2+ spike at the dendrite ( Figure 8D ) . We then determined the temporal window conducive to the generation of Ca2+ spike , by plotting the peak distal dendritic membrane potential as a function of the stimulus time difference ( Δt ) in the Up state and the Down state ( without the DC current , Figure 8F ) . The temporal window for the generation of the BAC firing was much narrower in the Up state ( Δt = 0–5 ms , Figure 8F black trace ) compared to the Down state ( Δt = −10–15 ms , Figure 8F blue trace ) . Ca2+ spike peak was smaller in the Up state , in agreement with previous experimental studies [53] . Interesting also is the larger gain in the Up state ( a larger number of additional BAC firing-related spikes ) in the Up state compared to the Down state ( see Figure 4 ) . Therefore , our simulations predict an increase in temporal sensitivity for BAP and EPSP coincidence , as well as an increase in perisomatic AP gain during the Up state ( see Discussion ) .
To the best of our knowledge , this is the first modeling study for any neuron type that utilizes an automated feature-based parameter search to faithfully replicate both dendritic Ca2+ and perisomatic Na+ electrogenesis and the interaction between these two spiking regions . In this study we modeled mature L5b PCs , focusing on the firing of Na+ APs at the soma in response to a prolonged step current , the generation of a Ca2+ spike at the distal apical dendrites , as well as the interaction between the two spiking zones via the BAC firing . To characterize these target behaviors , we used a total of 20 experimentally-based features and their experimental variability ( Table 1 ) . As a result , for a given modeled L5b PC morphology this study provides a set of acceptable models ( and consequently a range of model parameters ) that faithfully replicate the target experimental results , as well as exhibiting the experimental variability . Importantly , experimental studies also show that numerous combinations of ion channel densities can result in similar firing behavior [54] . Our sets of models can be used in further analyses that examine the interplay between channel density combinations . Dendritic Ih distribution in the models based on previous studies ensured replication of Ih-related subthreshold input integration properties ( e . g . , Figure S3 ) . The faithful performance of the models we present also generalized to stimuli with which they were not constrained ( Figure 5 ) , and the models perisomatic and dendritic maximal conductances are in the same order of magnitude as experimental estimates . Previous modeling studies of L5b PCs replicated only some aspects of the cell's behavior ( e . g . , BAC firing but not perisomatic step current firing [31] , or vice versa [33] ) . Thus , our work provides a set of models faithfully replicating a range of important active properties of a key neuron in the mammalian neocortex , which can serve as a basic building block for in silico models of large-scale cortical networks . In order to pinpoint key mechanisms that underlie the multi-regional firing properties of L5b PCs , we compared the range of parameters for models that optimized either of the two target behaviors . We sought parameters that differed between the two targets either in range size or in range values . Such differences provided a first order , readily-observable indication of the change in the role of a given ion channel and therefore hinted at its relevance to the target behavior . Also of interest were cases where the range of a parameter values included zero , indicating that this ion channel may be replaced by a combination of other ion channels for achieving the target behavior . Such comparisons provided a clear delineation of the range of apical Nat and Kv3 . 1 density values required for a proper active back-propagation of APs , and also highlighted the K+ and Ca2+ related mechanisms affecting dendritic capability to generate local Ca2+ spikes ( Figure 3 ) . In particular , Im and Kv3 . 1 may act directly to counter the regenerative effect of Ca2+ and Na+ currents , whereas Ca2+ dynamics γ may act by increasing the free Ca2+ concentration for a given Ca2+ current , thus triggering a larger SK current which dampens the local Ca2+ spike . Through similar analysis we differentiated between somatic mechanisms that are primarily involved in shaping the AP , and somatic mechanisms that are more involved in features of the AP train . Ranges of values for parameters of somatic mechanisms with long time constants , such as SK , CaHVA and Nap currents , and Ca2+ dynamics γ and τdecay , differed between the two sets of models . We verified that these mechanisms did indeed play a role in features of somatic spike trains such as adaptation . Such features were not sufficiently constrained by the BAC firing , which lasts only tens of milliseconds . By contrast , somatic parameter ranges that were similar in value were therefore likely to contribute to features of the perisomatic spike shape , which were constrained in both of our target behaviors . Additionally , we highlighted the role of somatic Nat density in compensating for changes in the dendritic load on perisomatic excitability in different L5b PC morphologies ( Figure 7 ) , as well as the role of dendritic Nat density in compensating for changes in the transfer impedance between soma and apical dendrite in different L5b PC morphologies ( Figure 6 ) . We also found that active dendritic mechanisms do not seem to play a significant role in perisomatic step current firing ( Figures 3 , S1 ) , except perhaps in contributing to the steady excitability involving the balance between persistent conductances such as dendritic Kv3 . 1 , and somatic Nap and Kv3 . 1 ( Figure S1 ) . The range of values for perisomatic NaT density in our models is somewhat higher than in several other estimates [32] , [55] . In addition to the limitations of the experimental methods used for estimating ion channel density , which may underestimate the true density ( as indicated by [32] ) , several reasons can account for the higher NaT density used in our models . Some experimental estimates were based on younger preparations [55] , where the cells ( and thus the “dendritic load” ) are considerably smaller than in mature cells [56] . Thus , models for younger cells require smaller density of perisomatic NaT for generating “healthy” somatic Na+ spikes ( as also suggested by Figure 7 ) . Other estimates are based on model-fitting of only a single-spike in response to a brief current pulse [32] . Interestingly , we could also fit a single spike response using lower NaT density ( ∼4 , 000 pS/µm2 , not shown ) . However , a higher density was required to fit prolonged train of spikes . This is mostly due to the inactivation of Na+ channels during the plateau depolarization seen during firing response to prolonged current step . Our perisomatic NaT density values agree with previous modeling studies of current step firing [30] , [33]–[34] . Another critical factor that affects the estimates of NaT channels density is the kinetics of the activation/inactivation of the channels . The estimates for kinetics parameters such as V½ values differ considerably in different studies [32] , [55] , [57] , by 5–15 mV on average . A shift in V½ to more hyperpolarized values compensates for reduced channel density [29] . We therefore verified that the peak Na+ current measured at the soma in our models agrees with experimental estimates [32] . We also note that in the axonal model ( Figure S2 ) , the ratio of axonal to somatic NaT densities agrees with recent experimental estimates [58] . In agreement with previous studies , we found that constraining one spiking zone did not guarantee the constraining of the other spiking zone [45] . In addition , the dissociation between the perisomatic AP features and the dendritic conductance mechanisms that underlie dendritic BAP is in agreement with previous experimental studies that blocked dendritic Na+ channels and showed no significant change in features of perisomatic APs in L5b PCs [12] . Previous modeling studies [47] suggest a non-uniform specific membrane resistance ( Rm ) in the apical dendrites . In our study we used a uniform distribution . However , we verified that our models retain the faithful replication of the features in both soma and dendrites also for the case when Rm is spatially non-uniform . Specifically , we simulated the model shown in Figure 4 , using the sigmoid function for Rm , with Rm ( soma ) = 34 , 963 , Rm ( end ) = 5357 , dhalf = 406 µm , steep = 50 µm , with a factor of 1 . 16 to maintain the overall spatial integral of the leak conductance density as in our original model . In this case , in order to retain the resting membrane potential in the dendrites and the soma , we found it necessary to adjust Eleak across the apical dendrites , with Eleak ( soma ) = −90 mV , Eleak ( end ) = −80 mV , with similar dhalf and slope as of Rm . In this study we did not attempt to fit for local dendritic Na+ spikes [5] , [10] , primarily to lessen the load on the optimization algorithm since their occurrence and function in the context of network activity in the intact brain seems to be minor [59] relative to Ca2+ spikes . Nor did we fit for active BAP in the basal dendrites since it does not differ substantially from passive propagation [10] . We also did not fit for dendritic NMDA spikes [8]–[9] , [11] since , although they are an important signal , there is currently insufficient data with regard to the distribution and density of NMDA receptors . Once sufficient experimental data is available , the models presented hereby could serve as a scaffold on which NMDA spikes can be fitted for , using a similar automated optimization framework , since NMDA spikes do not depend significantly on voltage gated channels [9] . In the present study , the optimization parameters were the densities of the different ion channels . Our evolutionary algorithm framework can be used for exploring the effect of the kinetics of the different ion channels [60] as well as of different spatial distribution of ion channels in the soma-dendritic surface . Similarly , the powerful search algorithm can be used to refine our selected set of conductance mechanisms , e . g . when results imply that a certain mechanism is not necessary for fitting any of the targets , or that different mechanisms are required to improve the fit . For example , future optimization studies will benefit from a better model of intracellular Ca2+ dynamics , one that would account for a saturating buffer and a saturating pump . To our knowledge , a well-constrained model of such complexity does not yet exist . One major shortcoming of the simplistic and widely used model for Ca2+ dynamics that we employed in this study is that it acts to reduce intracellular Ca2+ concentration to a similar degree regardless of the concentration level . It therefore either acts strongly or weakly during both intense and weak stimuli . Our study demonstrates the limitations in transferring an existing model to different morphologies . Even within the same general class ( in our case , L5b PCs ) , different dendritic morphologies can have significant differences in the degree of electrical coupling between the two active zones ( Figure 6 , and see also [50] , [61] ) as well as differences in the dendrite-to-soma conductance ratio . Interestingly , when the dendrite-to-soma conductance ratio differs significantly , parameters that enable the fitting of perisomatic firing features in one cell fail to fit this target behavior in the other cell ( Figure 7 ) . Further parameter range comparison and more systematic investigations are needed to elucidate morphological effects on transferring models across morphologies . This will help in defining general rules for constructing generic models for a particular neuron type that are invariant to their morphological differences . Previous in vitro studies [53] indicated invariance of the time window between BAP and dendritic EPSP conducive to BAC firing when comparing silent slice conditions ( which can be considered a Down state ) to when the cell is bombarded by intense dendritic input . Their suggested time window is ∼30 ms , in agreement with our simulation results for the Down state ( ∼25 ms ) . However , our model predicts a narrower time window ( 5 ms ) during an Up state , a condition observed in vivo [51]–[52] . In our model , threshold for Ca2+ spike generation remained fixed in both our Up and Down states; therefore the reason for narrowing of the time window for BAC firing in the Up state is the reduction in the peak BAP recorded at the distal dendrite ( reduction of ∼15 mV ) , due to inactivation of dendritic Na+ channels as a result of the DC depolarization . Our results thus complement the previous findings and suggest an enhanced sensitivity for input coincidence associated with increase in the BAC-firing related input-output gain–a prediction , which we hope , will be examined experimentally soon . With the current computational resources in most neurobiology departments , multi-objective evolutionary algorithms of the kind we have used [62] are limited to fitting only a few objectives [46] . Optimization convergence seems to be further compromised when the objectives involve several highly nonlinear active zones that interact strongly with each other . For this reason , our method of fitting in stages ( first the dendritic spiking zone , and then both spiking zones , Figure 4 ) can be useful for further optimization studies . It will be worthwhile to examine other MOO algorithms that may be better suited for a large number of objectives [63]–[64] . The sets of parameter and feature error values for all ( ∼2 , 000 ) models reported in this work are available in ModelDB [65] , along with the relevant NEURON code ( accession number 139653 ) .
We extended the algorithm described previously [36] . Briefly , statistics of electrophysiological features such as spike frequency , spike width , and adaptation index were grouped into multiple objectives and fitted to a detailed conductance-based model of a reconstructed L5b PC by an elitist non-dominated sorting evolutionary algorithm [62] . The free parameters in the optimization were primarily the density of a set of nine predefined ion channels ( see below and Figures S4 , S5 ) located in the soma and in the dendrites ( Table 2 ) . We divided the detailed reconstructed morphologies into compartments , each at most 20 µm long , resulting in an average of about 200 compartments per model cell . The algorithmic optimization and all simulations were conducted in NEURON [66] . For the evolutionary algorithm we used a population size of 1000 and 500 generations , running either on a grid of 60 Sun×4100 AMD 64 bit Opteron dual core ( 240 cores in total ) , running Linux 2 . 6 , or on 1024 cores of an IBM BlueGene/P supercomputer hosted by CADMOS and accessible to the Blue Brain Project [67] . Runtime ranged from 2 to 5 days . We calculated statistics for the features of perisomatic step current firing directly from experimental voltage traces of the firing response to step currents in adult , P36 Wistar rats measured in vitro ( see [4] for methods ) . Briefly , cells were injected with depolarizing current of variable amplitudes , each 2 seconds in duration , and recorded in whole-cell configuration at 33–35°C . We used data from 11 L5b PCs , each with 10–15 different current step amplitudes that were repeated twice . These cells were also stained with biocytin and some were 3D reconstructed under light microscopy in Neurolucida ( Microbrightfield ) . All morphologies have been checked for z-axis noise , improper diameters , and corrected for tissue shrinkage . We chose a cell with a typical response ( see below ) and morphology to be used as our reference cell for fitting and simulations ( Figure 1A ) . We used two other cell reconstructions for investigating how the models generalized across different morphologies ( Figures 6A , 7A ) . According to experimental studies [56] , pyramidal cells at age P36 are mostly mature in terms of electrical properties , morphology and the ability to generate dendritic Ca2+ spikes . For the BAC firing features , we used statistics reported in [6] as well as our own calculations from voltage traces of five cells that were kindly provided by M . Larkum . BAP attenuation features were characterized using statistics derived from the literature [44] . We used a set of key features of target firing behavior at the soma and dendrites ( Table 1 ) . These served as the objectives to be fitted by the evolutionary algorithm . The error in each feature was measured in terms of SD from the experimental mean for that feature .
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The pyramidal cell of layer 5b in the mammalian neocortex extends its dendritic tree to all six layers of cortex , thus receiving inputs from the entire cortical column and supplying the major output of the column to other brain areas . L5b pyramidal cells have been the subject of extensive experimental and modeling studies , yet realistic models of these cells that faithfully reproduce both their perisomatic Na+ and dendritic Ca2+ firing behaviors are still lacking . Using an automated algorithm and a large body of experimental data , we generated a set of models that faithfully replicate a range of active dendritic and perisomatic properties of L5b pyramidal cells , as well as the experimental variability of the properties . Furthermore , we show a useful way to analyze model parameters with our sets of models , which enabled us to identify some of the mechanisms responsible for the dynamic properties of L5b pyramidal cells as well as mechanisms that are sensitive to morphological changes . This framework can be used to develop a database of faithful models for other neuron types . The models we present can serve as a powerful tool for theoretical investigations of the contribution of single-cell dynamics to network activity and its computational capabilities .
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2011
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Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties
|
Jujube ( Ziziphus jujuba Mill . ) belongs to the Rhamnaceae family and is a popular fruit tree species with immense economic and nutritional value . Here , we report a draft genome of the dry jujube cultivar ‘Junzao’ and the genome resequencing of 31 geographically diverse accessions of cultivated and wild jujubes ( Ziziphus jujuba var . spinosa ) . Comparative analysis revealed that the genome of ‘Dongzao’ , a fresh jujube , was ~86 . 5 Mb larger than that of the ‘Junzao’ , partially due to the recent insertions of transposable elements in the ‘Dongzao’ genome . We constructed eight proto-chromosomes of the common ancestor of Rhamnaceae and Rosaceae , two sister families in the order Rosales , and elucidated the evolutionary processes that have shaped the genome structures of modern jujubes . Population structure analysis revealed the complex genetic background of jujubes resulting from extensive hybridizations between jujube and its wild relatives . Notably , several key genes that control fruit organic acid metabolism and sugar content were identified in the selective sweep regions . We also identified S-locus genes controlling gametophytic self-incompatibility and investigated haplotype patterns of the S locus in the jujube genomes , which would provide a guideline for parent selection for jujube crossbreeding . This study provides valuable genomic resources for jujube improvement , and offers insights into jujube genome evolution and its population structure and domestication .
Chinese jujube ( Ziziphus jujuba Mill . ) ( 2n = 2x = 24 ) , native to China , is one of the oldest cultivated fruit trees , with more than 7 , 000 years of domestication history [1] . It belongs to the Rhamnaceae family in the Rosales order . Jujube is valued as a woody crop and traditional herbal medicine , and cultivated on 2 million hectares in China alone , with an annual production of approximately 4 . 32 million tons [2] . Jujube cultivars have been traditionally classified as fresh or dry , and dry jujubes account for approximately 80% of the total production . Ripe fruits of dry jujube have a coarse texture while those of fresh types have a crisp texture . Cultivated jujubes were domesticated from their wild ancestors ( Z . jujuba Mill . var . spinosa Hu . ) through an artificial selection process for important agronomic traits , which resulted in architectural and structural changes in the tree such as a transition from bushes with more thorns to trees with fewer thorns and enlarged fruit sizes [1 , 3] . As with many agricultural crops , taste attributes of jujube fruits , such as sweetness and sourness , have been the subject of human selection . Fruits of cultivated jujubes have higher levels of sugars ( up to 72% of the dry weight ) , while wild jujube fruits accumulate more soluble organic acids [3 , 4] . The domestication mechanism of fruit sweetness and acidity taste from their wild relatives is still not well characterized . Therefore , characterization of the sugar and acid metabolism of domesticated and wild jujubes through genome-wide analyses would help elucidate the genomic mechanism underlying fruit sweetness and acidity taste improvement . The majority of jujube cultivars produce few seeds due to self-incompatibility or cross-incompatibility , which limit the practical artificial breeding of jujube . Gametophytic self-incompatibility ( GSI ) system is controlled by the S locus and has been found to operate in several Ziziphus species , including Z . jujuba [5–7] . Parents sharing the same S haplotype often result in seedless jujube kernels . Therefore , identification of the self-incompatibility locus ( S locus ) genes would provide a guideline to facilitate jujube breeding . Recently , the draft genome of a fresh jujube cultivar ‘Dongzao’ with a high level of heterozygosity was reported , and it provides insights into the ascorbic acid metabolism and the adaptation mechanism to abiotic/biotic stresses [8] . However , little is known about jujube evolution , domestication , and the genetic bases of fruit quality . The genome sequencing of additional diverse jujubes would help us to address these questions , laying the foundation for improved strategies for jujube breeding . Here , we report the genome of a dry jujube cultivar ‘Junzao’ Fig A and Fig B in S1 File , ) . We also resequenced the genomes of 31 cultivated and wild jujube accessions with a range of geographical distributions . The genome sequences provided insights into the evolution of Rhamnaceae . Integrative transcriptome and resequencing analyses illuminated the genomic mechanisms underlying the domestication events of fruit sweetness and acidity .
Sequencing of the ‘Junzao’ genome resulted in a 351-Mb assembly with contig and scaffold N50 sizes of 34 kb and 754 kb , respectively ( Table 1; Table A in S2 File ) . A k-mer analysis of ‘Junzao’ sequences suggested an estimate genome size of ~350 Mb , consistent with the size estimated from the flow cytometry analysis ( Fig C in S1 File; Table B in S2 File ) . The GC content of the assembled ‘Junzao’ genome was 32 . 6% ( Fig D in S1 File ) . Approximately 98 . 3% of the 2 , 901 expressed sequence tag ( EST ) sequences and 98 . 9% of the assembled transcriptome contigs could be mapped to the ‘Junzao’ genome ( Table 1; Table C in S2 File ) . In addition , 99 . 6% of the core eukaryotic genes were mapped to the ‘Junzao’ genome using CEGMA [9] ( Fig E in S1 File ) and 93 . 2% were completely mapped to the assembled ‘Junzao’ genome using BUSCO [10] ( Table 1 ) , indicating a high quality of the ‘Junzao’ genome assembly . Using two high-density genetic linkage maps , we anchored 600 assembled scaffolds to the 12 linkage groups , covering 83 . 6% ( 293 Mb ) of the assembled ‘Junzao’ genome ( Table D in S2 File; Fig F in S1 File ) . We predicted a total of 27 , 443 protein-coding genes with an average coding sequence length of 1 , 136 bp and an average of 4 . 83 exons ( Table E in S2 File ) , of which 91 . 2% were mapped to the 12 pseudo-chromosomes . A total of 2 . 1 million single-nucleotide polymorphisms ( SNPs ) were detected in the ‘Junzao’ genome , and therefore the heterozygosity level of the genome was calculated as 0 . 72% ( Table F in S2 File ) . In addition , 2 , 309 small insertions and deletions ( indels ) were found to be located in the exonic regions ( Table G in S2 File ) . The assembled ‘Junzao’ genome was 86 . 5 Mb smaller than the reported genome of ‘Dongzao’ ( 437 . 7 Mb ) , which was assembled by sequencing the in vitro cultured plantlet [8] . One notable difference between the ‘Junzao’ genome and the reported ‘Dongzao’ genome was the abundance of transposable elements ( TEs ) . A total of 136 Mb of TEs were identified , accounting for 38 . 8% of the assembled ‘Junzao’ genome , while the reported genome of ‘Dongzao’ contained 204 Mb of TEs ( 46 . 8% ) ( Table 1; Fig G in S1 File; Table H and Table I in S2 File ) . In addition , a more recent accumulation of TEs was found in ‘Dongzao’ ( <1 . 2 million years ago ) ( Fig H ( a ) in S1 File ) , and a greater proportion of genes were close to the TEs in ‘Dongzao’ than in ‘Junzao’ ( Fig H ( b ) in S1 File ) . Phylogenetic analysis also indicated a greater expansion of specific LTR retrotransposon clades in the ‘Dongzao’ genome ( Fig H ( c ) in S1 File ) . Collinear genome regions between ‘Dongzao’ and ‘Junzao’ were identified ( Table J in S2 File ) . The syntenic blocks in the ‘Dongzao’ genome ( 326 . 3 Mb ) were 34 . 1 Mb larger than those in the ‘Junzao’ genome ( 292 . 2 Mb ) . We found that 26 . 0 Mb ( 77% ) of the 34 . 1 Mb were repetitive sequences , further supporting that transposons are one of the major factors contributing to the genome size difference between ‘Junzao’ and ‘Dongzao’ . We found that unanchored scaffolds in the reported ‘Dongzao’ genome had many syntenic blocks with the anchored scaffolds , much higher than those in the assembled ‘Junzao’ genome ( Fig I in S1 File ) . Furthermore , read coverage distribution of coding regions in the ‘Dongzao’ genome displayed a heterozygous peak at the half depth of the major homozygous peak , while no heterozygous peak was found in ‘Junzao’ ( Fig J . and Fig K in S1 File ) . These findings suggest that sequences of heterozygous alleles from the same loci ( redundant sequences ) were sometimes failed to be assembled into consensus sequences in ‘Dongzao’ , partially contributing to the larger genome assembly size of ‘Dongzao’ than that of ‘Junzao’ . In addition , we identified ~4 . 9 Mb bacterial sequences in the ‘Dongzao’ genome assembly . Taken together , we suggest that higher levels of repetitive sequences , redundant sequences and bacterial contaminated sequences in the assembled ‘Dongzao’ genome have contributed to the larger genome assembly of ‘Dongzao’ than ‘Junzao’ . A presence-absence variation ( PAV ) analysis identified 7 . 8 Mb of ‘Dongzao’-specific sequences containing 354 genes and 14 . 2 Mb of ‘Junzao’-specific sequences containing 432 genes . Gene Ontology ( GO ) terms including DNA recombination and DNA integration were found to be significantly enriched in ‘Dongzao’-specific genes ( Table K in S2 File ) . In addition , we identified 131 expanded gene families ( 930 genes ) and 232 contracted families ( 702 genes ) in ‘Junzao’ in comparison with ‘Dongzao’ ( Fig L in S1 File ) . ‘Junzao’ and ‘Dongzao’ are representative cultivars of dry and fresh jujubes , respectively ( Fig B in S1 File ) . Their fruits contain highly different levels of crude fiber , which is derived from the fruit primary cell walls ( Table L in S2 File ) . We found that several families of genes involved in cell wall modification were substantially expanded in the dry jujube ‘Junzao’ compared with ‘Dongzao’ , including those encoding glycosyl hydrolases ( beta-glucosidases , xyloglucan endotransglucosylase-hydrolases , endoglucanases and polygalacturonases ) and those encoding pectin esterases and rhamnogalacturonate lyases ( Table M in S2 File ) . Eight putative proto-chromosomes of the common ancestor of Rhamnaceae and Rosaceae , two sister families in the order Rosales , were inferred based on the available genome sequences of jujube , peach ( Prunus persica ) and apple ( Malus × domestica ) ( Fig 1 ) , and they are similar to the nine putative proto-chromosomes of the ancestor of Rosaceae [11 , 12] No recent whole-genome duplication ( WGD ) events were detected in jujube [8] and peach [13] after their divergence , while one such event was identified in apple [14] . Although the numbers of proto-ancestral chromosomes in the Rosaceae increased from eight to nine after its divergence from the Rhamnaceae [11] , we were still able to identify a one-to-two relation between the jujube and apple genomes . Considering the intergenomic relations among jujube , peach and apple , we determined the two largest chromosome synteny pairs as follows: 1 ) jujube chromosome 3 , peach chromosome 2 and apple chromosomes 1 and 7 , and 2 ) jujube chromosome 10 , peach chromosome 3 and apple chromosomes 9 and 17 ( Fig 1 ) , which reflected the recent diploidization of the apple genome [14] . A conserved block was also identified among jujube chromosome 3 , peach chromosome 2 and apple chromosome 7 , which did not undergo any rearrangements , fissions or fusions and is thus likely derived directly from ancient chromosome III ( Fig 1 ) . These results showed that larger syntenic blocks were retained in jujube chromosomes , and illustrated that fewer chromosome fissions , fusions and rearrangements occurred in the jujube genome compared with the peach and apple genomes ( Table N in S2 File ) . Resequencing the genomes of 31 accessions , including 10 wild jujube individuals ( 6 typical wild jujubes and 4 semi-wild accessions ) and 21 jujube cultivars ( Table O in S2 File; Fig M in S1 File ) , generated a total of 344 Gb of sequences , representing an average depth of 27 . 8× and an average coverage of 92 . 5% ( Table O in S2 File ) . After mapping the reads of each accession to the genome of ‘Junzao’ , we detected a total of 5 , 300 , 355 SNPs . The parameter θπ values [15] indicated that wild jujubes , although represented in our analysis by half the number of accessions ( 10 ) as cultivated accessions ( 21 ) , exhibited greater diversity ( θπ = 2 . 60×10−3 ) than cultivated jujubes ( θπ = 2 . 19×10−3 ) . The neighbor-joining phylogenetic tree illustrated the domestication process as a transition from wild to cultivated jujubes via certain semi-wild accessions ( Fig 2A ) . In addition , the cultivated jujube group could be further divided into two subgroups that were generally correlated with their geographical distributions in West China and East China ( Fig 2A; Fig N in S1 File ) . A principle component analysis ( PCA ) generated a similar pattern ( Fig 2B ) to that of the phylogenetic analysis in that the jujube cultivars formed a tight cluster that was distant from the wild jujube accessions . Population structure analysis indicated that wild and cultivated jujubes could be divided into two groups when K = 2 , although admixed features were observed in 17 accessions covering both cultivated and wild jujubes ( Fig 2C ) . With K = 3 , the cultivated populations were further divided into two subgroups corresponding to their geographical distributions ( West and East China ) , whereas the wild population remained relatively uniform . When the K value increased progressively from 3 to 5 , new subgroups emerged in the wild jujube group and further differentiation was found within the cultivated jujubes ( Fig 2C ) . As shown in Fig 3A , jujube fruit had a much higher content of soluble sugars , and lower levels of organic acids than wild jujube fruits ( Table P in S2 File ) , indicating both sweetness and acidity are important traits under human selection . Selective sweep regions covering 1 , 372 genes were identified in the jujube genome ( Fig 3B; Table Q in S2 File ) . These included four genes , which encode an NADP-dependent malic enzyme ( NADP-ME ) , a pyruvate kinase ( PK ) , an isocitrate dehydrogenase ( IDH ) , and an aconitate hydratase ( ACO ) , all of which play key roles in organic acid metabolism in fruit ( Fig 3C; Table R in S2 File ) . In addition , three vacuolar proton pumps ( V-type proton ATPase ) , transporting H+ into vacuolar , were also in the putative sweep regions . On the other hand , three genes involved in sugar metabolism in fruit , encoding a sucrose synthase ( SUSY ) , a phosphoglucomutase and a 6-phosphofructokinase , and 13 sugar transporters were also identified in the regions of putative selective sweeps . Expression profiling analysis of sugar- and acid-related metabolism genes showed that a gene encoding a vacuole acid invertase ( VAINV ) , an enzyme that irreversibly catalyzes the hydrolysis of sucrose to glucose and fructose , was expressed at a significantly lower level in the ripe fruits of cultivated jujubes than in those of wild jujubes , possibly contributing to higher sucrose accumulation in the vacuoles of cultivated jujube fruits ( Fig 3C ) . In addition , most genes involved in acid metabolism pathways , including those encoding NADP-ME , PK , phosphoenolpyruvate carboxylase ( PEPC ) , malate dehydrogenase ( MDH ) , shikimate dehydrogenase ( SD ) and citrate synthesis ( CS ) , were expressed at much higher levels in wild than in cultivated jujube fruits ( Table S in S2 File ) . This trend was also the case for a neutral invertase in the sucrose biosynthesis pathway , which supplies glucose and fructose for organic acid metabolism ( Fig 3C ) . On the contrary , genes involved in decomposing citrate , such as ACO and IDH , were expressed at lower levels in wild than in the cultivated jujubes . Furthermore , a population differentiation analysis based on a population fixation index ( Fst ) between dry and fresh jujube groups ( Table T in S2 File ) uncovered four genes encoding beta-galactosidases and one encoding endo-1 , 4-beta-xylanase in the highly differentiated regions ( Table U in S2 File ) . We identified a candidate S-RNase gene ( Zj . jz035833030; chromosome 1 ) and two S-like RNases ( Zj . jz026761011 and Zj . jz022467042; chromosomes 7 and 9 , respectively ) that belong to the T2-RNase family in the ‘Junzao’ genome ( Table V in S2 File ) . We also identified the three T2-RNase genes in the ‘Dongzao’ genome ( Table V in S2 File ) . Phylogenetic analysis further confirmed that Zj . jz035833030 was the S-RNase and that Zj . jz026761011 and Zj . jz022467042 were S-like RNases ( Fig 4A ) . A transcriptome analysis revealed that Zj . jz035833030 ( named S1 ) was specifically expressed in flowers , and the two S-like RNase genes were expressed in all tested tissues ( Fig 4B ) . We identified four candidate SFB genes near the S1 S-RNase gene on chromosome 1 and inferred that the jujube S locus is likely localized within a narrow region ( from 3 . 8–4 . 3 Mb ) on chromosome 1 . However , none of these four SFB genes was specifically expressed in flowers ( Fig 4C ) . A phylogenetic analysis showed that all four SFB genes were clustered together in the same clade with the Prunus SFB ( Fig O in S1 File ) . The structure of the jujube S locus was similar to that of pear [16] . By mapping genome sequencing reads that were generated from the 31 jujube accessions to the putative S1 region , we identified 21 SNPs ( 9 in the first exon , 11 in the second exon , and one in the intron ) and 3 indels ( one in the first exon , one in the second exon , and one in the intron ) . Among the 21 SNPs , 13 were located in the ribonuclease domain of T2-RNase ( Fig P in S1 File ) . According to the SNP pattern in the S-RNase gene , we assigned each accession a corresponding haplotype . We found that S1 was the most common haplotype , and there were 10 accessions with a homozygous S1 locus and 14 with a heterozygous locus ( Fig 4D ) .
We report the 351-Mb genome of the heterozygous dry jujube cultivar ‘Junzao , ’ which is 86 . 5 Mb smaller than the assembled ‘Dongzao’ genome ( 437 . 7 Mb ) [8] . We performed a series of analyses to characterize the size difference between the two genome assemblies , and revealed that the difference was primarily attributed to different levels of transposable elements , redundant sequences caused by heterozygosity , and bacterial contaminated sequences . In addition , we note that the k-mer and flow cytometry analyses of leaves from a ‘Dongzao’ mature tree indicated a similar genome size ( ~352 Mb ) to that of ‘Junzao’ ( Table B and Table W in S2 File ) . There are several reports of tissue culture-induced genome-level changes in plants , and one of the major underlying factors is the proliferation of transportable elements [17 , 18] . Accordingly , the use of in vitro cultured plantlets as the source material for genome sequencing could lead to complex genetic background to some extent . We resequenced a wide range of jujube cultivars and their wild relatives covering different fruit types ( dry or fresh ) and geographical distributions . This exploration revealed the various consequences of artificial selection during jujube domestication and elucidated the history of jujube domestication . Genome-wide SNP analysis revealed that naturally grown wild jujubes possessed higher diversity than cultivated jujubes , and the overall genetic diversity of jujube is similar to that of peach [19] but lower than that of several herbaceous species , such as soybean [20] , rice [21] and cucumber [22] . A complex genetic background , caused by natural or artificial hybridization , was also revealed , which might explain why some semi-wild jujubes exhibited few traits that could distinguish them from cultivated jujubes , other than a sour taste and smaller fruit and tree sizes . Consistent with the population structure deduced by chloroplast diversity [23] , our analyses using various approaches supported the hypothesis of an admixed population structure of wild and cultivated jujubes . We propose that this pattern reflects frequent exchanges of jujube germplasm between human populations from different sites and natural and artificial hybridizations . The domestication of jujube has involved the selection for the fruit sourness and sweetness profiles , which are determined by acid and sugar metabolism . Analysis of fruit sugar and acid contents in a wide range of apple accessions suggested that fruit acidity rather than sweetness is likely to have undergone selection during apple domestication [24] . In this study , we found that unlike apple , sugar and acid metabolism were both undergone human selection during jujube domestication ( Fig 3A ) . In particular , four genes and several sugar transporters involved in acid and sugar metabolism were determined to be putatively under human selection , of which two genes ( NADP-ME and PK ) are closely associated with the production of malate , which contributes directly to the acid taste of the fruit [25] . In addition , the expression levels of genes encoding enzymes , such as NADP-ME , PK , PEPC and MDH were substantially lower in cultivated jujube fruits in comparison with wild jujubes . Differential expression of the same gene sets between high-and low-acid apple fruits has also been reported [25 , 26] . GSI have been characterized in the Rosaceae , Solanaceae and Plantaginaceae families which involves an S locus [27] . Although GSI is widely present in the Ziziphus species , the molecular mechanism controlling the hybridization behavior in jujubes is unknown . We identified the jujube S locus on the basis of genome sequence information , which was supported by gene expression analysis . Analyses of the S-locus genotypes of the 31 accessions provide guidelines for parent selection for crossbreeding . We have proved this strategy by crossing two random combinations with different S haplotypes , i . e . , ‘Dongzao’ × ‘Linyilizao’ [28] and ‘Dongzao’ × ‘Zhongningyuanzao’ [29] .
We assembled a draft genome of the dry jujube cultivar ‘Junzao’ covering 351 Mb with contig and scaffold N50 sizes of 34 kb and 754 kb , respectively , which was 86 . 5 Mb smaller than that of ‘Dongzao’ ( 437 . 7 Mb ) for which an in vitro culture plantlet was sequenced . Higher levels of repetitive sequences and redundant sequences in the assembled ‘Dongzao’ genome have primarily contributed to the larger genome assembly of ‘Dongzao’ than ‘Junzao’ . By comparing the fresh ( ‘Dongzao’ ) and dry ( ‘Junzao’ ) jujube genomes , we found gene families involved in cell wall modification were largely expanded in ‘Junzao’ , which might characterize the difference in fruit quality between dry and fresh cultivars . We reconstructed eight putative proto-chromosomes of the common ancestor of Rhamnaceae and Rosaceae based on the genome sequences of jujube , peach and apple , which elucidated the evolutionary processes that have shaped the genome structures of modern jujubes . Genome resequencing of 31 geographically diverse accessions of cultivated and wild jujubes illustrated the domestication progress of jujubes and revealed the complex genetic background of jujubes caused by natural or artificial hybridizations . Based on the analysis of selective sweeps , we identified four genes involved in acidity metabolism pathways that encode an NADP-ME , PK , an IDH , and an ACO , all of which play key roles in organic acid metabolism . In addition , three V-type ATPase , enhancing organic acid storage in fruit , were also in the putative sweep regions . Furthermore , SUSY and several sugar transporter genes were determined to be putatively under selection . These findings might elucidate the changes in the sweetness/acidity taste caused by domestication events . We also identified the S-locus genes that controlled gametophytic self-incompatibility , and investigated the haplotype patterns of the S locus in diverse jujube accessions . Our study offers novel insights into the jujube population structure and domestication and provides valuable genomic resources for jujube improvement .
A 9-year-old diploid , highly heterozygous dry jujube cultivar , ‘Junzao’ ( voucher number: NWAFU-Junzao001 ) , grown at the Jujube Experimental Station ( N 37 . 13 , E 110 . 09 ) of Northwest A&F University , Qingjian , Shaanxi Province , China , was used for genome sequencing . Genome sizes of jujubes including ‘Junzao’ , ‘Dongzao’ and other 11 accessions were estimated by flow cytometry analyses on the young leaves ( Table B in S2 File , S1 File Supplementary notes ) . The ‘Junzao’ genome was sequenced using a whole-genome shotgun strategy [30] . High-quality genomic DNA was extracted from young leaves using the Qiagen DNeasy Plant Mini Kit ( Qiagen , Valencia , CA , USA ) . A total of 3 μg of DNA was used for each library construction . Short-insert paired-end libraries ( 180 bp and 500 bp ) were generated using the NEB Next Ultra DNA Library Prep Kit for Illumina ( NEB , USA ) according to the manufacturer’s instructions . Large-insert ( 2 kb , 5 kb , 10 kb , 15 kb and 20 kb ) DNA sequencing libraries were prepared through circularization by Cre-Lox recombination [31] . These libraries were sequenced on the Illumina HiSeq 2000 system . A total of 79 Gb of high-quality cleaned sequences ( approximately 227x coverage of the genome ) was generated and used for de novo genome assembly ( Table X in S2 File ) . A modified version of SOAPdenovo was developed specifically for the de novo assembly of the highly heterozygous jujube genome ( S1 File Supplementary Notes ) . Augustus [32] , Geneid [33] , Genscan [34] , GlimmerHMM [35] and SNAP [36] were used for ab initio gene predictions . We also aligned the protein sequences of Arabidopsis thaliana , Capsicum annuum , Citrus clementina , Eucalyptus grandis , Malus × domestica , Oryza sativa , Populus trichocarpa , and Vitis vinifera to the ‘Junzao’ genome using TBLASTN with an E-value cutoff of 1e-5 . The homologous genome sequences were then aligned to the matched proteins for accurate spliced alignments using GeneWise [37] . Finally , a total of 36 Gb of high-quality RNA-Seq reads was aligned to the ‘Junzao’ genome using TopHat [38] with default parameters . Based on the RNA-Seq read alignments , Cufflinks [39] was then used for transcriptome-based gene structure predictions . Outputs from ab initio gene predictions , homologous protein alignments and transcript mapping were integrated using EVM [40] to form a comprehensive and non-redundant reference gene set and then filtered by removing the genes with incorrect coding sequences and putative repeat elements ( 80% coverage ) . We combined two genetic maps , described below , to anchor the assembled scaffolds of ‘Junzao . ’ First , we used a previously published restriction site-associated DNA ( RAD ) -based high-density genetic map generated from an inter-specific F1 population to anchor the genome assembly [41] . Second , we constructed a genetic map by using a different F1 population ( ‘Dongzao’ × ‘Yingshanhong’ , 96 progenies ) , which was also based on the RAD strategy according to Baird et al [42] . High-quality SNP and SSR markers were used to construct a linkage map ( Table D in S2 File ) . The resulting genetic map was used to further anchor the assembled scaffolds of ‘Junzao . ’ To better understand the evolutionary processes that shaped the genome structures of jujubes , we reconstructed the putative proto-chromosomes of the common ancestor of Rhamnaceae and Rosaceae , which are sister families in the order Rosales [43] . Protein sequences from 13 plant species ( A . thaliana , C . annuum , C . sinensis , M . × domestica , O . sativa , P . trichocarpa , V . vinifera , Cucumis sativus , Pyrus × bretschneideri , Actinidia chinensis , Cypripedium arietinum , Z . jujuba ‘Dongzao’ and Z . jujuba ‘Junzao’ ) were extracted for building gene families . For alternatively spliced isoforms , only the longest proteins were used in the analysis . An all-to-all BLASTP was used to compare protein sequences with an E-value cutoff of 1e-7 , and OrthoMCL [44] was then used to cluster genes from these species into families with the parameter “-inflation 1 . 5 . ” MUSCLE [45] was used to generate multiple sequence alignments of proteins in single-copy gene families with default parameters . RAxML [46 , 47] and a ‘supermatrix’ of protein sequences were used to construct the phylogenetic tree with the maximum likelihood algorithm . A molecular clock model was implemented to estimate the divergence time of these 13 species using McMctree in PAML [48] . To obtain a more accurate result , ‘r8s’ was used to estimate the divergence time based on the constructed tree . Café [49] was used to identify gene families that have undergone significant expansion or contraction in the Z . jujuba ‘Junzao’ genome with a p-value cutoff of 0 . 05 . The one-to-one collinear regions between ‘Junzao’ ( accession number: PRJNA306374 ) and ‘Dongzao’ [8] were detected using the MUMmer package [50] with the parameters ‘-maxmatch -c 90 -l 40 -d 0 . 05’ . The sequence alignments were performed on the scaffold level between these two jujube genomes . The best reciprocal alignments with length less than 300 bp or an identity less than 90% were discarded , and then the aligned regions within the same scaffold were connected together . Regions were identified as syntenic blocks if there were more than 5 adjacent alignment regions between the two genome sequences . In addition , we use the “show-snps” program in MUMmer package to detect homozygous SNPs and small indels from the one-to-one alignments . RepeatMasker ( http://www . repeatmasker . org/RepeatModeler . html ) was used to find repeat elements in sequences that could not be aligned to the ‘Junzao’ genome . Sequences shorter than 100 bp were removed . ‘Dongzao’-specific sequences were obtained after realigning them with the ‘Junzao’ genome and discarding sequences with identities of greater than 95% and gap lengths of less than 100 bp . Potential bacterial sequences , which were identified on the basis of BLAST searches against the GenBank NT database , were excluded . Genes with at least 90% of the CDS regions covered by ‘Dongzao’-specific sequences were defined as ‘Dongzao’-specific genes . Syntenic blocks shared by the seven species ( V . vinifera , P . trichocarpa , Theobroma cacao , A . thaliana , Prunus persica , M . × domestica , and Z . jujuba ‘Junzao’ ) were identified with MCscanX [51] using the grapevine genome as the reference . Syntenic blocks containing at least 3 gene pairs were retained to reconstruct the genome structure of the seven selected species . Based on the syntenic and overlapping relations of Z . jujuba , P . persica and M . × domestica genomes , we reconstructed the paleo-chromosomes of the common ancestor of Rhamnaceae and Rosaceae using a previously described method [52 , 53] . The structures of the V . vinifera , P . trichocarpa , T . cacao and A . thaliana genomes were reconstructed by comparing them with the seven proto-chromosomes of the eudicot ancestor . Thirty-one jujube accessions were chosen for genome resequencing analysis , including 10 wild jujube individuals ( 6 typical wild jujubes and 4 semi-wild accessions ) and 21 jujube cultivars ( Table O in S2 File; Fig M in S1 File ) . Illumina paired-end genome libraries were constructed for each accession following the manufacturer’s instructions and then sequenced on Illumina HiSeq 2500/4000 platforms , which yielded a total of 363 Gb of raw paired-end sequences . The raw data were processed to remove low-quality bases , adapter sequences , and putative PCR duplicates , resulting in a total of 344 Gb of high-quality paired-end sequences . The cleaned reads were mapped to the ‘Junzao’ genome using BWA [54] with the parameters “mem -t 4 -k -M” [29] . BAM files were processed for SNP calling using the SAMtools mpileup function [55] with the parameters “-m 2 -F 0 . 002 -d 1000 -u . ” High-quality SNPs , which were supported by a coverage depth of 5–1 , 000 , mapping quality >20 , distance of adjacent SNPs >10 bp and missing ratio of samples within each group <50% , were retained for subsequent analyses . Peach was used as the outgroup to construct the phylogenetic tree . The NUCmer program in MUMmer [50] was used to align the peach genome ( GenBank accession no . AKXU00000000 ) with the ‘Junzao’ genome with default settings . SNPs within the best-hit regions were extracted , and then the genotypes of peach were used to provide outgroup information at corresponding positions . The neighbor-joining phylogenetic tree was constructed using Treebest-1 . 9 . 2 ( http://treesoft . sourceforge . net/treebest . shtml ) on the basis of the p-distance . We used the parameter θπ [15] to assess the level of genetic diversity for cultivated and wild jujube populations by scanning whole-genome SNP sites , respectively . PCA was performed using EIGENSOFT [56] . The eigenvectors were obtained from the covariance matrix using the R function ‘eigen . ’ The population structure was further inferred using the program FRAPPE [57] with kinship ( K ) set from 2 to 5 and the maximum iteration of expectation-maximization set to 10 , 000 . Four methods , i . e . , the θπ ratios , pairwise population differentiation ( Fst ) levels [58] , Tajima’s D test [59] and the cross-population composite likelihood ratio test ( XP-CLR ) [60] , were used to identify the selective sweeps associated with jujube domestication events . Briefly , θπ ratios ( θπ , wild/θπ , cultivated ) , Fst and TajimaD were calculated using a sliding window analysis with a window size of 20 kb and a step size of 10 kb . XP-CLR test was performed with the following parameters: sliding window size of 0 . 6 cM , grid size of 10 kb , maximum number of SNPs within a window of 300 , and correlation value for 2 SNPs weighted with a cutoff of 0 . 95 . Genome regions with the top 5% of scores in each of the four methods were identified and those detected by at least two of the methods were identified as selective sweeps . In addition , we used the top 5% highest FST values to characterize the population differentiation between dry and fresh jujubes . Genes within these regions were subjected to GO enrichment analysis using EnrichPipline [61] . We compared the ‘Junzao’ genes to the information in a local database containing known S-RNase gene sequences collected from NCBI with an E-value cutoff of 1e-10 using BLASTN . We then screened for genes belonging to the T2 RNase gene family from the BLAST results because S-RNase genes are members of the T2 RNase family [62 , 63] . Candidate S-RNase genes were further screened according to two criteria: the absence of the amino acid pattern 4 ( [CG] P [QLRSTIK][DGIKNPSTVY] ) [63] and the presence of a maximum of two introns [64] . The S-RNase genes from four families ( Rosaceae , Fabaceae , Solanaceae , and Plantaginaceae ) and the candidate jujube S-RNases were used to construct the phylogenetic tree using RAxML with a generalized time-reversible ( GTR ) model of sequence evolution . Pollen-determinant S-haplotype-specific genes belong to the F-box family . We used a similar BLAST strategy to that described above to search for the F-box genes in the chromosome region in which the candidate S-RNase gene was located . A phylogenetic analysis was performed for those candidate SFB genes together with the known Prunus SFB , Petunia SLFs , Prunus SLF1 and Malus SFBs . To investigate the expression of the candidate S-RNase and SFB genes , we used RNA-Seq data from leaves , phloem , flowers and fruits of ‘Junzao . ’ The SNP calling results derived from the resequencing of 31 accessions were used to reconstruct the two haplotypes of S-RNase gene using HapCUT [65] . Phloem , mature leaves , flowers , and fruits at different stages ( expanding fruit , half-red , and full-red ) of the ‘Junzao’ cultivar and the wild jujube ‘Qingjiansuanzao’ ( 8 years old ) were collected in 2013 and 2014 , respectively . All the samples were immediately frozen in liquid nitrogen . Total RNAs were isolated using a modified CTAB method and then treated with RNase-free DNase I ( Promega , USA ) . First-strand cDNAs were synthesized using a Clontech kit . RNA-Seq libraries were constructed using the NEB Next UltraTM RNA Library Prep Kit ( NEB , USA ) and sequenced on a HiSeq 2000/2500 system . RNA-Seq reads were mapped to the ‘Junzao’ genome using TopHat [38] . The total numbers of aligned reads ( read counts ) for each gene were normalized to the reads per kilobase exon model per million mapped reads ( RPKM ) [66] . DESeq [67] was used to identify differentially expressed genes . Fruits were collected at three developmental stages: expanding fruit , half-red fruit and full-red fruit . Sugars ( fructose , glucose and sucrose ) and acids ( malic acid , citric acid and succinic acid ) were quantified using high-performance liquid chromatography ( HPLC , Shimadzu ) as described previously [68] . A total of 1 g of the edible part of the dried jujubes was ground and incubated in 50 mL of 80% ethanol in an ultrasonic bath ( 40 kHz , 45°C , 20 min ) . The samples were centrifuged at 3 , 500 ×g for 10 min , and the supernatant was collected in a new tube . The pellet was re-extracted by repeating the above steps . The combined supernatants were evaporated in a rotary evaporator at 45°C and then diluted with deionized water to 10 mL . The diluted extracts were filtered through a 0 . 45-μm membrane filter prior to HPLC analysis . Accession codes: Sequence data have been deposited in the GenBank/EMBL/DDBJ nucleotide core database under the accession number LPXJ00000000 ( PRJNA306374 ) and all sequence reads have also been deposited in the online database . The version described in this paper is the first version .
|
A balanced sweetness and acidity taste is among the most important characteristics of fruits . It is generally believed that human selection of sweetness plays a crucial role in the process of domestication from wild to cultivated fruit trees . However , the molecular mechanisms underlying fruit taste domestication still remain unclear . It is also unclear whether taste improvement is mainly determined by positive selection of advantageous traits such as sweetness or negative selection of disadvantageous trait such as acidity . Chinese jujube , domesticated from the wild jujube , is an economically important fruit tree crop in China . In this study , we sequenced and assembled the genome of a dry jujube and analyzed the genetic relationship between cultivated and wild jujubes through genome resequencing . Key genes involved in the acid and sugar metabolism were identified in the selective sweep regions . This finding suggested an important domestication pattern in fruit taste and also provided insights into the fruit molecular breeding and improvement .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Conclusions",
"Methods"
] |
[
"biotechnology",
"genomic",
"library",
"construction",
"sequence",
"assembly",
"tools",
"plant",
"science",
"phylogenetic",
"analysis",
"genome",
"analysis",
"crops",
"plant",
"genomics",
"dna",
"construction",
"molecular",
"biology",
"techniques",
"plants",
"research",
"and",
"analysis",
"methods",
"sequence",
"analysis",
"crop",
"science",
"gene",
"mapping",
"bioinformatics",
"sequence",
"alignment",
"plant",
"genetics",
"comparative",
"genomics",
"molecular",
"biology",
"fruits",
"molecular",
"biology",
"assays",
"and",
"analysis",
"techniques",
"agriculture",
"dna",
"library",
"construction",
"database",
"and",
"informatics",
"methods",
"genetics",
"biology",
"and",
"life",
"sciences",
"genomics",
"plant",
"biotechnology",
"computational",
"biology",
"organisms"
] |
2016
|
The Jujube Genome Provides Insights into Genome Evolution and the Domestication of Sweetness/Acidity Taste in Fruit Trees
|
The severity of cardiac disease in chronic Chagas disease patients is associated with different features of T-cell exhaustion . Here , we assessed whether the ability of T cells to secrete IFN-γ in response to T . cruzi was linked to disruption in immune homeostasis and inflammation in patients with chronic Chagas disease . PBMCs from chronic Chagas disease patients and uninfected controls were examined for frequencies of T . cruzi-responsive IFN-γ-producing cells by ELISPOT and cellular expression and function of IL-7R using flow cytometry . Serum levels of IL-7 , IL-21 , IL-27 , soluble IL-7R , and inflammatory cytokines were also evaluated by ELISA or CBA techniques . Patients possessing T . cruzi-specific IFN-γ-producing cells ( i . e . IFN-γ producers ) had higher levels of memory T cells capable of modulating the alpha chain of IL-7R and an efficient response to IL-7 compared to that in patients lacking ( i . e . IFN-γ nonproducers ) parasite-specific T-cell responses . IFN-γ producers also showed low levels of soluble IL-7R , high basal expression of Bcl-2 in T cells and low basal frequencies of activated CD25+ T cells . Modulation of IL-7R was inversely associated with serum IL-6 levels and positively associated with serum IL-8 levels . Circulating IL-21 and IL-27 levels were not associated with the frequency of IFN-γ producing cells but were reduced in less severe clinical forms of the disease . In vitro stimulation of PBMCs with IL-7 or IL-27 enhanced IFN-γ production in IFN-γ producers but not in IFN-γ nonproducers . Alterations of the IL-7/IL-7R axis and in the levels of inflammatory cytokines were linked to impaired T . cruzi-specific IFN-γ production . These alterations might be responsible of the process of immune exhaustion observed in chronic Chagas disease .
Chagas disease is a major health problem in Latin America and an increasing threat in other countries that are non-endemic for Trypanosoma cruzi infection [1–4] . The relevance of T cell-mediated immunity in controlling T . cruzi infection has been demonstrated in human T . cruzi infections and in experimental models [5–9] . Individuals chronically infected with T . cruzi-have several indicators of T-cell exhaustion . A major finding was an overall low level of detectable T . cruzi-specific T cells and a predominance of single cytokine interferon ( IFN ) -γ only-producing T cells in the circulation of subjects with long-term T . cruzi infections [10–12] . Other feature of immune exhaustion of T cells in chronic Chagas disease is the expression of cytotoxic T lymphocyte antigen 4 by IFN-γ-producing CD4+ T cells in response to T . cruzi [13] and in the total T-cell compartment [13–15] . CTLA-4 expression was also observed in CD3+ T lymphocytes infiltrating the heart tissues of chronically infected subjects with severe myocarditis [13] . These findings suggest that parasite persistence induces overexpression of inhibitory receptors that might regulate deleterious consequences of a sustained immune response but also dampens the parasite-specific T-cell responses necessary for parasite control . Interleukin-7 ( IL-7 ) plays an important role in the maintenance of naïve and memory T cells by homeostatic mechanisms [16] . The IL-7 cell-surface receptor ( IL-7R ) comprises two chains , namely , the specific IL-7Rα ( CD127 ) chain and the common γ-chain ( CD132 or γc ) [17] . The regulation of each chain is different; CD127 is downregulated , whereas the CD132 chain is rapidly upregulated upon T-cell activation [18] . Soluble IL-7R ( sCD127 ) binds to IL-7 with an affinity similar to that of membrane-bound IL-7R [19] , leading to sIL-7R-mediated inhibition of IL-7 signaling in T cells [20–21] . Inflammation perturbs the IL-7 axis , promoting senescence and exhaustion [22–23] . In a previous study , we found that chronic Chagas disease patients with severe cardiomyopathy have impaired function of IL-7R in total T cells [24] . Here , we sought to investigate whether the ability of T cells to produce IFN-γ in response to T . cruzi antigens was associated with the expression and function of IL-7R , with serum concentrations of the soluble form of IL-7R , STAT5 signaling , and inflammatory cytokines in chronic Chagas disease subjects with different degrees of cardiac dysfunction . In addition , in vitro treatment of PBMCs with IL-7 or IL-27 to enhance T . cruzi-specific IFN-γ production was evaluated . We showed that the ability of T cells to secrete IFN-γ in response to T . cruzi was associated with a functional IL-7/IL-7R signaling pathway in memory T cells , low levels of sCD127 and enhanced basal expression of Bcl-2 on T cells .
This study was approved by the Institutional Review Board of the Hospital Interzonal General de Agudos Eva Perón . All patients signed informed consent forms prior to inclusion in the study . T . cruzi-infected subjects were recruited at the Chagas Disease Unit Cardiology Department , Hospital Interzonal General de Agudos Eva Perón , Buenos Aires , Argentina . T . cruzi infection was determined with indirect immunofluorescence assays , hemagglutination and ELISA tests [25] . Patients positive in at least two of these tests were considered to be infected . Subjects were clinically evaluated and grouped according to a modified version of the Kuschnir grading system [26]: Group 0 ( G0 ) , seropositive individuals exhibiting a normal electrocardiogram ( ECG ) and normal echocardiograph; Group 1 ( G1 ) , seropositive individuals with a normal echocardiograph but ECG abnormalities; Group 2 ( G2 ) , seropositive individuals with ECG abnormalities and heart enlargement; and Group 3 ( G3 ) , seropositive individuals with ECG abnormalities , heart enlargement and clinical or radiological evidence of heart failure . These individuals were originally infected while living in areas where T . cruzi infection was endemic but had lived in an area where T . cruzi infection was not endemic for an average of 30 years . Two patients both in the G2 clinical stages had been treated with benznidazole six years prior to inclusion in this study . The serologic titers against T . cruzi did not decrease after treatment and in one of them the clinical stage changed from G1 to G2 after benznidazole administration , supporting that treatment was not successful in these two patients . Healthy subjects from Buenos Aires that have always resided in non-endemic areas and with negative serology for T . cruzi infection served as the uninfected group ( UI ) . The primary characteristics of the study population are summarized in Table 1 . T . cruzi-infected patients and uninfected controls with hypertension , ischemic heart disease , cancer , HIV infection , syphilis , diabetes , arthritis or serious allergies were excluded from this study . Approximately 50 mL of blood was drawn by venipuncture into heparinized tubes ( Vacutainer , BD Biosciences ) . PBMCs were isolated by density gradient centrifugation with Ficoll-Hypaque medium ( Amersham ) and resuspended in a volume of RPMI 1640 medium ( Corning ) supplemented with 10% heat-inactivated FBS ( NOTACOR ) . Cells were then cryopreserved with an equal volume of freezing media containing 20% DMSO and 80% FBS and stored in liquid nitrogen until use . For serum separation , blood was allowed to coagulate at room temperature and centrifuged at 2000 rpm for 10 min . Then , serum was aliquoted and stored at -70°C until use . Cell viability was evaluated by trypan blue staining ( 80–95% viable cells/sample ) prior to use . Due to sample availability , the assays were not run for all samples . Protein lysate from T . cruzi amastigotes derived from the Brazil strain was obtained by four freeze/thaw cycles followed by sonication , as previously reported [10] . The number of T . cruzi-specific IFN-γ-producing T-cells was determined by ex vivo ELISPOT assays with a commercial kit ( BD Biosciences ) , as described elsewhere [10] . PBMCs were stimulated with 10 μg/mL of a T . cruzi lysate preparation , with or without IL-7 ( Abcam ) or IL-27 ( R&D ) , at 50 ng/mL final concentration . Stimulation with 20 ng/mL of PMA ( Sigma ) plus 500 ng/mL ionomycin ( Sigma ) or medium alone , with or without cytokines , was performed as a positive or negative control , respectively . The number of specific IFN-γ-secreting cells was calculated by subtracting the value of the wells containing media alone . Responses were considered positive when a minimum of 10 spots/4×105 PBMCs were present per well , and this number was at least twice the number present in wells with medium alone . Thereafter , subjects who showed positive IFN-γ ELISPOT responses were referred as “IFN-γ producers” and those with ELISPOT responses below background levels were referred as “IFN-γ nonproducers” . Fluorescein ( FITC ) -conjugated anti-CD25 ( catalog number 555431 ) , phycoerythrin ( PE ) -conjugated anti-CD132 ( catalog number 555900 ) , allophycocyanin ( APC ) , peridinin-chlorophyll proteins ( PerCP ) - and Pacific Blue ( PB ) -conjugated anti-CD4 ( catalog numbers 555349 , 347324 and 558116 , respectively ) , PerCP- or FITC-conjugated anti-CD8 ( catalog numbers 347314 and 555634 , respectively ) , Alexa Fluor 647-conjugated anti-CD127 ( catalog number 558598 ) , FITC-conjugated anti-CD45RA ( catalog number 555488 ) , PE-conjugated anti-phosphorylated STAT5 ( 612567 ) , PE Cy7-conjugated anti-PD-1 ( catalog number 561272 ) , Alexa Fluor 488-conjugated anti-IFN-γ ( catalog number 557718 ) and Fixable Viability 510 ( 564406 ) were purchased from BD Biosciences . PE-conjugated anti-Bcl-2 ( MHBCL04 ) was purchased from Thermo Fisher Scientific . Cell samples were acquired on a FACS Aria II flow cytometer ( BD , USA ) and analyzed with FlowJo software ( Tree Star , San Carlos , CA , USA ) . Lymphocytes were gated based on their forward scattering and side scattering parameters , followed by the use of forward scatter area vs . forward scatter height dot-plot for doublet discrimination . The subsequent analyses were performed on viable cells ( FV510— ) ( S1A Fig ) One million PBMCs were stained with Fixable Viability 510 ( FV510 ) according to the manufacturer’s instructions . Then , these PBMCs were stained with anti-CD4 PerCP , anti-CD8 PerCP , anti-CD45RA FITC , anti-CD127 Alexa Fluor 647 and anti-CD132 PE for 30 min on ice . Then , cells were washed and resuspended in PBS containing 2% paraformaldehyde ( PFA ) . Memory and effector T cells were gated according to CD45RA and CD127 expression in CD4+ and CD8+ T cells ( S1A Fig ) . IL-7-induced STAT5 phosphorylation , as well as Bcl-2 and CD25 expression in PBMCs , was determined as previously described [24] . Briefly , 2x106 PBMCs were cultured overnight in serum-free medium ( AIM-V , Invitrogen , Carlsbad , USA ) followed by a 15 min incubation with 100 ng/mL recombinant human IL-7 ( rhIL-7 , Abcam ) for the STAT5 phosphorylation assay or cultured for two days in complete RPMI medium with or without 10 ng/mL of rhIL-7 for Bcl-2 and CD25 expression analysis at 37°C , 5% CO2 . Then , cells were labeled with anti-CD4 APC , anti-CD8 PerCP/FITC or anti-CD25 FITC on ice and immediately fixed . Cells were permeabilized , and intracellular staining with anti-phosphorylated STAT5 ( pSTAT5 ) PE or anti-Bcl-2 PE was performed . IL-7-induced STAT5 phosphorylation ( Δ % pSTAT5+ ) for CD4+ and CD8+ T cells was determined by calculating the difference in percentages of pSTAT5+ cells between IL-7-stimulated and unstimulated samples . Since Bcl-2 is constitutively expressed , the change in the mean fluorescence intensity ( MFI ) was used to evaluate induction above basal levels . The induction of Bcl-2 and CD25 expression was measured by subtracting the MFI for Bcl-2 , or the percentages of CD25-expressing T cells in unstimulated cultures , from those in IL-7-stimulated cultures . For sixteen to twenty hours , 4×106 PBMCs were incubated with 15 μg/mL of lysate preparation [10] or media alone plus 1 μg/mL CD28/CD49d ( BD Biosciences ) at 37°C in a CO2 incubator . Brefeldin A ( 10 μg/mL; Sigma ) was added for the last 5 h of incubation , as previously described [11 , 12] . Stimulation with Staphylococcal enterotoxin B ( SEB ) ( 1 μg/mL; Sigma Aldrich ) served as a positive control . Cells were stained with FV510 and anti-CD4 PB , anti-CD127 Alexa Fluor 647 , anti-CD132 PE and anti-PD-1 PE-Cy7 monoclonal antibodies ( BD Bioscience ) for 30 min on ice followed by fixation and permeabilization for intracellular staining with anti-IFN-γ ( AF488 ) ( BD , Bioscience ) . CD127 , CD132 and PD-1 expression levels were quantified in IFN-γ-producing and non-IFN-γ-producing CD4+ T cells . Serum levels of IL-7 , IL-9 , IL-21and IL-27 ( Abcam ) and sCD127 ( MyBioSource ) were measured in duplicate using ELISA kits . Inflammatory cytokines including IL-1β , IL-6 , IL-8 , IL-9 , IL-10 , IL-12 and TNF-α , were measured by Cytometric Bead Array ( CBA , BD Biosciences ) according to the manufacturer’s instructions . Cells ( 3×106 ) were plated in 24-well cell-culture plates in a total volume of 1 . 5 mL complete RPMI with 10% FBS and incubated for 10 days at 37°C , with 5% CO2 atmosphere , 99% humidity , along with T . cruzi lysate ( 10 μg/mL final concentration ) , in the presence or absence of rhIL-7 or rhIL-27 ( 25 ng/mL final concentration ) . On day three , 20 IU/mL of IL-2 ( BioLegend ) was added to each well . After 10 days , PBMCs were harvested , washed , and resuspended in complete RPMI medium . Cultured PBMCs were tested for the presence of IFN-γ-secreting T cells in response to T . cruzi lysate using ELISPOT assay , by seeding 2×105 cultured PBMCs/well along with 1×105 autologous unstimulated cryopreserved PBMCs/well as antigen presenting cells . The normality of data was evaluated by the Shapiro-Wilk test . The results are given as medians and interquartile ranges . Differences between IFN-γ producers and IFN-γ nonproducers of each clinical group and the uninfected group were determined by analysis of variance ( ANOVA ) followed by Bonferroni’s or Dunn’s multiple comparisons test , as appropriate according to the normality of data , or by a test for lineal trend . The correlations between variables was determined by a Spearman or Pearson test , as appropriate and were considered significant when p≤0 . 05 . Univariate analysis defining the ability to secrete IFN-γ in response to T . cruzi antigen stimulation as the outcome was evaluated with the Wilcoxon rank sum test or the two-sample t-test , as appropriate , for continuous variables . All parameters in the univariate analysis with a p<0 . 05 and two variables with p<0 . 1 were transformed into log scale . Correlation analyses between variables were performed to incorporate each variable into one out of six logistic regression models for the multivariate analysis . We used odds ratios with 95% confidence intervals for the logistic regression analysis .
Subjects with measurable IFN-γ-producing T cells in response to T . cruzi lysate ( i . e . , IFN-γ producers , Fig 1A ) exhibited lower frequencies of CD127+CD132+ cells and higher frequencies of CD127—CD132+ in CD4+ cells ( Fig 1B and 1C; S1C Fig ) and CD8+ ( Fig 1D and 1E , S1D Fig ) memory ( CD45RA— ) T cells compared with non-IFN-γ-producers and uninfected subjects . Memory CD4+ and CD8+ T cells with downregulated CD127 diminished with the intensification of disease severity in IFN-γ producers , and CD127+CD132+ increased ( S2 Fig , test for linear trend between medians ) . CD45RA+ T cells are primarily comprised of naïve and terminally differentiated effector T cells [27 , 28] . We measured the proportion of recent thymic emigrant cells ( RTE ) ( i . e . , CD127+CD132— ) and terminally differentiated T cells ( TTE ) ( i . e . , CD127—CD132+ ) [29–30] among CD45RA+ T cells based on the expression of CD127 and CD132 . IFN-γ producers in patients with cardiac disease ( i . e . , subjects in the G1 , G2 and G3 clinical groups ) exhibited lower frequencies of RTE in the CD4+ and CD8+ T-cell compartments compared with IFN-γ nonproducers and uninfected subjects ( Fig 2A and 2B ) . IFN-γ producers and IFN-γ nonproducers in G0 patients exhibited equally decreased values of CD4+ RTE compared to uninfected subjects ( Fig 2A ) . In contrast , CD4+CD45RA+cells in T . cruzi-infected subjects were enriched in the TTE cells compared with uninfected subjects , regardless of the ability of T cells to respond to T . cruzi antigens and the clinical status ( Fig 3A and S1E Fig ) . Only IFN-γ producers and IFN-γ nonproducers with no signs of cardiac disease exhibited equally significantly increased levels of TTE cells among CD8+ T cells ( Fig 3B ) compared with uninfected subjects . TTE levels were also slightly increased in patients with cardiac disease ( Fig 3B ) . We examined whether IFN-γ-producing T cells in response to T . cruzi exhibited distinct expression of CD127 and CD132 chains of the IL-7R and the inhibitory receptor PD-1 compared with IFN-γ nonproducing T cells . IFN-γ-producing CD4+ T cells in response to a T . cruzi lysate were enriched in CD127—CD132+ T cells compared with IFN-γ nonproducing CD4+ T cells ( Fig 4A–4C , S3 Fig ) . IFN-γ producing CD4+ T cells exhibited increased PD-1 expression compared with IFN-γ nonproducing cells ( Fig 4D , S3 Fig ) . We evaluated the association between IFN-γ secretion capacity in response to T . cruzi and the function of IL-7 receptor as measured using STAT5 phosphorylation and CD25 and Bcl-2 expression in T cells following stimulation with rhIL-7 . IFN-γ nonproducers with cardiac disease ( i . e . , subjects of the G1 , G2 and G3 clinical groups ) exhibited lower frequencies of phosphorylated STAT5+ ( pSTAT5 ) in CD4+ T cells than IFN-γ producers and uninfected subjects , in response to IL-7 ( Fig 5A , S4A and S4B Fig ) . CD8+pSTAT5+ T cells were also lower in IFN-γ nonproducers irrespective of clinical status , but this difference was not statistically significant ( Fig 5B , S4C and S4D Fig ) . The lower functional capacity of the IL-7R in T cells of IFN-γ nonproducers in response to IL-7 was associated with increased basal levels of pSTAT5 ( Fig 5C and 5D , S4B and S4D Fig ) compared with IFN-γ producers and uninfected subjects . Decreased CD25 upregulation in response to IL-7 in CD4+ T cells was observed in IFN-γ nonproducers with severe cardiomyopathy compared with IFN-γ producers and uninfected subjects ( Fig 6A ) . Most patients exhibited unaltered basal CD25 expression in T CD4+ T cells ( Fig 6C ) . The basal expression of the activation marker CD25 in CD8+ T cells was increased in IFN-γ nonproducers with cardiac disease compared with IFN-γ producers and uninfected subjects ( Fig 6D ) , but the upregulation of CD25 after IL-7 stimulation was not drastically impaired in subjects who lacked IFN-γ-producing cells ( Fig 6B ) . A slight decrease in Bcl-2 expression in response to IL-7 along with decreased basal Bcl-2 expression levels was found in CD4+ and CD8+ T cells of IFN-γ nonproducers compared with IFN-γ producers and uninfected subjects ( Fig 6E–6H ) . We investigated whether increased serum levels of IL-7 observed in patients with chronic Chagas disease [24] were associated with altered levels of the soluble form of the IL-7R ( sCD127 ) and IFN-γ production . A lower sCD127 concentration was found in IFN-γ producers without signs of cardiac dysfunction than IFN-γ nonproducers and uninfected controls ( Fig 7A ) with a strong inverse correlation between sCD127 levels and the number of IFN-γ-producing cells ( Fig 7B ) . In contrast , no differences were found in IL-7 levels between IFN-γ nonproducers and IFN-γ producers , and a weak inverse correlation was found between IL-7 and sCD127 serum levels ( Fig 7C and 7D ) . Increased basal levels of T cells expressing phosphorylated STAT5 in IFN-γ nonproducers led us to hypothesize that other cytokines signaling through STAT5 , including IL-9 , IL-21 and IL-27 , and inflammatory cytokines may be altered in these patients . IL-21 , IL-27 and IL-6 levels were not significantly different between IFN-γ producers and IFN-γ nonproducers but varied according to disease severity ( Fig 8A–8C , S5 Fig ) . Patients with a lower degree of cardiac dysfunction exhibited decreased levels of circulating IL-21 and IL-27 compared with patients with more severe disease and uninfected subjects ( S5A and S5B Fig ) . Patients with severe cardiomyopathy exhibited increased IL-6 levels compared with patients with less severe forms of the disease and uninfected subjects ( S5C Fig ) . IL-8 levels in patients with no signs of cardiac disease ( i . e . , the G0 group ) were higher in IFN-γ producers than IFN-γ nonproducers and both groups were higher than uninfected subjects ( Fig 8D ) . IL-6 levels positively correlated with the frequencies of CD4+ and CD8+ memory T cells with unmodulated CD127 ( Fig 8E ) . In contrast , IL-8 levels positively correlated with the frequencies of CD4+ T cells with downregulated CD127 ( Fig 8F ) and inversely associated with sCD127 levels ( Fig 8G ) . Serum concentrations of IL-1β , IL-9 , IL-10 , IL-12 , and TNF-α were undetectable in most of the evaluated patients ( S1 Table ) . Univariate and multivariate analyses were performed to identify independent parameters linked to the ability of T cells to produce IFN-γ in response to T . cruzi antigens regardless of the clinical status of the disease . Among T cells , a lower frequency of CD45RA—CD127+CD132+ cells , lower basal STAT5 phosphorylation and CD25 expression along with a higher frequency of CD45RA—CD127—CD132+ cells , higher frequency of phosphorylated STAT5 and CD25 expression after rhIL-7 stimulation , and higher basal Bcl-2 expression increased the likelihood of IFN-γ secretion in response to T . cruzi antigens ( Table 2 ) . In multivariate logistic regression analysis , the lower frequency of CD45RA—CD127+CD132+ cells , lower basal STAT5 phosphorylation and CD25 expression , along with higher basal Bcl-2 expression in T cells , were significant independent correlates of IFN-γ production in patients chronically infected with T . cruzi ( Table 3 ) . Notably , these associations were not uniform between CD4+ and CD8+ T cells ( Tables 2 and 3 ) . Correlation analyzes were performed to evaluate differences in the expression of the parameters evaluated according to the magnitude of T . cruzi-specific responses in the group of IFN-γ producers . The number of IFN-γ CPS in patients in the G0 and G1 groups positively correlated with the percentages of memory CD8+ T cells with unmodulated CD127 ( i . e . , CD45RA—CD127+CD132+ ) and inversely correlated with the percentages of memory CD8+ T cells with downregulated CD127 ( i . e . , CD45RA—CD127—CD132+ ) ( Table 4 ) . The number of IFN-γ CPS in G0 patients also positively associated with the basal expression of Bcl-2 in T cells and inversely associated with IL-7 serum concentration ( Table 4 ) . An inversely correlation was also observed between IFN-γ CPS and the basal percentages of CD8+pSTAT5+ in G1 patients ( Table 4 ) . IFN-γ CPS in patients in the G2 and G3 groups positively correlated with the percentages of TTE CD4+ and CD8+ T cells and inversely correlated with CD4+RTE ( Table 4 ) . We examined whether in vitro treatment of PBMCs with IL-7 or IL-27 enhanced T . cruzi-specific T cell responses in chronic Chagas disease patients . Addition of IL-7 or IL-27 in short-term cultures with T . cruzi antigens increased the number of IFN-γ-producing T cells in response to T . cruzi in IFN-γ producers but not IFN-γ nonproducers in patients with no signs of cardiac disease and patients with some degree of cardiac dysfunction . This increase was specific because no changes in the frequencies of T . cruzi-responsive IFN-γ-producing cells were observed in uninfected subjects ( Fig 9A and 9B ) . A significant expansion of IFN-γ-producing cells were obtained after a 10-day ex vivo culture with T . cruzi antigens following the addition of IL-7 or IL-27 in IFN-γ producers , and IFN-γ-producing cells remained unchanged in IFN-γ nonproducers ( Fig 10A and 10B ) . Notably , the fold increase in IFN-γ producers with cardiac dysfunction was significantly lower than IFN-γ producers without cardiac disease ( Fig 9A and 9B , right panels; Fig 10A and 10B , right panels ) .
The IL-7/IL-7R signaling pathway is necessary for memory T-cell formation , homeostasis , and self-renewal after resolution of an acute infection . In contrast , T cells fail to develop into self-renewing , antigen-independent memory T cells during chronic infections , which may be driven by the reduced expression of IL-7R on T cells or a failure to respond efficiently to this cytokine , with a subsequent loss of pathogen-specific T cells [31–36] . The present study demonstrated that the ability of T cells to secrete IFN-γ in response to T . cruzi was associated with a functional IL-7/IL-7R signaling pathway in memory T cells , high basal expression of Bcl-2 , low basal levels of activated T cells , and fewer inhibitory mechanisms on this axis , regardless the clinical stage of the disease . Grouping of patients according to the clinical stage revealed striking differences in the IL-7/IL-7R pathway in IFN-γ producers and IFN-γ nonproducers . The downregulation of IL-7Rα chain expression in memory T cells decayed with increasing disease severity in IFN-γ producers . Notably , the number of IFN-γ producing cells in IFN-γ producers with less severe forms of the disease was inversely associated with the frequency of memory T cells with downregulated CD127 , serum levels of IL-7 and the basal frequencies of pSTAT5+ T cells and positively associated with the basal expression of the anti-apoptotic molecule Bcl-2 . The number of IFN-γ producing cells in IFN-γ producers with severe cardiomyopathy was positively associated with TTE levels and inversely with the frequencies of RTE cells . These findings support the hypothesis that T . cruzi-specific T-cell responses are maintained at least partially via recruitment from RTE cells , which is consistent with the decreased frequencies of naïve T cells [11 , 37] and the low degree of differentiation in IFN-γ-producing cells [13] from individuals chronically infected with T . cruzi subjects . The increased recruitment of RTE in patients with more severe forms of the disease may be a compensatory mechanism to maintain parasite-specific T cells . Increased IL-7 levels may be responsible for the increased basal levels of pSTAT5+ and CD25+ T cells as part of these compensatory mechanisms to counteract the disruption in the IL-7/IL-7R axis . Fonseca et al . demonstrated high mRNA expression of IL-7 in heart tissues of patients with Chagas disease cardiomyopathy [38] . T . cruzi-infected subjects lacking parasite-specific T-cell responses and showing no signs of cardiac dysfunction still exhibited a functional signature of the IL-7/IL-7R pathway in T cells with STAT5 phosphorylation and CD25 expression in response to IL-7 . In contrast , IFN-γ nonproducers with severe cardiomyopathy exhibited an impaired capacity to respond to IL-7 . We confirmed that IFN- γ-producing cells in response to T . cruzi exhibited downregulated CD127 expression . IL-7R plays a role in IL-7 signaling during homeostasis , and it is rapidly internalized and recycled to the cell surface without changing T cell phenotype [39] . IL-7R regulation is also controlled at the transcriptional level , and IL-7 and other cytokines suppress IL-7R mRNA expression [40] . The role of the soluble form of IL-7R ( sCD127 ) , which is generated by cleavage or alternative splicing [41] , is controversial . Some reports demonstrated the sCD127 inhibits IL-7 activity [19 , 21 , 42] , and other studies demonstrated that IL-7 in complex with sCD127 delivered a more potent signal to cell-bound IL-7Rs or constituted a reservoir of IL-7 [43–44] . Increased serum levels of IL-7 in patients with less severe forms of Chagas disease may be induced by the need to maintain T cells during chronic infection because receptor-mediated uptake largely regulates IL-7 levels [45] . Increased IL-7 levels may inhibit IL-7R expression and the release of sCD127 , which was supported by the negative correlation between IL-7 and sCD127 . This scenario , sustained over time , may lead to desensitization of the pathway , which would result in losses of pathway regulation and T-cell responses , as part of the mechanism of immune exhaustion . This hypothesis was particularly demonstrable in IFN-γ producers with severe cardiomyopathy , who exhibit normal values of IL-7 and sCD127 but decreased responses to IL-7 by the inverse association between IFN-γ producing cells and circulating sCD127 . Our previous observations and other studies support the process of immune exhaustion and demonstrated that patients with no signs of cardiac dysfunction exhibited higher frequencies of circulating IFN-γ-producing cells compared with patients with severe cardiomyopathy [10 , 14; 46] . Several mechanisms underlying immune exhaustion in chronic Chagas disease were described , including the lack proliferative capacity and downregulation of CD28 and CD3ζ [47] , increased nitric oxide production concomitant with increased tyrosine nitration [48] and increased expression of inhibitory receptors in T cells [13–14; 49–50] . The present study demonstrated that T . cruzi-induced IFN-γ-producing cells expressed high levels of PD-1 , which is a primary inhibitory receptor associated with immune exhaustion [51–53] , in addition to CTLA-4 and LIR-1 [13] . Our data also demonstrated a positive correlation between IL-6 levels and T cells with unmodulated CD127 , which suggests that this inflammatory cytokine blocks T-cell responses to IL-7 , as found in HIV infection [22–23] . Notably , inflammatory cytokines and IL-7 induce the expression of exhaustion and senescence markers , PD-1 and CD57 , on T cells [23] . Several studies in subjects chronically infected with T . cruzi revealed that increased levels of IL-6 were associated with cardiac dysfunction [54–57] , which supports the hypothesis that sustained inflammation in the chronic phase of infection may also alter the homeostatic mechanisms of T-cell maintenance . IL-8 is a monocyte-derived cytokine that is upregulated by IL-7 [58] , and it was increased in IFN-γ producers with no signs of cardiac dysfunction and inversely associated with sCD127 . These results suggest that IL-7 also plays a critical role in the regulation of macrophage cytokine expression . However , other pathways besides IL-7/IL-7R may be involved in the maintenance of T-cell responses . We observed that subjects with a functional IL-7 axis lacked T . cruzi-responsive T cells . The IL-21 and IL-27 levels , which also signal via STAT5 and induce T-cell proliferation and effector function [59–61] , were also altered in individuals chronically infected with T . cruzi . IL-21 and IL-27 appeared to be consumed in IFN-γ-producers with less severe forms of the disease , which may be another mechanism for the maintenance of T-cell responses . Exogenous addition of IL-7 or IL-27 did not rescue T . cruzi-responsive IFN-γ-producing T cells in patients with undetectable IFN-γ-producing cells , which suggests that T . cruzi-specific T cells are present but the JAK/STAT pathway is dysfunctional or that T . cruzi-specific T cells were already depleted from the circulation . The higher fold-increase in T . cruzi-specific T cells in IFN-γ responders with no signs of cardiac dysfunction compared with patients with severe disease further supports the hypothesis that impairment in T-cell function is a gradual process . Therefore , our findings provide new insights into the regulation of T cell-mediated immunity against T . cruzi-infection and may aid the design of a vaccine against T . cruzi . However , it is not possible to ascertain whether the impairment of T . cruzi-specific T-cell responses is a cause or a consequence of disease progression . Another limitation of the present study is that the T-cell responses focused solely on responses elicited by a T . cruzi lysate , for which the bulk of the response is CD4+ . Unfortunately , CD8+ T-cell responses elicited by HLA-restricted T . cruzi-derived epitopes are of very low frequencies [12] . Impaired T-cell responses in chronic Chagas disease are specific for T . cruzi , but these alterations in the IL-7/IL-7R pathway are another example of how this chronic infection affects the general status of the host immune system . Taken together , the present study demonstrated that defective signaling and regulatory mechanisms in the IL-7/IL-7R axis during the chronic phase of Chagas disease may affect the maintenance of parasite-specific IFN-γ-producing cells .
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Mechanisms of acquired immune response against Trypanosoma cruzi antigens include both humoral and cellular components that might be critical in a chronic infection . Through a vast number of studies , several groups have postulated that , similar to other chronic infections , T-cell responses in chronic Trypanosoma cruzi infection are driven to exhaustion . Alterations in T-cell signaling pathways have emerged as one of the mechanisms of immune exhaustion . Here , we investigated whether the ability of T cells to secrete IFN-γ in response to T . cruzi was linked to the expression and function of the IL-7 receptor and the cytokines involved in regulating this axis in patients with different clinical forms of chronic Chagas disease . This study showed that the ability of T cells to secrete IFN-γ in response to T . cruzi is linked to an efficient modulation and function of IL-7R and low levels of inflammatory cytokines . Low IFN-γ-ELISPOT responses could not be reverted by in vitro treatment with IL-7 . These findings contribute to our understanding of the long-term consequences of T . cruzi-infection and might be useful to delineate novel therapeutic strategies .
|
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2018
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Trypanosoma cruzi-specific IFN-γ-producing cells in chronic Chagas disease associate with a functional IL-7/IL-7R axis
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Macrophages ( MØ ) and mononuclear phagocytes are major targets of infection by dengue virus ( DV ) , a mosquito-borne flavivirus that can cause haemorrhagic fever in humans . To our knowledge , we show for the first time that the MØ mannose receptor ( MR ) binds to all four serotypes of DV and specifically to the envelope glycoprotein . Glycan analysis , ELISA , and blot overlay assays demonstrate that MR binds via its carbohydrate recognition domains to mosquito and human cell–produced DV antigen . This binding is abrogated by deglycosylation of the DV envelope glycoprotein . Surface expression of recombinant MR on NIH3T3 cells confers DV binding . Furthermore , DV infection of primary human MØ can be blocked by anti-MR antibodies . MR is a prototypic marker of alternatively activated MØ , and pre-treatment of human monocytes or MØ with type 2 cytokines ( IL-4 or IL-13 ) enhances their susceptibility to productive DV infection . Our findings indicate a new functional role for the MR in DV infection .
Dengue is the most prevalent mosquito-borne viral disease worldwide and in the past 40 years has undergone a global resurgence such that almost half the world's population are currently living at risk in dengue-endemic areas [1] . There is a spectrum of disease severity following dengue virus ( DV ) infection that in its more severe forms results in dengue haemorrhagic fever ( DHF ) and shock syndrome . The resultant morbidity and mortality , and subsequent considerable economic burden , make the development of a safe and effective vaccine imperative . DV pathogenesis is complex and multifactorial [2] , and macrophages ( MØ ) are thought to play an important role in disease both as primary targets of viral infection and as a source of immunomodulatory cytokines . The four serotypes of DV ( DV1-DV4 ) bind to a number of opsonic and non-opsonic receptors on cells of the mononuclear phagocyte lineage including DC-SIGN [3 , 4] , glycosaminoglycans [5] , and when in complex with specific antibody , Fc and complement receptors [6] . MR is a multi-domain protein that is composed of a cysteine-rich ( CR ) domain which has lectin activity and binds to sulphated sugars , a fibronectin type-II ( FNII ) domain that mediates binding to collagen [7] and eight C-type-lectin-like domains ( or carbohydrate-recognition domains , CRD ) . The fourth CRD mediates most of the specificity of these domains for glycans terminating in mannose , fucose and N-acetyl glucosamine . In addition to many endogenous ligands , MR binds to bacteria ( e . g . Mycobacterium tuberculosis ) , fungi ( e . g . Pneumocystis carinii ) and viruses ( e . g . HIV ) . MR is constitutively internalized from the plasma membrane by clathrin-mediated endocytosis and recycled back to the cell surface . Intracellular targeting is mediated by a tyrosine-based motif in the cytoplasmic tail , although it contains no recognised signalling motifs ( for a comprehensive review see [8] ) . DC-SIGN , a lectin with similar sugar specificity to that of the MR , can mediate DV attachment to dendritic cells [3 , 4] . Even though DV binding to DC-SIGN on these cells is important for attachment , DC-SIGN-mediated viral endocytosis is not required for DV entry [9] . While the immune response to viruses is classically described as Th1 mediated , the literature in the case of DV suggests that this may not be absolute . IgE ( characteristic of a Th2 environment ) has recently been shown to be elevated in the acute stages of DV infection [10] and at defervesence [11] . A microarray study of whole blood gene expression during secondary DV infection has shown early upregulation of IL-13 transcripts in acute samples from DHF patients [12] . This suggests that a Th2 response may be occurring in patients at some stages of infection . Indeed , studies of IL-12 , IL-13 and TGFβ cytokine levels in DHF patients suggest that the DV response shifts from a Th1-dominant response to a Th2-biased response during disease progression [13 , 14] . The immune responses in infants/neonates differ qualitatively from those of adults , with the immature immune system having a bias towards Th2 rather than Th1 immune responses , presenting a particularly relevant challenge for pediatric DV vaccination [15] . MØ are profoundly influenced by the cytokine profile in their immediate environment . Functionally diverse subsets of alternatively or classically activated mononuclear phagocytes can develop in an immune response . Exposure of MØ to IL-4 or IL-13 elicits an ‘alternate type of activation’ , as opposed to the classical activation induced by IFNγ [16] . These alternatively activated cells have been implicated to have regulatory functions in cellular and humoral immunity by affecting the balance of pro- and anti-inflammatory reactions [17] . Protein expression studies and transcriptional profiling have shown that IL-4 induces upregulation of several receptors , including the mannose receptor ( MR ) on monocytes and MØ [18 , 19] . In this study we show that MR binds to DV grown in mosquito cells and to recombinant mammalian cell–produced DV envelope glycoprotein . A recombinant MR fusion protein ( CRD4–7-Fc ) was shown to recognize DV envelope ( E ) protein in ELISA and blot overlays , and binding was inhibited by mannose , fucose and EDTA . The presence of MR on transfected cells is sufficient to confer DV binding . DV infection of MØ was blocked by antibodies against the human MR suggesting that it is a novel functional receptor contributing to DV infection . We also show that pre-treatment of primary human monocytes with Th2 cytokines ( IL-4/IL-13 ) , which upregulate MR expression , increases their susceptibility to DV infection in vitro . Better understanding of receptor/s and entry pathways mediating infection in humans could be crucial to the design and safety of a dengue vaccine .
The ability of MR to bind DV antigen produced in mosquito ( C6/36 ) and human ( 293T ) cells was examined . ELISA wells were coated with semi-purified C6/36-grown DV2 or recombinant soluble E ( sE ) protein produced in the endothelial kidney cell line 293T ( see below for characterisation of this reagent ) and probed with the entire extracellular region of the murine MR expressed with an HA tag or recombinant truncated forms of the murine MR with human Fc tags . MR-HA bound to purified mosquito cell–derived DV2 ( Figure 1A ) and to sE ( Figure 1B ) , and binding was mediated specifically by CRD4–7 and not the CR or the FNII domains . Binding of CRD4–7-Fc to both C6/36-grown DV2 ( Figure 1C ) and sE ( Figure 1D ) was inhibited by 2mM d-mannose , 2mM l-fucose , and to a lesser extent 2mM d-galactose , and depended on the presence of divalent cations . This is consistent with the known sugar specificity and calcium dependence of the MR CRD4–7 domains . We extended the study by investigating the binding of CRD4–7-Fc to mosquito cell–derived virus of the other 3 DV serotypes ( DV1 , DV3 and DV4 ) and to mammalian ( Vero ) cell–grown DV2 by ELISA . CRD4–7-Fc bound to all four serotypes of DV in a dose-dependent manner ( Figure 1E ) . Binding of the CRD4–7-Fc correlated with the different coating levels of these antigens as determined with an antibody against all 4 DV serotypes ( Figure 1F ) . In addition , we tested whether CRD4–7-Fc and sMR-HA interacted with other flaviviruses . ELISA data showed that both CRD4–7-Fc and MR-HA both bound to Japanese encephalitis virus ( inactivated vaccine antigen ) and tick-borne encephalitis virus ( inactivated , mouse brain-grown ) ( data not shown ) . The specificity of MR CRD4–7-Fc binding to DV sE was further examined by blot overlay . CRD4–7 bound exclusively to a single band that migrated at 52 kDa ( Figure 2A , left-hand lane ) . This band was recognized by the anti-E protein antibody , 3H5 , when blots were stripped and reprobed ( Figure 2B , left-hand lane ) , confirming that CRD4–7 binds to DV E-protein . CRD4–7 did not bind sE deglycosylated with peptide:N-glycosidase F ( PNGaseF ) ( Figure 2A , right-hand lane ) , in contrast to 3H5 that bound to both the native and the deglycosylated forms of the protein ( Figure 2B ) , indicating that CRD4–7 binds specifically to N-linked glycans on sE . Binding of CRD4–7 to sE was inhibited by the presence of either 2mM d-mannose , 2mM l-fucose or 20mM EDTA . The presence of sE on these blots was confirmed by washing blots and reprobing in the absence of inhibitors ( data not shown ) . In addition , CRD4–7-Fc did not bind to unglycosylated domain III of DV E protein produced in bacteria in ELISA experiments ( data not shown ) . Given the interaction of DV with MR described above , it was important to characterise the glycans on the human cell–produced sE , especially since we are unaware of any similar analysis in the literature . This reagent is valuable , as its glycan modifications may more closely resemble the patterns found on viral particles produced during infection in the human host compared with baculovirus and E . coli produced molecules . DV E protein has two conserved N-linked glycosylation sites at Asn-67 and Asn-153 . Deglycosylation of sE by PNGaseF led to a shift in apparent mobility on SDS-PAGE from 52 kDa to 46 kDa ( the predicted molecular weight of sE is 45 kDa ) , indicating that the protein carries N-linked glycan modifications ( Figure 3A and 3B ) . Conversely , digestion of sE by endoglycosidase H , which cleaves high mannose oligosaccharides , did not result in a mobility shift on SDS-PAGE ( Figure 3B ) . RNAse B was deglycosylated by both enzymes under corresponding reaction conditions as a positive control ( data not shown ) . A more specific glycan analysis by sequential digestion with sialidase , fucosidase and mannosidases ( Figure 3C ) showed approximately 40% of the glycoforms were sialylated and 25% contained α1–3 , 4 linked outer arm fucose . There was no evidence of terminal mannose . The glycans were also processed by weak anion exchange ( WAX ) HPLC before and after sialidase digestion . There were charged glycoforms remaining after sialidase digestion which may be sulphated ( data not shown ) . To further evaluate MR as a potential DV receptor , we examined binding of DV to human MR-transfected 3T3 cells ( 3T3 . hMR ) . As DC-SIGN has previously been shown to be an important attachment receptor for DV , DV binding to 3T3 . hMR cells was compared with binding to 3T3 cells transfected with DC-SIGN . Initially we confirmed expression of the respective receptors on the 3T3 cell surface ( Figure 4A and 4B ) . To assess virus binding , cells were incubated with mosquito cell–grown DV , unbound virus was washed away and DV bound to the cells was detected with the anti-E protein antibody , 3H5 ( Figure 4C–4E ) . Histograms show a clear shift in fluorescent intensity indicating DV binding to cells transfected with either human MR or DC-SIGN . Similar data were observed using anti-pre-membrane glycoprotein ( prM ) antibody ( 2H2; data not shown ) to detect bound DV . Thus , surface expression of human MR on transfected 3T3 cells was sufficient to confer DV binding . A human primary cell culture assay system was established in which we examined the functional role of MR and the effects of cytokines on the susceptibility of mononuclear phagocytes to DV infection . Monocytes were purified from human PBMC fractions and cultured for 2 or 7 d to prepare monocyte-derived MØ ( MDMØ ) or differentiated into monocyte-derived dendritic cells ( MDDC ) prior to infection . The percentage of cells infected was quantified by microscopy by staining nuclei with DAPI and viral antigen with the anti-DV2 E protein monoclonal antibody , 3H5 . MDDC were more susceptible to infection by DV ( percent infected DC: 12 . 3% +/− 7 . 1 ) compared with either 2 or 7 d differentiated MDMØ in the absence of added cytokine ( percent infected 2 d MDMØ: 1 . 8% +/− 0 . 7; 7 day MDMØ: 1 . 1% +/− 0 . 3 ) from the same donor ( 3 donors ) , using a multiplicity of infection of 0 . 4 . The presence of DV non-structural protein in infected 2 d MDMØ using anti-NS1 monoclonal antibodies ( obtained from Eva Harris ) suggested that active viral replication and de novo viral protein production , rather than mere uptake of viral antigen , was occurring ( data not shown ) . Plaque assays on supernatants from infected primary 2 or 7 d MDMØ cell cultures confirmed the occurrence of productive infection , with viral titres increasing over time and reaching 104 pfu/ml in cell supernatants 2 d after infection of 6 × 106 cells . Considering that MDDC , which were grown in an IL-4/GM-CSF cytokine cocktail , were infected to a higher degree than the MDMØ , various cytokines were tested for their ability to alter the susceptibility of MDMØ to DV infection by pre-incubation with monocytes for 48 h prior to DV infection ( see Table S1 for full list and concentrations ) . Only the type 2 cytokines IL-4 and IL-13 , which utilize a common receptor chain , had a substantial effect on the susceptibility of MDMØ to DV infection in culture ( Figure 5A and 5B ) , increasing the percentage of infected cells from around 1% to between 4% and 21% ( Figure 5C ) . An almost 6-fold average increase in the percentage of cells infected was observed for 8 independent donors ( p = 0 . 005 ) . IL-4 may also contribute to the enhanced degree of DV infection of MDDC , as GM-CSF alone did not increase the level of infection of MDMØ ( Table S1 ) . Neither the age of the cells nor the length of treatment altered the enhancing effects of IL-4 . Enhanced susceptibility to DV infection was seen when monocytes were allowed to differentiate into MØ over 7 d , and then treated with IL-4 for 48 h prior to DV infection ( day 9 MDMØ , Figure 5D ) . An 8-fold increase in the percentage of infected cells was observed for 3 independent donors ( p = 0 . 03 ) . Alternatively , monocytes treated for 2–7 d with IL-4 prior to infection with DV all showed similar heightened susceptibility ( data not shown ) . This suggested that the increased number of infected cells was not due to maturation of the cells . A dose response experiment indicated that the enhancement of DV infection of MDMØ could be achieved with as little as 1 . 5ng/ml IL-4 ( data not shown ) . The interaction of DV with primary cells appears to be multifactorial , as we found variability between donors and receptor expression level . Surface expression of both MR and DC-SIGN is upregulated on MDMØ by IL-4 treatment ( Figure 6A and 6B ) , consistent with previous data for DC-SIGN on human monocytes [20] and for MR on primary mouse MØ [19 , 21] . While the fold increase in surface MR levels following IL-4 treatment ( 4 . 1–fold +/−1 . 3 ) parallels the fold increase in the percent of infected cells ( 4 . 6-fold +/− 1 . 4 ) , from analysis of a limited number of donors no clear correlation can be drawn between the two . This is also true of the relationship between upregulation of surface DC-SIGN expression ( 7 . 7-fold +/− 0 . 9 ) on the same IL-4-treated cells and increase in the percent of infected cells ( mean of 4 donors; data not shown ) . The functional role of MR in DV infection of primary human MØ was investigated using a polyclonal anti-MR antibody to block infection . This was examined in the IL-4-treated MDMØ since these cells showed the highest rate of DV infection . Anti-MR antibody significantly blocked DV infection of IL-4-treated MDMØ ( p = 0 . 008 ) in all donors tested ( 6 donors; Figure 7A shows data from one representative donor ) . Normal goat serum control did not inhibit infection , suggesting that the MR may be a new functional receptor contributing to DV infection of human MØ . Production of infectious virus ( pfu/ml ) by these cells at 2 d post infection was reduced by 60%–95% with either mannan or anti-human MR antibody ( Figure 7C ) , indicating that attachment and/or entry via this receptor is required for productive infection . The goat anti-human MR antibody blocked mannosylated BSA-FITC binding to both IL-4-treated MDMØ and 3T3 cells transfected with human MR ( Figure S1 ) . Mannan , which blocks MR , DC-SIGN and other receptors with specificity for mannose , also blocked DV infection of MDMØ ( Figure 7A and 7B ) . We tested the ability of several different anti-DC-SIGN antibodies to block infection of MDMØ . Antibodies against DC-SIGN have been shown by others to block DV infection of DC [3 , 4] . Interestingly , some anti-DC-SIGN monoclonal antibodies also blocked DV infection of IL-4-treated MDMØ ( Figure 7B ) , and to a similar degree to that observed blocking with mannan or anti-MR antibodies .
We have shown for the first time that MR is a functional receptor for DV infection of human MØ . Binding of the MR to DV surface glycoproteins was mediated via the lectin activity of the CRD binding to glycans on the DV E protein . Gain of function binding data showed that surface expression of human MR on 3T3 cells was sufficient to confer DV binding . Antibodies specific for the MR significantly blocked both infection of MDMØ and the production of infectious virus in these cells . FACS analysis showed surface MR expression increased over 4-fold following IL-4 treatment of monocytes , corresponding with a similar fold increase in percent infected cells . Thus , the MR provides a potential link explaining the increase in MØ permissiveness to DV when stimulated by IL-4 or IL-13 . We hypothesise that the MR may play a role in at least one of the stages of DV infection of human MØ . The first stage of virus entry into a cell is attachment , and the mechanism by which MR enhances the efficiency of DV entry could be by increasing virus attachment , as suggested for DC-SIGN and DV . As the MR can be internalized by macropinocytosis , pinocytosis , receptor-mediated endocytosis and phagocytosis , its role could also be in increasing the rate of DV internalisation , the second stage of virus entry . Analogous to the proposed mechanism of Fc-receptor enhancement of DV-antibody complex attachment/uptake in antibody-dependent enhancement , the presence of receptors such as MR or DC-SIGN , which enhance virus attachment/entry , may play a significant role in vivo . Anti-DC-SIGN monoclonal antibodies were also able to block DV infection of MDMØ , as has been seen previously in MDDC . The anti-DC-SIGN antibodies that blocked DV infection of IL-4 treated MDMØ to the greatest degree ( DC28 and 120612 ) are known to cross react with DC-SIGNR . We hypothesise that the different specificities of the monoclonal antibodies may explain why some but not all block DV infection of MØ . These findings corroborate data by Tassaneetrithep et al . [4] examining blocking of DV infection of THP-1 cells transfected with either DC-SIGN or DC-SIGNR , where the same two anti-DC-SIGN antibodies ( but not others ) blocked infection . The observation that antibodies to either MR or DC-SIGN can inhibit infection to such an extensive degree suggests that DV is likely to be using both MR and DC-SIGN for entry on cells that express both . It is difficult to assess the relative ( and possibly differing ) roles of MR and DC-SIGN in DV infection of primary MØ or DC that express both; however , our observation that expression of MR on 3T3 cells confers DV binding suggests that MR can mediate direct recognition of DV by myeloid cells . DC-SIGN has been suggested to function in DV attachment rather than internalisation , raising the possibility of another receptor being involved in the internalisation step of infection . At this stage the simplest hypothesis that explains the above findings in primary cells expressing both MR and DC-SIGN is that DC-SIGN is required for DV attachment and MR for internalisation . Alternatively , these receptors may function simultaneously and even co-operatively throughout the infectious process . Further studies on the cellular expression , location in the cell and ligand specificity of these receptors may provide clues as to the absolute roles they play in DV infection . MR is ideally poised to act as a DV entry receptor given its constitutive recycling to the cell surface and ability to promote ligand internalisation via both endocytic and phagocytic pathways . While DC-SIGN mainly localises to the plasma membrane [22] , ∼85% of cellular MR is located within the endocytic pathway [8] as a large intracellular receptor pool from which internalised receptor is rapidly replaced . In addition to expression on MØ , certain subpopulations of DC , including dermal DC in human skin , express the MR [22] , in which case it may be involved in antigen delivery for presentation [23 , 24] . MR is expressed on MØ as well as cells lining venous sinuses in human spleen [25] and is therefore well located to act as a receptor for DV replication in these physiologically relevant target cells . Here it may have roles in clearance or adhesion , but also potentially in vascular leakage . A soluble form of MR has been found to be abundant in mouse [26] and human plasma ( unpublished observations , L . Martinez-Pomares ) and its CR domain has targets in the spleen . Binding of ligands by soluble MR can result in the transport of MR ligands to the B cell follicles , which may lead to clearance or enhanced presentation of viral antigen depending on TLR co-stimulation . Soluble MR could also potentially play a role in protection of virus from complement activation . Further elucidation of the exact role of MR in the attachment/entry/infectivity of DV will be a fundamental step in gaining a better understanding of DV pathogenesis . MR and DC-SIGN both contain lectin domains , but differ distinctly in terms of ligand specificity , with MR binding terminal mannose , fucose and N-acetyl glucosamine , and DC-SIGN binding mannose within high-mannose oligosaccharides and fucosylated glycans [8 , 27 , 28] . Glycosylation endows unique properties to glycoproteins and can play a significant role in immunity . Recent observations using DV mutants in one or both of the N-linked glycosylation motifs have shown that N-linked glycosylation at Asn-67 is required for virus growth in mammalian cells [29] . In addition , new data by Mondotte and colleagues showed that DV lacking carbohydrate at Asn-67 had reduced capacity to infect MDDC [30] . This , combined with the observation that MDDC express both DC-SIGN and MR [22] , and our demonstration that the presence of MR alone is sufficient to confer DV binding to transfected cells , suggest that glycosylation at Asn-67 may be relevant for mediating MR binding , in addition to that of DC-SIGN . While the sE may contain sulphated glycans , it was not bound by the CR fusion protein ( Figure 1B ) , and so the CR domain of the MR is not expected to contribute to the binding of MR to DV . Our deglycosylation studies on sE show that it bears either complex or hybrid N-linked glycans . Terminal fucose is a reported ligand of MR and as such is the likely ligand on this source of DV antigen . A more detailed glycan analysis of DV grown in human MØ will be an important challenge for the future . We expanded our study of the interaction of MR with DV by demonstrating binding of CRD4-7-Fc to all four serotypes of DV . Differences in glycosylation between DV serotypes , and more broadly between different flaviviruses , may be relevant for interaction with lectin receptors such as MR and DC-SIGN . We showed that MR can bind in ELISA to Japanese encephalitis virus and tick-borne encephalitis virus , both of which are reported to have glycosylated envelope proteins . In this report we have shown that the type 2 cytokines IL-4 and IL-13 enhance the susceptibility of MDMØ to DV infection . The mechanisms resulting in increased infection in response to IL-4 and IL-13 are unknown . Analysis of IL-4-treated human monocytes showed that these cells are characterised by the overexpression and enhanced function of several endocytic receptors , including scavenger and C-type lectin receptors [18] . Functional ligand binding and transcriptional profiling studies [19 , 31] reveal that the MR is markedly upregulated on alternatively activated MØ . A number of important conditions result in polarised activation of MØ phenotype . Our findings make it highly relevant to understand the clinical and epidemiological significance of a Th2 environment on DV infection , pathogenesis , and enhancement and in the development of a desirable vaccine response . It will be of great interest to examine the broader context in which dengue pathogenesis occurs by considering the effects on DV disease of settings that induce Th2 cytokines , including co-infection with parasites , the presence of immune complexes and allergy ( e . g . asthma ) . There are few studies into the implications of co-incidence of parasitic infections or allergic disease and dengue infection . Guzman and colleagues showed significantly enhanced replication of DV in PBMC from asthmatic patients compared with controls [32] . During secondary dengue infection when anti-DV antibody is present , the study of DV interactions with MØ stimulated through FcγR ligation ( ‘type II' activated MØ ) , may be of particular relevance . Given the risk of antibody-dependent enhancement to vaccine trials , a Th2 environment ( which is known to influence humoral responses and FcR expression [33 , 34] ) may be a contributing factor to vaccine efficacy and DV pathogenesis and as such will require further examination .
16681 and New Guinea C ( NGC ) strains of DV2 ( both gifts from E . Gould , Oxford Centre for Ecology and Hydrology , UK ) were propagated in the Aedes albopictus–derived C6/36 cell line ( a gift from Armed Forces Research Institute of Medical Sciences , Thailand ) . Virus titres were obtained by plaque assay on LLC-MK2 monkey kidney cells ( a gift from Armed Forces Research Institute of Medical Sciences , Thailand ) , as described previously [35] . 16681 strain DV2 grown in Vero cells , precipitated with polyethylene glycol , purified on a sucrose gradient and inactivated with formaldehyde was from Biodesign . Hawaii strain DV1 , NGC strain DV2 , H-87 strain DV3 and H-241 strain DV4 grown in C6/36 cells were precipitated with 7% polyethylene glycol and inactivated with beta-propiolactone ( Biodesign ) . Human 293T-HEK cells ( ATCC ) were maintained in Dulbecco's modified Eagle's medium ( DMEM ) ( Invitrogen ) supplemented with 2mM glutamine , 0 . 1mg/ml streptomycin , 100U/ml penicillin and 10% heat inactivated fetal calf serum ( FCS ) . A stable NIH3T3 cell line expressing the human MR ( 3T3 . hMR ) was a gift from Gordon Brown ( University of Cape Town , South Africa ) and Philip Taylor ( University of Cardiff , UK ) , made using the protocol described previously [21] . Human MR was amplified from cDNA derived from human mRNA . High expressing clones were selected after limiting dilution in 96 well plates . Control 3T3 cells expressing the vector only were prepared in parallel . Production of stable NIH3T3 expressing the murine MR ( 3T3 . mMR ) has been described previously [21] . These NIH3T3 transfectants were maintained in DMEM ( Invitrogen ) supplemented as above and with 0 . 6mg/ml geneticin ( Invitrogen ) . NIH3T3 cells transfected with DC-SIGN ( 3T3 . DCSIGN ) were obtained through the NIH AIDS Research and Reference Reagent Program ( from Drs Thomas Martin and Vineet KewalRamani ) and maintained in similar DMEM medium without geneticin . Fc chimeric proteins ( derived from the murine MR ) and the hemagglutinin-tagged form of MR ( MR-HA ) were prepared by Richard Stillion as described previously [7 , 36] . Mannan was from Saccharomyces cerevisiae ( Sigma ) . Antibodies were specific for DV2 E protein ( 3H5; a gift from Dale Greiner ) , MR ( goat anti-hMR; a kind gift from Philip Stahl ( Washington University School of Medicine , St . Louis , MO ) , DC-SIGN ( 120507 ) , DC-SIGNR ( 120604 ) and both DC-SIGN and DC-SIGNR ( DC28 and 120612 ) ( all from R&D Systems ) . The specificity of anti-hMR antibody is examined in Figure S1 . For the generation of MDMØ and MDDC , human PBMC were isolated from buffy coats ( NHS Blood and Transport ) by centrifugation over a Ficoll-PaqueTM PLUS ( Amersham ) gradient , according to standard protocols . Adherent monocytes , isolated as described previously [37] , were cultured in X-VIVO medium ( BioWhittaker ) with 1% heat-inactivated autologous plasma to allow differentiation into MDMØ . These cells were >95% macrophages phenotypically ( CD14+ , CD16- , CD86+ , HLA-DR+ , CD3− ) ( data not shown ) . For the generation of MDDC , recombinant human IL-4 ( 25ng/ml; Peprotech ) and GM-CSF ( 50ng/ml; Peprotech ) were added to monocytes in X-VIVO medium ( BioWhittaker ) with 1% heat-inactivated autologous plasma and the cells cultured for 4 d . The use of human blood was approved by the central Oxford University research ethics committee ( MSD/IDEC/C1/2006/32 ) . Monocytes were treated with recombinant human IL-4 ( 25ng/ml; Peprotech ) or IL-13 ( 10ng/ml; Peprotech ) for 2 d then infected with mosquito cell-grown 16681 DV2 virus . After 1 h the viral supernatant was replaced with cell culture medium without cytokine and the cells incubated for 2 d then fixed with 4% paraformaldehyde . In some experiments the IL-4-treated MDMØ were incubated with various blocking reagents for 40 min at 37°C before the addition of 16681 DV2 virus ( in the presence of blocking agent ) . 96-well Maxi-Sorp plates were coated overnight with DV antigens ( 50ug/ml C6/36-grown NGC DV2 and 20ug/ml sE , or as specified in Figure 1 ) in phosphate-buffered saline ( PBS; 138mM NaCl , 2 . 7mM KCl , 8mM Na2HPO4 , 1 . 5mM KH2PO4 , [pH 7 . 4] ) at 4°C in triplicate . Wells were blocked with 0 . 5% immunoglobulin-free BSA ( Sigma ) before incubation with 15 ug/ml MR-HA , 2ug/ml human Fc-fusion MR domain proteins or 15ug/ml rabbit anti-DV1–4 antibody for 2 h at room temperature in Tris buffered saline ( TBS; 10mM TrisHCl , pH 7 . 4 , 0 . 154 M NaCl , 0 . 05% Tween 20 ) containing 10mM CaCl2 . For competitive binding studies MR fusion proteins were pre-incubated for 30 min on ice in TBS containing 1M NaCl and 10mM CaCl2 in the presence of 2mM d-mannose , 2mM l-fucose or 2mM galactose , or TBS containing 10mM EDTA . The wells were washed 6 times and binding of the Fc-fusion proteins was detected by incubating the wells with an alkaline phosphatase-conjugated anti-human antibody ( 1:1 , 000 dilution ) , visualized using 1mg/ml 4-Nitrophenyl phosphate disodium salt hexahydrate in 100mM Tris , 100mM NaCl , 5mM MgCl2 , pH9 . 5 , and the absorbance was read at 405nm . The wells with HA-tagged protein were incubated with 10ug/ml of MR5D3 ( rat anti-mouse MR; [21] ) for 1 h at room temperature followed by an alkaline phosphatase-conjugated anti-rat antibody ( 1:1 , 000 dilution ) and visualized as above . The wells with rabbit anti-DV1–4 were incubated with an alkaline phosphatase-conjugated anti-rabbit antibody ( 1:1 , 000 dilution ) and visualized as above . Wells coated with mannan and SO43GalNAc-PAA ( Lectinity ) were included as a positive control for proteins containing the CRD4–7 and CR domains respectively ( data not shown ) . We verified that DV2 ligands had coated all of the wells equally by using direct ELISA detection of DV antigens 3H5 ( 10ug/ml ) followed by a goat anti-mouse alkaline phosphatase-labelled ( 1:1 , 000 dilution ) secondary antibody and visualised as above . CRD4–7 binding to sE was investigated by blot overlay by running 0 . 9ug sE protein and 0 . 9ug sE protein deglycosylated with peptide: N-glycosidase F ( New England Biolabs ) on 10% SDS-PAGE , and subsequently transferring proteins to Hybond C-Extra nitrocellulose membranes ( Pharmacia ) . Blots were blocked for 1 h in 0 . 5% skimmed milk powder in TBS containing 10mM CaCl2 ( blocking/washing solution ) . Blots were probed with 1ug/ml MR CRD4–7-Fc in the absence or presence of either 2mM d-mannose , 2mM l-fucose or 20mM EDTA for 2 h , and then washed three times with blocking solution . Binding was detected with 1ug/ml horseradish peroxidase-conjugated anti-human IgG antibody ( Vector Laboratories ) and visualized by chemiluminescence . Blots probed in the presence of inhibitors were washed 3 times and reprobed with MR CRD4–7-Fc in the absence of inhibitor . Blots were stripped by incubation in 63mM Tris , pH 6 . 7 , 2% SDS and 100mM 2-mercaptoethanol at 50°C for 30 min , re-blocked and probed with the anti-DV E-protein antibody , 3H5 , at 10ug/ml . Binding was detected with 10ug/ml horseradish peroxidase-conjugated anti-mouse IgG antibody and visualized by chemiluminescence . An open reading frame consisting of the last 20 aa of C-protein , the entire prM protein , and E protein truncated by 96 amino acids at the C-terminus and containing a hexahistidine tag was amplified by PCR from cDNA prepared from DV2 strain 16681 . The expression cassette was cloned in the mammalian expression vector pLEX [38] and transfected into human 293T-HEK cells cultured in Optimem ( Invitrogen ) , and sE was partially purified from the supernatants of these cultures by Ni-chelation affinity chromatography ( Figure 3A ) . The identity of the protein was confirmed by mass-spectrometric analysis of peptides resulting from tryptic digest of the excised SDS-PAGE band ( data not shown ) . Glycans were released from approximately 25ug of recombinant soluble dengue virus E-glycoprotein and labelled by reductive amination with the fluorophore 2-aminobenzamide [39 , 40] . The glycans were processed through NP-HPLC and the retention times for the individual glycans were converted to glucose units ( GU ) using a standard dextran curve [41] . These were then compared with a database of experimental values ( http://glycobase . ucd . ie/cgi-bin/public/glycobase . cgi ) and initial assignments made were confirmed following digestions of the glycans with an array of exoglycosidase enzymes [40] . FACS was performed according to conventional protcols at 4°C in the presence of 2mM NaN3 . Non-specific binding sites on cells were blocked with PBS containing 5% heat-inactivated rabbit serum , 5% heat-inactivated goat serum , 0 . 5% BSA and 5mM EDTA ( blocking buffer ) before the addition of primary antibodies . Surface expressed MR and DC-SIGN was detected using 10ug/ml 15–2 ( Serotec ) and 120507 ( R&D Systems ) monoclonal antibodies , respectively , and was compared with an isotype control ( Serotec ) . Surface bound DV was detected using 10ug/ml 3H5 . The primary antibodies were detected using Alexafluor 488-conjugated anti-mouse antibody ( Molecular Probes ) diluted 1:200 in blocking buffer . Cells were fixed with 1% paraformaldehyde in PBS before analysis . Binding was quantified on a FACSCalibur flow cytometer and data from ∼10 , 000 cells were routinely acquired for each sample . Data were analysed using FlowJo software ( Treestar ) . Percent of max represents the number of events normalised according to FlowJo algorithms . Fold increase in receptor expression was measured by geometric mean fluorescent intensity of specific receptor antibody staining for IL-4-treated cells divided by untreated cells . Cells suspensions were prepared by scraping cells to preserve receptor expression at the cell surface . Mosquito cell-grown NGC DV2 ( 1 . 5 × 106 pfu/ml ) or media was incubated with cells ( 4 × 106 ) at a multiplicity of 0 . 35 infectious virions per cell for 80 min on ice . Unbound virus was washed away with cold media , the cells were fixed with 1% paraformaldehyde in PBS , and surface bound virus detected with anti-DV E-protein antibody by flow cytometry as described above . FITC-labelled , mannosylated or galactosylated BSA ( 5ug/ml; Sigma ) was incubated for 90 min at 37°C with primary human MDMØ or 3T3 transfectants plated on tissue culture-treated plastic . For blocking studies cells were pre-incubated with mannan ( 2mg/ml ) , normal goat serum ( NGS ) or goat-anti hMR antibody for 20 min at 37°C . After incubation cells were washed with PBS , harvested using PBS containing 5mM EDTA and lidocaine ( 4mg/ml ) and fixed in 2% paraformaldehyde in PBS . Binding was quantified by a FACSCalibur flow cytometer and analysed using FlowJo software . Fixed , DV-infected cells were permeabilised with 0 . 5% Triton-X and stained with 10ug/ml 3H5 followed by an Alexafluor 488-labelled secondary anti-mouse IgG antibody ( Molecular Probes ) and the nuclei stained with DAPI . Stained coverslips were mounted in DakoCytomation fluorescent mounting medium ( Dako ) , and analyzed using either CCD1 ( Axioplan ) and CCD camera ( Spot ) or a METATM confocal microscope linked to LSM 510TM software ( Carl Zeiss MicroImaging , Inc . ) . Confocal images were acquired sequentially using the multitrack configuration of the Zeiss METATM to avoid bleed-through between fluorescence channels , and the appropriate controls with and without primary antibody were performed . Additional image processing was performed using Adobe Photoshop 7 . The image is presented as single two-dimensional x-y sections and the corresponding transmission image . At least twelve fields ( 600–1 , 000 individual cells ) were counted by fluorescent micrsocopy . Statstics were calculated using GraphPad PRISM ( version 2 . 0; GraphPad Software , San Diego , CA ) and Microsoft Excel . Two-tailed Student's t tests were used to calculate p values .
|
Dengue disease and its severe manifestations are a growing public health concern , with a third to half the world's population living in dengue-endemic areas . In recent years there have been significant advances in understanding dengue virus ( DV ) interactions with target cells such as macrophages , dendritic cells , hepatocytes , and endothelial cells . Interaction with and infection of these cells leads to the production of new virions as well as immune mediators , which can shape the course of the subsequent immune response . The vascular leakage associated with dengue haemorrhagic fever is believed to be immune mediated . Our work on the interaction of DV with human macrophages has led to two major findings; first , we have identified that the macrophage mannose receptor is important for mediating the infection of human macrophages by DV , and second , that the type 2 cytokines IL-4 and IL-13 enhance macrophage susceptibility to DV infection . DV–receptor interactions are of critical importance for understanding not only the mechanisms of entry , but also the biology of infection and the pathogenesis . Understanding the immunopathogenesis of dengue disease is crucial to the development of both a safe dengue vaccine and therapeutic inhibitors of early DV replication .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"viruses",
"infectious",
"diseases",
"virology",
"immunology",
"homo",
"(human)",
"insects"
] |
2008
|
The Mannose Receptor Mediates Dengue Virus Infection of Macrophages
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Endemic typhus caused by Rickettsia ( R . ) typhi is an emerging febrile disease that can be fatal due to multiple organ pathology . Here we analyzed the requirements for protection against R . typhi by T cells in the CB17 SCID model of infection . BALB/c wild-type mice generate CD4+ TH1 and cytotoxic CD8+ T cells both of which are sporadically reactivated in persistent infection . Either adoptively transferred CD8+ or CD4+ T cells protected R . typhi-infected CB17 SCID mice from death and provided long-term control . CD8+ T cells lacking either IFNγ or Perforin were still protective , demonstrating that the cytotoxic function of CD8+ T cells is not essential for protection . Immune wild-type CD4+ T cells produced high amounts of IFNγ , induced the release of nitric oxide in R . typhi-infected macrophages and inhibited bacterial growth in vitro via IFNγ and TNFα . However , adoptive transfer of CD4+IFNγ-/- T cells still protected 30–90% of R . typhi-infected CB17 SCID mice . These cells acquired a TH17 phenotype , producing high amounts of IL-17A and IL-22 in addition to TNFα , and inhibited bacterial growth in vitro . Surprisingly , the neutralization of either TNFα or IL-17A in CD4+IFNγ-/- T cell recipient mice did not alter bacterial elimination by these cells in vivo , led to faster recovery and enhanced survival compared to isotype-treated animals . Thus , collectively these data show that although CD4+ TH1 cells are clearly efficient in protection against R . typhi , CD4+ TH17 cells are similarly protective if the harmful effects of combined production of TNFα and IL-17A can be inhibited .
Rickettsioses are emerging febrile diseases that can be fatal and are caused by obligate intracellular bacteria of the family of Rickettsiaceae . This family consists of two genera: Orientia with only one member ( Orientia tsutsugamushi ) and Rickettsia ( R . ) that is further subdivided into four groups: The spotted fever group ( SFG ) that contains the vast majority of rickettsiae ( e . g . R . rickettsii , R . conorii ) , the typhus group ( TG; R . prowazekii and R . typhi ) , the transitional group ( R . felis , R . akari and R . australis ) and the non-pathogenic ancestral group ( R . bellii and R . canadensis ) [1 , 2] . TG rickettsiae are the causative agents of epidemic typhus ( R . prowazekii ) and endemic typhus ( R . typhi ) . R . prowazekii is transmitted from human to human by the human body louse while rodents are considered as the dominant natural reservoir for R . typhi and fleas serve as vectors for these bacteria . Rickettsiae primarily infect endothelial cells [3] , leading to local vascular lesions and inflammatory responses that become visible as a characteristic hemorrhagic skin rash in 40–60% of the patients [1] . Symptoms of epidemic and endemic typhus are quite similar . After a 10–14 days period of latency patients suffer from high fever accompanied by headache , muscle and joint pain , nausea and vomiting . Furthermore , neurological symptoms such as confusion and stupor are common [4] . In severe cases , fatal multi-organ pathology including pneumonia , myocarditis , nephritis , hepatitis , splenomegaly and encephalitis/meningitis can occur [4 , 5] . The lethality of epidemic typhus is up to 20–30% [5–7] while the course of disease of endemic typhus is usually milder . The lethality of endemic typhus is estimated to be less than 5% [7 , 8] if untreated with antibiotics . Vaccines are not available . In recent years mouse models of rickettsial infections have been established , using nearly exclusively SFG rickettsiae . While BALB/c and C57BL/6 mice are resistant to the infection with various rickettsiae , C3H/HeN mice were revealed to be susceptible [9–13] . These mice have been used in various studies to analyze immune response against rickettsiae . CD8+ T cells seem to be critical for protection . C3H/HeN mice depleted of CD8+ T cells died upon infection with a normally sublethal dose of R . conorii while CD4+ T cell-depleted animals showed a similar course of illness as control mice [14] . Furthermore , adoptive transfer of immune CD8+ T cells protected C3H/HeN mice against a lethal challenge with R . conorii [14] but also the transfer of immune CD4+ T cells was protective in this system [14] . The role of CD8+ T cells was further addressed by the infection of CD8+ T cell-deficient C57BL/6 MHCI-/- mice and C57BL/6 Perforin-/- mice that lack the cytotoxic activity of CD8+ T cells and NK cells with R . australis . Both animals showed enhanced lethality in this infection [15] , demonstrating a critical role of CD8+ T cells and their cytotoxic activity in protection against SFG rickettsiae . Important cytokines that are involved in rickettsial defense are TNFα and IFNγ . R . conorii-infected C3H/HeN mice produced enhanced serum levels of IFNγ and IL-12 , the main IFNγ-inducing factor for T cells and NK cells [16] , in the first days of infection [12] . Furthermore , depletion of NK cells led to reduced IFNγ release and enhanced susceptibility of C3H/HeN mice to the infection with R . conorii [12] , suggesting the contribution of NK cells to early defense against rickettsiae via the release of IFNγ . Neutralization of either IFNγ or TNFα was associated with reduced nitric oxide ( NO ) production , led to uncontrolled bacterial growth and was fatal for C3H/HeN mice upon infection with a normally sublethal dose of R . conorii [17] . In line with these observations C57BL/6 IFNγ-/- mice showed enhanced lethality upon R . australis infection compared to wild-type mice [15] . Knowledge about immune response against TG rickettsiae , however , is still rare . Depletion of NK cells enhanced the susceptibility of normally resistant C57BL/6 mice to R . typhi infection [12] . Depletion of CD8+ T cells as well as the neutralization of IFNγ led to enhanced bacterial growth and mortality of C3H/HeN mice in R . typhi infection [18] . We recently showed that immune CD8+ as well as CD4+ T cells are capable of protecting T and B cell-deficient C57BL/6 RAG1-/- mice against R . typhi [19] , a model where the bacteria persist for several months and finally cause lethal CNS inflammation [20] . These observations suggest that similar mechanisms including NK cells , T cells , IFNγ and TNFα are involved in protection against both SFG and TG rickettsiae . The current study was performed to further clarify the protective capacity of CD4+ and CD8+ T cells and to decipher the effector mechanisms that are needed for T cell-mediated protection employing BALB/c wild-type mice and the CB17 SCID model of R . typhi infection . In CB17 SCID mice R . typhi induces splenomegaly , severe liver injury and fatal systemic inflammation [21] . Thus , the CB17 SCID model of infection reflects complications that frequently occur in patients with severe outcome of murine typhus [22] . In this model we show that the cytotoxic activity is not essential for CD8+ T cell-mediated protection . The release of IFNγ and other factors by CD8+ T cells is obviously as efficient in the control of R . typhi as direct killing of infected cells . Furthermore , we show that either CD4+ TH1 or TH17 cells protect against R . typhi . Here , TH17 cells that produce TNFα , IL-17A and IL-22 are as protective as IFNγ-releasing TH1 cells , provided that the non-beneficial biological effects of either TNFα or IL-17A are inhibited .
All experimentations and procedures were approved by the Public Health Authorities ( Amt fuer Gesundheit und Verbraucherschutz , Hamburg; No 88/13 ) and performed according to the German AnimalWelfare Act . BALB/c , BALB/c IFNγ-/- [23] , BALB/c Perforin-/- [24] and congenic CB17 SCID ( CB17/lcr-PrkdcSCID/lcrlcoCrl ) mice that lack T and B cells due to a genetic autosomal recessive mutation in the PrkdcSCID allele on chromosome 16 [25 , 26] were bred and maintained in the animal facilities of the Bernhard Nocht Institute for Tropical Medicine , Hamburg . Animals were housed in a biosafety level 3 facility for experimentation . The facilities are registered by the Public Health Authorities ( Amt für Gesundheit und Verbraucherschutz , Hamburg ) . R . typhi ( Wilmington strain ) was cultivated in L929 mouse fibroblasts ( ATCC CCL-1 ) and purified as described previously [20] . Stocks of purified bacteria were frozen in liquid nitrogen . Spot forming units ( sfu ) of thawed bacteria were determined by immunofocus assay as described [20] . CD8+ and CD4+ T cells were purified employing the MagniSort Mouse CD4 and CD8 Enrichment kits from eBioscience , Frankfurt , Germany . Procedures were performed according to the manufacturer´s instructions . The purity was generally >95% as determined by flow cytometric stainings . Mice were infected subcutaneously ( s . c . ) into the tail base with 2×106 sfu R . typhi in 50 μl PBS . Either 1×106 purified CD8+ or CD4+ T cells from naïve BALB/c , BALB/c IFNγ-/- or BALB/c Perforin-/- mice were adoptively transferred 1 day prior to infection . TNFα was neutralized by intraperitoneal application of 500 μg anti-TNFα ( clone XT3 . 11 , BioXCell , West Lebanon , US ) in 200 μl PBS . Control mice received the same amount of isotype antibody ( clone HRPN , BioXCell , West Lebanon , US ) . Treatment was performed every three days beginning on day 3 post infection . For the neutralization of IL-17A , anti-IL-17A ( clone 13F3 ) was used ( BioXCell , West Lebanon , US ) . 500 μg of the antibody were injected i . p . in 200 μl PBS every 2 days starting on day 2 post infection . Control mice received the same amount of isotype antibody ( clone MOPC-21 , BioXCell , West Lebanon , US ) . The health status of the animals was monitored with a clinical score [20] assessing five criteria: posture ( 0: normal , 1: temporarily curved , 2: curved ) , fur condition ( 0: normal , 1: staring in the neck , 2: overall staring ) , activity ( 0: normal , 1: reduced , 2: strongly reduced ) , weight loss ( 0: < 10% , 1: 10–14% , 2: > 15% ) and food and water uptake ( 0: normal , 1: reduced , 2: none ) . Mice were euthanized reaching a total score ≥ 8 or showing weight loss of ≥ 20% . This point in time was determined as the time of death and used to determine survival rates . The state of health of the animals was assessed by clinical scoring . Blood was taken submandibular or by cardiac puncture . EDTA coated tubes ( KABE Labortechnik GmbH , Nümbrecht-Elsenroth , Germany ) were used for plasma samples and centrifuged at 5654×g . Serum samples were obtained by agglutination for 15–20 at RT in Eppendorf tubes followed by centrifugation for 10 min 5654×g . DNA was prepared employing the QIAamp DNA Mini Kit ( Qiagen , Hilden , Germany ) according to the manufacturer’s guide . 10 mg tissue was homogenized in 500 μl PBS in Precellys ceramic Kit tubes ( Peqlab . Erlangen , Germany ) in a Precellys 24 homogenizer ( Peqlab . Erlangen , Germany ) with following cycle parameters: 6000 rpm two times for 45 sec with a 60 sec break . DNA was prepared from 80 μl homogenized organs . qPCR was performed as described previously [20] by amplification of a 137 bp fragment of the prsA gene ( RT0565 ) with the forward primer 5´-ACA GCT TCA AAT GGT GGG GT-3´ and reverse primer 5´-TGC CAG CCG AAA TCT GTT TTG-3´ in a standard SYBR green real-time PCR . A standard template plasmid ( pCR2 . 1-prsA ) was used as a reference . Reactions were performed in a Rotor Gene 6000 ( Qiagen , Hilden , Germany ) . Single cell suspensions were prepared from spleen and blood . Erythrocytes were eliminated by incubating the cells in erythrocyte lysis buffer ( 10 mM Tris , 144 mM NH4Cl , pH 7 . 5 ) for 5 minutes at room temperature . Afterwards , cells were washed twice with PBS . Fc receptors were blocked with 50 μl 5% CohnII human IgG fraction ( Sigma-Aldrich , Deisenhofen , Germany ) in PBS or Permeabilization buffer of the FoxP3/Transcription factor staining buffer kit ( eBioscience , Frankfurt , Germany ) . Spleen cells were restimulated with 10 ng/ml PMA and 500 ng/ml Ionomycin in 200 μl in 96well plates in the presence of 1 μl GolgiStop ( BD Biosciences , Heidelberg , Germany ) for 4h and permeabilized with Fixation and Permebilization buffer . For intracellular stainings antibodies were diluted in the Permeabilization buffer of the kit . The following antibodies were used at indicated dilutions: anti-mouse CD4-PE ( clone GK1 . 5; 1:200 ) and CD8-PerCP-Cy5 . 5 ( clone 53–6 . 7; 1:200 ) from BD Biosciences , Heidelberg , Germany; anti-mouse CD8-Alexa488 ( clone 53–6 . 7; 1:200 ) , anti-mouse CD4-FITC ( clone H129 . 19; 1:200 ) , anti-mouse CD8-APC ( clone 53–6 . 7; 1:200 ) , anti-mouse KLRG1-PE ( clone 2F1/KLRG1; 1:800 ) , anti-mouse IFNγ-PE/Dazzle ( clone XMG1 . 2; 1:333 ) and anti-mouse Granzyme B-PacificBlue ( clone GB11; 1:200 ) from Biolegend , London , UK . anti-mouse CD11a-eFluor450 ( clone M17/4; 1:200 ) from eBioscience , Frankfurt , Germany . After staining , cells were washed and resuspended in PBS/1% PFA prior to flow cytometry . Analyses were performed with a BD Accuri C6 or BD LSR II flow cytometer ( BD Biosciences , San José , USA ) and FlowJo single cell analysis software ( FlowJo LLC , Ashland , USA ) . Bead-based LEGENDplex immunoassay ( BioLegend , London , UK ) was used for the quantification of plasma cytokines and cytokines in cell culture supernatants . Procedures were performed according to the manufacturer’s protocol using cluster tubes ( ThermoScientific , Loughborough UK ) . 12 . 5 μl of plasma from EDTA blood samples was used diluted 1:2 in assay buffer . Supernatants from bmMΦ were used non-diluted . Analyses were performed using a BD Accuri C6 ( BD Biosciences , San José , USA ) and LEGENDplex analysis software ( BioLegend , San Diego , USA ) . Serum levels of GPT were evaluated using Reflotron GPT ( ALT ) stripes and Reflotron Plus device ( Roche Diagnostics , Mannheim , Germany ) according to the manufacturer’s instructions . Serum samples were diluted 1:3 in PBS prior to analyses . Bone marrow was isolated from femur and tibia of BALB/c mice . 2×106 cells were plated in petri dishes and differentiated for 12 days in IMDM ( PAA , Cölbe , Germany ) supplemented with 10% FCS , 2 mM L-glutamine , 5% horse serum ( Biochrom , Berlin , Germany ) and L929 fibroblast medium as a source of M-CSF . Medium was exchanged every 3 days . bmMΦ were harvested after 12 days and washed twice with PBS prior to use . 1×106 bmMΦ were seeded into 24-well tissue culture plates and infected in duplicates with 5 R . typhi particles as determined by qPCR per cell . 2×106 purified CD4+ T cells from either naïve BALB/c mice or immune BALB/c mice that had been infected with 2×106 sfu R . typhi 7 days earlier were added to the culture after 24h . 10 μg/ml neutralizing antibodies ( anti-IFNγ clone XMG1 . 2 , anti-TNFα clone XT3 . 11; BioXCell , West Lebanon , US ) and T cells were added simultaneously . Cells were further incubated for 48h . Alternatively , recombinant cytokines ( IFNγ , 1 U/ml , TNFα , 400 U/ml; PreproTech , Hamburg , Germany ) were added instead of T cells and neutralized with the same amounts of antibodies . Complete wells including supernatant were then harvested . Cells were obtained by high speed centrifugation and used for DNA preparation and qPCR detection of R . typhi . In parallel , similar cultures were performed for the quantification of nitric oxide ( NO ) and cytokines in the culture supernatants 24h and 48h after T cell addition . NO was quantified by Griess reaction in supernatants of bmMΦ and co-cultures with T cells . Assays were performed in microtiter plates ( Greiner Bio-One , Frickenhausen , Germany ) . 100 μl of sample were mixed with 50 μl Griess 1 reagent ( 0 . 5 g sulfonamide in 50 ml 1M HCl ) and 50 μl Griess 2 reagent ( 0 . 15 g N- ( 1-Naphtyl ) ethylendiamine-dihydrochloride in 50 ml H2O ) . A serial dilution of sodium nitrite ( NaNO2 ) in culture medium was used as a standard ( cmax 125 μM ) . The absorbance was measured at 560 nm with a Dynex MRXII spectrophotometric microplate reader ( Dynex Technologies , Chantilly , USA ) . GraphPad Prism 5 software ( GraphPad Software , Inc . , La Jolla , USA ) was used for statistical analysis . The proportion of surviving animals was analyzed with Log-rank ( Mantel-Cox ) test . For comparison between multiple groups One-way ANOVA ( Kruskal Wallis test followed by Dunn´s post test ) was used .
BALB/c mice do not develop symptomatic disease upon R . typhi infection but the bacteria persist [20] . Control of the bacteria in these mice is mediated by the adaptive immune response because T and B cell deficient congenic mice are highly susceptible and succumb to the infection within three weeks [21] . Therefore , we first analyzed the T cell response in BALB/c mice upon R . typhi infection . For this purpose , spleen cells from R . typhi-infected BALB/c mice were isolated during the course of infection at day 7 , 15 and 35 . Control mice were treated with PBS ( day 0 ) . Cells were restimulated with PMA and ionomycin for the intracellular staining of effector molecules ( Granzyme B and IFNγ ) . Additionally , surface CD8 , CD4 , CD11a and KLRG1 ( inhibitory killer cell lectin-like receptor G1 ) were stained . CD11a and KLRG1 are upregulated during immune response , allowing to distinguish CD11a+ antigen-experienced CD8+ T cells [27] and KLRG1+ activated effector CD8+ T cells from naïve T cells that are negative for these markers [28 , 29] . Cells were analyzed by flow cytometry and gated either on CD8+ or CD4+ T cells . Total numbers of CD8+ T cells did not significantly increase at any point in time but a significant increase of the frequency of CD11a+ and KLRG1+ CD8+ T cells was observed on day 7 after infection ( Fig 1 ) . At this point in time 16 . 63±0 . 39% of the CD8+ T cells expressed CD11a and 10 . 02±0 . 98% were positive for KLRG1 while 6 . 27±0 . 37% CD11a+ and 1 . 69±0 . 29% KLRG1+ CD8+ T cells were detectable in control animals ( day 0 ) . In addition , the percentage of CD8+ T cells that expressed Granzyme B and IFNγ was significantly enhanced on day 7 . 5 . 12±0 . 47% of the CD8+ T cells were Granzyme B+ and 20 . 93±1 . 31% CD8+ T cells expressed IFNγ compared to 0 . 75±0 . 28% Granzyme B+ and 9 . 87±0 . 78% IFNγ+ cells in control mice ( day 0 ) . The CD8+ T cell response declined until day 15 but did not reach background levels again . On the contrary , CD8+ T cells appeared to be reactivated on day 35 post infection again . At this late point in time IFNγ expression was significantly enhanced and IFNγ-producing CD8+ T cells reached frequencies ( 19 . 93±0 . 52% ) comparable to those observed on day 7 post infection . A similar trend was true for the expression of Granzyme B ( Fig 1 ) . As seen for CD8+ T cells the absolute cell counts of CD4+ T cells did not increase during the course of infection but the frequency of CD4+ T cells that expressed intracellular IFNγ was significantly increased on day 7 post infection ( 8 . 54±0 . 60% compared to 5 . 66±0 . 27% in control mice ( day 0 ) ) . Similar to activated CD8+ T cells , IFNγ-expressing CD4+ T cells declined until day 15 and increased again on day 35 reaching even higher frequencies ( 10 . 78±0 . 68% ) compared to day 7 ( Fig 2 , left ) . This indicates sporadic reactivation also of CD4+ T cells during chronic infection . In persistent infections CD4+ T cells can acquire cytotoxic function . These cells up-regulate CD11a and express cytolytic mediators including Granzyme B [30] . Granzyme B was not expressed at all by CD4+ T cells during R . typhi infection ( Fig 2 , middle ) . Approximately 17% of the CD4+ T cells were positive for CD11a in naïve mice ( Fig 2 , right ) . CD11a was not expressed at significantly enhanced levels by CD4+ T cells in R . typhi-infected BALB/c mice at any point in time ( Fig 2 ) . Thus , CD4+ T cells in R . typhi-infected animals do not show characteristics of cytotoxic cells . These findings demonstrate that BALB/c mice mount a cytotoxic CD8+ T cell effector response and generate IFNγ-expressing CD4+ TH1 effector cells both of which become reactivated in persistent infection with similar kinetics . We next addressed the question whether either CD8+ or CD4+ T cells can protect mice against R . typhi infection . For this purpose , we performed adoptive transfer of T cells from BALB/c mice into susceptible congenic T and B cell-deficient CB17 SCID mice that were infected with R . typhi . Either CD4+ or CD8+ T cells were isolated from naïve BALB/c mice . T cells were then adoptively transferred into CB17 SCID mice one day prior to R . typhi infection . Control mice received PBS instead of T cells and were infected with R . typhi . The presence of CD4+ and CD8+ T cells was analyzed in spleen and blood on day 7 post infection in all groups . As expected , neither CD4+ T cells ( spleen: 0 . 64±0 . 12%; blood: 0 . 03±0 . 01% ) nor CD8+ T cells ( spleen: 0 . 66±0 . 11%; blood: 0 . 02±0 . 00% ) were detectable in CB17 SCID control mice that did not receive T cells ( Fig 3A ) . In CD4+ T cell recipients 10 . 13±2 . 65% of the spleen cells were CD4+ while CD8+ T cells were clearly absent ( 0 . 89±0 . 06% ) . The same was true for the blood . CD4+ T cells constituted 2 . 96±0 . 77% of the leukocytes in the blood while CD8+ T cells were not detectable ( 0 . 03±0 . 01% ) . Vice versa CD8+ T cells were present in the spleen of CD8+ T cell recipients ( 2 . 75±0 . 21% ) and in the blood of these animals ( 1 . 23±0 . 31% ) while CD4+ T cells were absent ( spleen: 0 . 58±0 . 09%; blood: 0 . 16±0 . 06% ) ( Fig 3A ) . The health status of the animals was monitored by measuring body weight and evaluation by a clinical score . Weight change , clinical score and survival rates of CD4+ and CD8+ recipients and control mice are depicted in Fig 3B . R . typhi-infected CB17 SCID control mice continuously lost weight beginning around day 10 and developed a high clinical score >8 and severe disease until day 20 . Mice showing a score ≥8 were euthanized . This point in time was defined as time of death to analyze survival rates . All CB17 SCID control mice succumbed to the infection before day 21 . In contrast , CD8+ T cell recipients neither lost weight nor showed signs of disease at any point in time and all animals survived the infection ( Fig 3B ) . R . typhi-infected CB17 SCID mice that received CD4+ T cells showed temporary weight loss with similar kinetics as control animals which was significant compared to CD8+ T cell recipients and a low clinical score with a maximum of 4 around day 12 and 13 ( p = 0 . 06 compared to CD8+ T cell recipients ) ( Fig 3B ) . Surprisingly , except for one animal CD4+ T cell recipient mice recovered until day 20 and survived the infection ( Fig 3B ) . In addition , GPT levels were detected in the serum during the course of infection as a measure for liver damage that is observed in CB17 SCID mice upon R . typhi infection [21] . In concordance with temporary disease , GPT levels significantly increased in CD4+ T cell recipients until day 14 post infection , reaching comparable levels as in infected control mice . GPT concentrations then declined to background levels again until day 21 when the mice recovered . Serum GPT levels were not increased in CD8+ T cell recipients at any point in time ( Fig 3B ) . These data show that both CD8+ as well as CD4+ T cells are capable of protecting CB17 SCID mice from death upon R . typhi infection although CD4+ T cells are less efficient in protection from R . typhi-induced disease . As an indicator for the activation of adoptively transferred T cells we further analyzed serum cytokine levels on day 7 post infection in control mice , CD4+ and CD8+ T cell recipients . Non-infected CB17 SCID mice that received PBS were used as an additional control . At this early point in time in infection , enhanced amounts of IFNγ ( 248 . 80±34 . 54 pg/ml ) and TNFα ( 3 . 47±1 . 17 pg/ml ) were already present in the serum of R . typhi-infected control mice compared to non-infected animals where TNFα was not detectable at all and IFNγ was present at background levels ( 60 . 13±30 . 12 pg/ml ) . IL-6 was not yet enhanced in R . typhi-infected CB17 SCID mice ( 7 . 48±2 . 64 pg/ml ) compared to non-infected animals ( 4 . 90±4 . 90 pg/ml ) and IL-2 was not detectable in these mice as expected . IL-2 was also not measurable in CD8+ T cell recipients . TNFα ( 1 . 94±0 . 54 pg/ml ) was slightly enhanced compared to non-infected animals and comparable to levels measured in infected control animals ( 3 . 47±1 . 17 pg/ml ) . IFNγ ( 70 . 06±23 . 54 pg/ml ) and IL-6 ( 2 . 74±0 . 79 pg/ml ) were detected at background levels in CD8+ T cell recipients , indicating that the immune response is already being terminated . In contrast to CD8+ T cell recipients , IFNγ concentrations were strongly and significantly enhanced in the sera of animals that had received CD4+ T cells ( 1963±982 . 30 pg/ml ) . Similar was also true for TNFα ( 12 . 97±5 . 42 pg/ml ) and IL-6 ( 34 . 36±14 . 95 pg/ml ) although levels of these cytokines were still quite low . In addition , very low amounts of IL-2 were measurable in the sera from CD4+ T cell recipients ( 0 . 63±0 . 33 pg/ml ) ( Fig 4A ) . Other T cell-derived cytokines including IL-17A/F and IL-22 were generally not detectable . These data show that adoptively transferred CD4+ T cells produce IFNγ and enhance the inflammatory response at day 7 post infection while the immune response seems to be already terminated in CD8+ T cell recipients at this time . To analyze the capacity of both CD4+ and CD8+ T cells to eliminate the bacteria in vivo we further determined the bacterial load in different organs of all groups of mice on day 7 post infection by qPCR . Highest numbers of bacteria were generally detectable in the spleen followed by the brain , lung and liver in CB17 SCID mice ( Fig 4B and [21] ) . At this early point in time after R . typhi infection bacterial numbers were still low . In R . typhi-infected control mice 427 . 30±263 . 80 copies were detectable in the spleen , 99 . 57±46 . 64 copies in the brain , 6 . 40±2 . 14 copies in the lung and 1 . 43±0 . 87 copies in the liver . The bacterial load in these organs was unaltered in CD4+ T cell recipients ( spleen: 471 . 20±168 . 1 copies , brain: 108 . 40±27 . 93 copies , lung: 11 . 10±5 . 37 copies , liver: 2 . 09±0 . 39 copies ) at this point in time but significantly reduced in animals that received CD8+ T cells . In fact , the bacteria were already almost absent in the organs of this group of mice ( spleen: 11 . 23±6 . 26 copies , lung: 0 . 03±0 . 01 copies , liver: 0 . 58±057 copies ) except for the brain ( 33 . 55±12 . 41 copies ) ( Fig 4B ) . In control animals , bacterial numbers further increased until death ( spleen: 40931±17971 copies , brain: 1151±269 . 9 copies , lung: 3897±1334 copies , liver 2767±970 . 4 copies ) ( Fig 4C ) . Five mice of the CD4+ and CD8+ recipient groups were further monitored for 175 days . One mouse of the CD8+ recipient group died on day 85 for undefined reasons . This animal did not show typical symptoms of R . typhi-induced disease and was not further analyzed . All other animals survived . The bacteria were not detectable in the organs from the CD8+ T cell recipients at this very late point in time ( Fig 4B ) . Furthermore , the bacteria were also absent in the organs from R . typhi-infected CD4+ recipient mice except for one animal that showed very low numbers of bacteria in the lung ( 8 . 65 copies; Fig 4B ) . This mouse , however , did not show any signs of disease . These results demonstrate that adoptively transferred CD8+ as well as CD4+ T cells are capable of eliminating R . typhi and provide long-term control of the bacteria . The results presented so far show that both CD4+ and CD8+ T cells can mediate protection against R . typhi in this model of infection . We next asked which effector functions are required by both cell types to mediate protection and focused on IFNγ as an activator of bacterial killing by macrophages [31 , 32] and Perforin which is critical for cell-mediated cytotoxicity [33–35] . We first infected BALB/c mice lacking either IFNγ or Perforin with R . typhi . Neither BALB/c IFNγ-/- nor BALB/c Perforin-/- mice showed symptoms of disease at any point in time . All animals survived the infection for more than 120 days ( Fig 5A ) , demonstrating long-term protection in the absence of one or the other molecule . To further elucidate the requirement of IFNγ and Perforin for CD4+ and CD8+ T cell-mediated protection we adoptively transferred either purified CD4+ or CD8+ T cells from BALB/c IFNγ-/- or Perforin-/- mice into CB17 SCID mice . Mice were infected with R . typhi one day after T cell transfer . R . typhi-infected CB17 SCID mice that had received CD4+ IFNγ-/- T cells developed a clinical score and lost body weight between day 9 and 14 . However , 70% of these animals recovered and survived the infection ( Fig 5B ) . R . typhi-infected CB17 SCID mice that had either received CD8+ T cells from IFNγ-/- or Perforin-/- mice never showed any signs of disease and all of the mice survived the infection ( Fig 5B ) . At the time of death R . typhi-infected control animals had high bacterial loads in all organs ( spleen: 38333±7786 copies; liver: 6507±1529 copies; brain: 8781±2093 copies; lung: 5420±1620 copies ) as observed before while strongly reduced numbers of bacteria were detectable in the organs of the two mice of the CD4+ T cell recipient group that succumbed to the infection ( spleen: 277 . 5±237 . 6 copies; liver: 1123±416 . 8 copies; brain: 1632±1243 copies; lung: 561 . 8±414 . 8 copies ) ( Fig 5C ) . Surviving animals were further followed for more than 120 days and did never show symptoms of disease again . Organs were analyzed for bacterial content when the experiments were terminated ( between day 120 and 168 p . i . ) . Bacterial copy numbers of individual animals are given in table 1 and Fig 5D shows the statistical analysis . R . typhi was not detectable at all in the organs of CD8+Perforin-/- T cell recipients ( Fig 5D and Table 1 ) . However , low numbers of bacteria were detectable in the majority of the CD8+IFNγ-/- T cell recipients ( 5 out of 6 mice ) . Here , R . typhi was predominantly found in the brain ( 206 . 0±192 . 8 copies ) while the lung , liver and spleen were much less affected ( Fig 5D and Table 1 ) . In contrast , only 2 out of 8 mice of the CD4+IFNγ-/- T cell recipients showed low numbers of bacteria in the brain and 1 mouse was positive in the spleen ( Fig 5D and Table 1 ) . These results demonstrate that CD8+ T cells that either lack cytotoxic function or IFNγ can control the bacteria for a long period of time . For this control , IFNγ seems to be even more critical than the cytotoxic activity . On the other hand , IFNγ-deficient CD4+ T cells are protective and as efficient in long-term bacterial control as wild-type CD4+ T cells , suggesting that IFNγ is dispensable for CD4+ T cell-mediated protection . The observation that still 70% of R . typhi-infected CB17 SCID mice that received IFNγ-/- CD4+ T cells survived the infection was surprising . Another cytokine that can activate phagocytes for bacterial killing and may compensate for the absence of IFNγ is TNFα [36] which was produced by adoptively transferred CD4+ T cells in R . typhi-infected CB17 SCID mice ( Fig 4A ) . To clarify the contribution of TNFα to CD4+ T cell-mediated bacterial killing , we analyzed the impact of CD4+ T cell-derived TNFα and IFNγ on the activation of R . typhi-infected macrophages . For this purpose , we isolated CD4+ T cells from either naïve or R . typhi-infected BALB/c wild-type mice on day 7 post infection and incubated the cells with R . typhi-infected macrophages . TNFα and IFNγ were inhibited by the addition of neutralizing antibodies . Cytokines and NO were quantified in the supernatants and bacterial growth was assessed by qPCR . Fig 6A shows that CD4+ T cells from naïve mice did not react to R . typhi-infected macrophages at all with cytokine production while cultures containing immune T cells from R . typhi-infected animals produced high amounts of IFNγ ( 21749±9799 pg/ml ) in addition to TNFα ( 84 . 97±23 . 68 pg/ml ) ( Fig 6A ) . The release of IFNγ was strongly but not completely inhibited by the addition of anti-IFNγ ( 1141±607 pg/ml ) and also to some extent upon neutralization of TNFα ( 6407±3047 pg/ml ) . Correspondingly , IFNγ release was further inhibited by a combination of anti-IFNγ and anti-TNFα ( 520 . 8±270 . 3 pg/ml ) ( Fig 6A , left ) . TNFα was reduced in the presence of anti-TNFα ( 24 . 93±2 . 68 pg/ml ) and a combination of both antibodies ( 20 . 70±4 . 41 pg/ml ) but not influenced by the addition of anti-IFNγ ( 69 . 56±20 . 93 pg/ml ) ( Fig 6A , middle ) . Although neither the neutralization of IFNγ nor TNFα was complete , the release of IFNγ and TNFα was not significantly enhanced anymore compared to cultures containing naïve T cells . Neutralization of IFNγ further led to the release of the TH17 cytokine IL-22 by immune CD4+ T cells ( 176 . 90±84 . 08 pg/ml ) which was hardly detectable in the absence of anti-IFNγ ( 10 . 37±3 . 65 pg/ml ) and comparably increased upon neutralization of both IFNγ and TNFα ( 162 . 90±82 . 76 pg/ml ) ( Fig 6A , right ) . IL-2 was not significantly altered in the presence of the antibodies ( Fig 6A , below left ) and other cytokines were not detectable . Because T cells are major cellular sources of IFNγ and IL-22 [37 , 38] , we conclude that these cytokines are produced by the T cells upon antigen recognition rather than by the macrophages and similar is likely true for TNFα in this system . We further assessed the activation of the bactericidal function of R . typhi-infected macrophages by measuring nitric oxide ( NO ) . Immune CD4+ T cells from wild-type mice induced the release of significant amounts of NO ( 5 . 69±1 . 51 μM ) by R . typhi-infected macrophages ( Fig 6B ) . Although cytokine neutralization was incomplete ( Fig 6A ) , NO release was comparably and nearly completely abrogated in the presence of either anti-IFNγ ( 0 . 40±0 . 19 μM ) , anti-TNFα ( 0 . 28±0 . 16 μM ) or a combination of both antibodies ( 0 . 24±0 . 15 μM ) ( Fig 6B ) . Moreover , immune but not naïve BALB/c CD4+ T cells strongly inhibited bacterial growth in infected macrophages in vitro ( Fig 6C ) . Compared to cultures containing naïve CD4+ T cells where 267 . 6±57 . 92 R . typhi prsA gene copies were detected , the bacterial content was significantly reduced to 4 . 86±2 . 18 copies in the presence of immune CD4+ T cells ( Fig 6C ) . Bacterial growth was partially restored in the presence of anti-IFNγ ( 32 . 35±26 . 62 prsA copies ) and a combination of anti-IFNγ and anti-TNFα ( 54 . 05±34 . 12 prsA copies ) while the effect of anti-TNFα alone on bacterial growth was less pronounced ( 10 . 73±5 . 86 prsA copies ) ( Fig 6C ) . Bacterial growth , however , was not completely restored by the addition of neutralizing antibodies and differences were not statistically significant compared to untreated cultures which may be ascribed to incomplete cytokine neutralization ( Fig 6A ) . To further elucidate a direct influence of IFNγ and TNFα on bacterial growth , additional cultures of R . typhi-infected macrophages were treated with recombinant IFNγ and TNFα in the absence of T cells . Neither IFNγ nor TNFα induced the release of detectable levels of cytokines or NO ( Fig 6D ) but clearly inhibited bacterial growth . In the absence of recombinant cytokines 1518±367 . 4 R . typhi prsA gene copies were detectable while only 90 . 35±40 . 16 prsA copies and 63 . 49±31 . 87 prsA copies were measured in the presence of recombinant TNFα or IFNγ , respectively . This effect was partially abolished upon neutralization of the respective cytokine ( TNFα+anti-TNFα: 412 . 3±42 . 02 prsA copies , IFNγ+anti-IFNγ: 400 . 8±73 . 55 prsA copies ) . This experiment was performed twice so that statistical analysis was not performed ( Fig 6E ) . These data demonstrate that both T cell-derived IFNγ and TNFα can contribute to the activation of macrophage bactericidal activity and bacterial killing by CD4+ T cells . Having shown that T cell-derived IFNγ as well as TNFα activate the bactericidal function of macrophages and inhibit the growth of R . typhi in infected macrophages in vitro , we speculated that TNFα may compensate for the absence of IFNγ in protection against R . typhi by CD4+IFNγ-/- T cells . To test the contribution of TNFα to bacterial killing by CD4+IFNγ-/- T cells we isolated naïve and immune CD4+ T cells from R . typhi-infected BALB/c IFNγ-/- mice on day 7 post infection and incubated these cells with R . typhi-infected macrophages in vitro . TNFα was blocked by the addition of neutralizing antibody and bacterial growth was assessed by qPCR . CD4+IFNγ-/- T cells indeed inhibited bacterial growth at least by tendency . While 52 . 91±13 . 03 R . typhi prsA gene copies were detectable in cultures containing naïve CD4+IFNγ-/- T cells , only 6 . 55±4 . 91 prsA copies were present in cultures with CD4+IFNγ-/- T cells from immune animals . Inhibition of bacterial growth by immune CD4+IFNγ-/- T cells was partially restored by the neutralization of TNFα ( 16 . 81±13 . 20 prsA copies ) , indicating a contribution of TNFα to bacterial killing by CD4+IFNγ-/- T cells ( Fig 7A ) . Next , we again adoptively transferred CD4+ T cells from IFNγ-/- mice into CB17 SCID mice one day prior to R . typhi infection and neutralized TNFα to clarify the role of this cytokine in CD4+ T cell-mediated protection in vivo . Control mice were infected with R . typhi and received PBS instead of T cells . Mice were then either treated with isotype antibody or with neutralizing anti-TNFα every three days . Weight change , clinical score and survival rates were assessed . All R . typhi-infected CB17 SCID control mice continuously lost weight , developed a high clinical score and succumbed to the infection within 21 days whether treated with isotype antibody or anti-TNFα ( Fig 7B ) . 2 out of 7 ( 29% ) of the CD4+IFNγ-/- T cell recipients that received isotype antibody survived the infection ( Fig 7B ) . The surviving animals of this group lost weight until day 18 and showed a peak clinical score of 3–4 on day 16 post infection . The mice then recovered ( Fig 7B ) . Surprisingly , the survival rate of anti-TNFα-treated CD4+IFNγ-/- T cell recipients was higher . 5 out of 7 ( 86% ) of the anti-TNFα-treated CD4+IFNγ-/- T cell recipients survived the infection ( Fig 7B ) . Furthermore , disease of these animals was apparently milder and the animals recovered faster . The weight loss of surviving animals of this group was less pronounced and peaked on day 16 post infection ( -5 . 44±2 . 59% ) while isotype-treated surviving CD4+IFNγ-/- T cell recipients still showed weight loss of -16 . 45±1 . 55% at this point in time ( Fig 7B ) . In addition , the peak of disease as measured by clinical scoring was 4 days earlier ( day 12 ) than that of isotype-treated CD4+IFNγ-/- T cell recipients and the animals recovered faster ( Fig 7B ) . All CD4+IFNγ-/- T cell recipients that finally succumbed to the infection continuously lost weight and developed a clinical score with similar kinetics or even a little earlier than control mice , whether treated with isotype or anti-TNFα antibody ( Fig 7B ) . These data show that TNFα obviously exerts pathological effects and is non-beneficial in CD4+ T cell-mediated protection in the absence of IFNγ . Because CB17 SCID mice develop severe liver necrosis upon R . typhi infection [21] , we also assessed serum GPT levels as a measure for liver damage in all groups of mice at the time of death and in surviving CD4+IFNγ-/- T cell recipients at day 34 post infection when the experiment was terminated . GPT levels in CD4+IFNγ-/- T cell recipients including those that succumbed to the infection were generally normal , whether treated with isotype antibody or anti-TNFα , while serum GPT was enhanced in control animals compared to non-infected mice ( Fig 7C ) . Liver damage in control mice was unaltered by the application of anti-TNFα ( Fig 7C ) . These data show that TNFα is generally not involved in liver damage in R . typhi-infected CB17 SCID mice and that CD4+IFNγ-/- T cells completely prevent liver damage in the presence or absence of this cytokine . To analyze bacterial clearance , we further quantified the bacterial burden of all animals that succumbed to the infection at the time of death and of surviving mice at day 34 when the experiment was terminated . As expected , control CB17 SCID mice that did not receive T cells developed high and comparable bacterial loads in all organs when treated with isotype antibody ( spleen: 94547±30121 , brain: 11109±3904 , lung: 1624±407 . 6 , liver: 1310±292 . 6 prsA copies ) ( Fig 7D ) . The application of anti-TNFα did not alter the bacterial load in these animals ( spleen: 80536±19501 , brain: 12259±2881 , lung: 2450±724 . 8 , liver: 828 . 4±17 . 1 prsA copies ) ( Fig 7D ) . Bacterial numbers were reduced in all CD4+IFNγ-/- T cell recipients including those that succumbed to the infection . Isotype-treated mice of this group that died upon infection had 278 . 4±150 . 3 prsA copies in the spleen , 499 . 2±367 . 3 copies in the brain , 77 . 3±29 . 7 copies in the lung and 103 . 1±82 . 6 copies in the liver and the mouse of the anti-TNFα-treated group that died on day 13 also had reduced amounts of bacteria in all organs ( spleen: 286 , brain: 621 . 5 , lung: 454 . 5 , liver: 456 . 0 prsA copies ) ( Fig 7D ) . CD4+IFNγ-/- T cell recipients that survived the infection and were treated with isotype antibody were almost negative for R . typhi . The bacteria were generally not detectable in the liver and lung of these animals while low copy numbers were detectable in the spleen ( 1 . 12±1 . 12 copies ) and brain ( 64 . 93±19 . 13 copies ) at the end of the experiment . Similar was also true for anti-TNFα-treated CD4+IFNγ-/- T cell recipients . Here , spleen and liver were negative for R . typhi while low copy numbers were found in the brain ( 14 . 34±9 . 51 copies ) and lung ( 18 . 50±16 . 30 copies ) . These data clearly demonstrate that TNFα is not essential for bacterial elimination by CD4+IFNγ-/- T cells in vivo , and that TNFα exerts pathological effects . The previous experiment gave rise to the question which alternative cytokines may then be involved in protection by CD4+ T cells in the absence of both IFNγ and TNFα . Therefore , we compared the cytokine profile that is expressed by immune wild-type BALB/c and BALB/c CD4+IFNγ-/- T cells upon antigen recognition . Either naïve or immune CD4+ T cells prepared from BALB/c and BALB/c IFNγ-/- mice on day 7 post R . typhi infection were incubated with infected macrophages in vitro to achieve antigen-specific restimulation . Naïve CD4+ T cells were used as a control . Cytokines were generally not detectable in cultures containing R . typhi-infected macrophages and naïve CD4+ T cells ( Fig 8 ) . Cultures of infected macrophages and immune wild-type CD4+ T cells produced very high amounts of IFNγ ( 21749±9799 pg/ml ) in addition to IL-2 ( 1213±104 pg/ml ) and lower amounts of TNFα ( 84 . 97±23 . 68 pg/ml ) . Furthermore , low levels of IL-10 ( 82 . 22±31 . 18 pg/ml ) and negligible amounts of IL-22 ( 10 . 37±3 . 65 pg/ml ) and IL-6 ( 10 . 71±3 . 09 pg/ml ) were measurable in cultures with immune wild-type CD4+ T cells . Other cytokines were not produced ( Fig 8 ) . In contrast , the supernatants of cocultures of immune CD4+IFNγ-/- T cells and R . typhi-infected macrophages contained high amounts of IL-17A ( 3061±1587 pg/ml ) and IL-22 ( 1127±401 pg/ml ) in addition to low levels of IL-17F ( 17 . 76±2 . 35 pg/ml ) while only low amounts of IL-13 ( 41 . 13±16 . 76 pg/ml ) were detectable . IL-4 was not produced at all ( Fig 8 ) . Amounts of IL-2 ( 1407±314 pg/ml ) , TNFα ( 56 . 65±19 . 59 pg/ml ) , IL-10 ( 12 . 98±6 . 50 pg/ml ) and IL-6 ( 21 . 49±6 . 14 pg/ml ) were comparable to those produced by cultures with immune wild-type CD4+ T cells ( Fig 8 ) . Thus , immune wild-type CD4+ T cells clearly show a TH1 cytokine profile characterized by the production of IFNγ and TNFα whereas CD4+IFNγ-/- T cells acquire a TH17 phenotype , producing IL-17A , IL-22 and TNFα . The TH2 response by these cells is negligible . Because immune CD4+IFNγ-/- T cells produce no other cytokines than IL-17A , IL-22 and TNFα at higher amounts , the data presented so far strongly suggest that the TH17 cytokines IL-17A and IL-22 are involved in protection by CD4+ T cells in the absence of both IFNγ and TNFα . To test this hypothesis , we finally performed adoptive transfer of CD4+IFNγ-/- T cells into R . typhi-infected CB17 SCID mice and treated the animals either with neutralizing anti-IL-17A or isotype antibody . Control mice were infected with R . typhi and neither received T cells nor antibody . First of all , we analyzed the mice for the presence of T cells in the blood on day 8 post infection . Isotype- and anti-IL-17A-treated CD4+IFNγ-/- T cell recipients showed comparable frequencies of CD4+ T cells among leukocytes in the blood ( isotype: 8 . 90±0 . 95% , anti-IL-17A: 11 . 09±1 . 15% ) ( Fig 9A , left ) . Second , we analyzed the production of plasma cytokines at the same point in time and included plasma from non-infected CB17 SCID mice as an additional control . In these mice IFNγ was negligible ( 17 . 28±7 . 59 pg/ml ) while significantly enhanced amounts of the cytokine were detectable in R . typhi-infected control animals ( 433 . 5±97 . 08 pg/ml ) . Compared to these , isotype-treated CD4+IFNγ-/- T cell recipients showed strongly reduced plasma levels of IFNγ ( isotype: 53 . 98±47 . 90 pg/ml ) while amounts of the cytokine were only reduced by half in anti-IL-17A-treated mice ( 221 . 4±28 . 93 pg/ml ) ( Fig 9A , right ) . These data suggest that IL-17A released by the T cells suppresses the production of IFNγ in CB17 SCID mice where NK cells and macrophages represent the major sources of this cytokine upon R . typhi-infection [21] . Enhanced amounts of other cytokines were not detectable at this early point in time . Contrary to our expectations , however , anti-IL-17A-treated CD4+IFNγ-/- T cell recipients showed much milder disease than isotype-treated animals . All CD4+IFNγ-/- T cell recipients that received anti-IL-17A hardly lost weight upon R . typhi infection ( Fig 9B , left ) , showed a very weak temporary clinical score peaking on day 12 post infection ( Fig 9B , middle ) and survived the infection ( Fig 9B , right ) . In contrast , isotype-treated CD4+IFNγ-/- T cell recipients significantly lost weight between day 14 and 21 compared to anti-IL-17A-treated animals ( Fig 9B , left ) . In addition , isotype-treated CD4+IFNγ-/- T cell recipients developed a significantly higher and prolonged clinical score peaking on day 16 when anti-IL-17A-treated animals already appeared healthy ( Fig 9B , middle ) . Nonetheless , only one mouse of this group died through the infection ( Fig 9B , right ) , demonstrating once more that CD4+ T cells are capable of protecting against R . typhi without the capability to produce IFNγ . We further analyzed the bacterial content in the organs of all animals by qPCR . As expected , PBS-treated control mice that died upon infection showed high amounts of bacteria in the spleen ( 2485±688 . 9 copies ) while the bacterial burden was lower in the brain ( 677 . 7±255 . 2 copies ) , lung ( 330 . 3±84 . 12 copies ) and liver ( 199 . 7±46 . 33 copies ) ( Fig 9C ) . In contrast , all CD4+IFNγ-/- T cell recipients whether treated with isotype antibody or anti-IL-17A were negative for R . typhi in the organs when the experiment was terminated on day 26 post infection . Furthermore , also the only mouse of the isotype-treated CD4+IFNγ-/- T cell recipient group that died upon infection on day 16 had only very low amounts of bacteria predominantly in the liver ( 52 . 0 copies ) followed by the brain ( 20 . 8 copies ) , lung ( 6 . 49 copies ) and spleen ( 1 . 01 copies ) ( Fig 9C ) . These data demonstrate that IL-17A exerts pathological effects similar to TNFα , and that this cytokine is also not essential for bacterial elimination by CD4+IFNγ-/- T cells in vivo .
In the current study we describe the T cell response in R . typhi-infected BALB/c mice and show by adoptive transfer into congenic CB17 SCID mice , a lethal model of R . typhi infection [21] , that both CD8+ and CD4+ T cells are protective , eliminate the bacteria and provide long-term control . The cytotoxic function is not essential for CD8+ T cell-mediated protection . Our results further suggest that CD4+ T cells require either IFNγ , TNFα or TH17 cytokines to activate the bactericidal activity of macrophages and to mediate protection . R . typhi-infected BALB/c wild-type mice developed IFNγ-expressing CD4+ T cells and cytotoxic CD8+ T cells that expressed IFNγ and Granzyme B , an effector molecule that is involved in target cell killing [39] . CD8+ T cells in R . typhi-infected BALB/c mice expressed CD11a and KLRG1 , demonstrating that they were functional antigen-experienced effector cells . Both the CD4+ and CD8+ T cell response peaked on day 7 post infection and then declined as it was also observed in R . typhi-infected C57BL/6 mice [19]and C3H/HeN mice [40] . Surprisingly , in BALB/c mice activated T cells were again detectable late in infection at day 35 . This observation is interesting as we could recently show that R . typhi persists in BALB/c as well as in C57BL/6 wild-type mice [20] . Reactivation of T cells , however , was not observed in R . typhi-infected C57BL/6 mice that instead seem to maintain a certain level of effector cells until day 35 [19] . Comparing the T cell response of BALB/c and C57BL/6 mice it is obvious that both the activation of cytotoxic CD8+ T effector cells and IFNγ-expressing CD4+ T cells both of which are important for the control of intracellular pathogens is less efficient in animals of the BALB/c background upon R . typhi infection . For example , 5% of the CD8+ T cells expressed Granzyme B in R . typhi-infected BALB/c mice at the peak of response on day 7 while 10% of the CD8+ T cells were Granzyme B+ in C57BL/6 mice at this point in time [19] . Similar was true for the CD4+ T cell effector response . Approximately 8% of the CD4+ T cells in BALB/c mice expressed IFNγ on day 7 whereas 14% of the CD4+ T cells were IFNγ+ in C57BL/6 mice [19] . Less efficient induction of TH1 and cytotoxic T cells in BALB/c mice compared to C57BL/6 mice has been also described for other infections such as Leishmania major [41] . The weaker T cell response could be the reason why sporadic reactivation of T cells is necessary to control persisting R . typhi in BALB/c mice while the continuous presence of low numbers of activated T cells is sufficient to control the bacteria in C57BL/6 mice . Adoptively transferred CD8+ T cells efficiently and quickly eliminated the bacteria in R . typhi-infected CB17 SCID mice . The bacteria were almost completely eradicated in the organs already by day 7 post infection in CD8+ T cell recipients . Furthermore , these animals were completely protected from R . typhi-induced disease . This was also true for CD8+ T cells that either lacked Perforin , an essential component of the cytotoxic machinery [39] , or IFNγ . Similar observations were made for the infection of C57BL/6 IFNγ-/- mice with R . australis , a member of the transitional group . Here , adoptive transfer of immune CD8+IFNγ-/- T cells into R . australis-infected C57BL/6 IFNγ-/- mice was protective and reduced the bacterial load [15] . In the CB17 SCID model of R . typhi infection CD8+Perforin-/- as well as CD8+IFNγ-/- T cell recipients remained asymptomatic for a long period of time ( >120 days ) . However , low numbers of R . typhi were again detectable in the organs of animals that had received CD8+IFNγ-/- T cells beyond day 120 but not in CD8+Perforin-/- T cell recipients . In this context , it is interesting that the bacteria were predominantly detectable in the brain which may indicate that R . typhi preferentially persists in this immune privileged organ . It has been suggested that the cytotoxic activity of CD8+ T cells is more important in defense against rickettsiae than the production of IFNγ . For example , C57BL/6 Perforin-/- mice showed a higher susceptibility and lethality upon infection with R . australis compared to C57BL/6 IFNγ-/- mice [15] . Our results , however , suggest that the release of IFNγ is not only sufficient for CD8+ T cell-mediated protection against TG rickettsiae but may be even more important than the cytotoxic activity , at least for long-term control of the bacteria . IFNγ activates the bactericidal activity of macrophages and endothelial cells by inducing the expression of iNOS and subsequent release of NO [31 , 32 , 42] . Both endothelial cells and macrophages are target cells of rickettsiae [1 , 43 , 44] that can eliminate the bacteria with the help of IFNγ . CD4+ T cells from R . typhi-infected BALB/c mice produced very high amounts of IFNγ upon antigen-specific restimulation . Similar to CD8+ T cells the adoptive transfer of CD4+ T cells into R . typhi-infected CB17 SCID mice was protective . In contrast to CD8+ T cell recipients , however , CB17 SCID mice that received CD4+ T cells showed temporary signs of disease and enhanced serum GPT levels with similar kinetics as R . typhi-infected control animals . This observation corresponds with the prolonged presence of R . typhi in the organs of these mice compared to CD8+ T cell recipients . While the bacteria were almost completely eradicated by CD8+ T cells on day 7 post infection , the bacterial load in the organs of CD4+ T cell recipients was reduced by trend compared to that of control animals at this point in time . However , CD4+ T cell recipients then completely recovered from R . typhi-induced disease and appeared healthy by day 21 suggesting bacterial elimination within this time frame . Moreover , although CD4+ T cells were obviously less efficient than CD8+ in bacterial elimination , they provided long-term protection against recurrence of R . typhi . The bacteria were detectable at very low levels in only very few mice beyond day 120 post infection . Thus , CD4+ T cells are sufficient to protect CB17 SCID mice from R . typhi . In line with these observations , immune CD4+ T cells were protective against a lethal infection of C3H/HeN mice with R . conorii [14] and of C57BL/6 RAG1-/- mice with R . typhi [19] . We further show that CD4+ T cells act bactericidally and inhibit bacterial growth in infected macrophages in vitro . CD4+ T cells may acquire cytotoxic function during R . typhi infection in vivo . For example , cytotoxic CD4+ T cells are observed in chronic viral infections of humans , e . g . cytomegalovirus ( HCMV ) , hepatitis virus and human immunodeficiency virus 1 ( HIV-1 ) [45–51] and mice , e . g . lymphocytic choriomeningitis virus ( LCMV ) and gamma-herpes virus [52 , 53] . These cells are characterized by the upregulation of CD11a and the expression of Granzyme B [30] . Both markers were not detectable in CD4+ T cells from R . typhi-infected mice . Therefore , it is unlikely that immune CD4+ T cells from R . typhi-infected BALB/c mice exert direct cytotoxicity . CD4+ T cells most likely mediate bacterial elimination by macrophages via the induction of bactericidal mechanisms via cytokines . Immune CD4+ T cells from R . typhi-infected BALB/c mice induced the release of NO by R . typhi-infected macrophages in vitro and produced high amounts of antigen-specific IFNγ and lower amounts of TNFα in vitro and in vivo upon transfer into R . typhi-infected CB17 SCID mice . Similar to IFNγ , also TNFα induces the expression of iNOS in MΦ [31] and synergizes with IFNγ in this effect [36] . Correspondingly , NO release induced by immune CD4+ T cells from BALB/c wild-type mice was inhibited by the addition of either neutralizing IFNγ- or TNFα-specific antibody , demonstrating that this effect depends in large part on these cytokines . Moreover , recombinant IFNγ and TNFα alone strongly reduced the growth of R . typhi in infected macrophages in vitro , showing the bactericidal effect of both cytokines . Nonetheless , bacterial growth in the presence of immune CD4+ T cells was not completely restored by neutralization of IFNγ or TNFα . This can be explained , however , by incomplete neutralization of the cytokines . Both IFNγ and TNFα have been shown to be important in defense against a number of intracellular pathogens . TNFα enhances the phagocytic activity and helps macrophages to control intracellular growth of Mycobacterium ( M . ) tuberculosis [54] . IFNγ further induces the expression of TNFα in macrophages which is required for IFNγ-mediated induction of iNOS [55] , and coactivation by IFNγ and TNFα is needed for the induction of full bactericidal activity and granuloma formation in M . tuberculosis infection [56] . Similarly , IFNγ and TNFα were found to be important in rickettsial defense . Neutralization of either IFNγ or TNFα leads to enhanced bacterial growth and fatality in R . conorii-infected C3H/HeN mice [17] . Second , C57BL/6 IFNγ-/- mice show enhanced susceptibility and lethality in the infection with R . australis [15] . It has been assumed that TNFα produced by macrophages acts in a synergistic manner with T cell- and NK cell-derived IFNγ on adjacent infected cells such as endothelial cells , hepatocytes and macrophages to induce NO production and rickettsial killing [17] . Indeed , we found that macrophages express TNFα in R . typhi-infected CB17 SCID mice . In addition , NK cells as well as macrophages produce IFNγ in these animals upon R . typhi infection [21] . Nonetheless , the high amounts of IFNγ and TNFα that are released by NK cells and macrophages in CB17 SCID mice upon R . typhi infection are not protective but rather seem to be the reason for death [21] . Together with the findings from the current study these observations suggest that IFNγ and TNFα must be locally provided by T cells to act bactericidal , and that both cytokines are involved in protection mediated by IFNγ-competent CD4+ T cells . Because IFNγ is considered the most important cytokine involved in protection against intracellular pathogens including rickettsiae , it was then surprising that this cytokine is obviously not essential for protection against R . typhi . BALB/c IFNγ-/- mice infected with R . typhi not only survived the infection but were absolutely asymptomatic . Moreover , even adoptive transfer of CD4+ T cells that lack IFNγ still protected 30–90% of the R . typhi-infected CB17 SCID mice . Bacterial numbers in the organs even of those CD4+IFNγ-/- T cell recipients that succumbed to the infection were clearly reduced at the time of death compared to R . typhi-infected control mice , demonstrating bactericidal activity of CD4+IFNγ-/- T cells in vivo . Furthermore , CD4+IFNγ-/- T cells provided long-term control in those animals that survived the infection . Only very low copy numbers of R . typhi were detectable in few animals late post infection ( > d120 ) . Although harboring persistent bacteria these mice appeared healthy . Finally , CD4+IFNγ-/- T cells inhibited the growth of R . typhi in infected macrophages in vitro . Similar to immune CD4+ T cells from wild-type mice , this effect was inhibited at least in part by the neutralization of TNFα . Thus , CD4+ T cells lacking IFNγ obviously exert bactericidal functions and are clearly able to eliminate and control R . typhi by activating macrophages . This may be in part mediated by TNFα . The neutralization of TNFα in R . typhi-infected CD4+IFNγ-/- T cell recipients , however , led to milder disease and enhanced survival , and the cells were still capable of eliminating the bacteria . These findings demonstrate pathological effects of TNFα that were not observed in mice that received wild-type TH1 cells , and that other factors , most likely IL-17A and IL-22 , play a dominant role in protection in this situation . In contrast to immune CD4+ T cells from wild-type mice , CD4+IFNγ-/- T cells produced high amounts of these cytokines in addition to lower levels of IL-17F instead of IFNγ upon antigen recognition . IL-2 and TNFα were produced at equal amounts by wild-type CD4+ and CD4+IFNγ-/- T cells and negligible amounts of TH2 cytokines were released . Other cytokines such as IL-21 that could be involved in bacterial defense were not detectable . IL-17A and F were not produced at all by immune wild-type CD4+ T cells and amounts of IL-22 were very low . These data show that CD4+ T cells preferentially differentiate to TH17 cells that are characterized by the production of IL-17A , IL-17F and IL-22 [57] in the absence of IFNγ . TH17 cells have been mainly involved in protective immunity against extracellular bacterial pathogens such as Klebsiella pneumonia and Citrobacter rodentium [58–60] . In recent years , however , TH17 cells have also been implicated in defense against intracellular bacteria and parasites . IL-17A and F mediate in part similar biological effects [61 , 62] and induce the activation of NF-κB [63–65] and the production of granulopoietic factors such as G-CSF , GM-CSF and stem cell factor and chemokines ( CXCL-1 , CXCL-2 , CXCL-5 , CXCL-8 ) that are involved in the recruitment of neutrophils [57] . These can contribute to parasite elimination . Apart from that , IL-17A/F induce the release of inflammatory cytokines ( TNFα , IL-6 , IL-1β ) and matrix metalloproteases ( MMPs ) in tissue cells and macrophages [66–70] and of antimicrobial peptides in tissue cells in concert with IL-22 [71 , 72] . In addition , both IL-17A and IL-22 exert direct antiparasitic effects in target cells . IL-17A reduces the growth of the intracellular bacterium Chlamydia ( C . ) muridarum in the lung of infected mice in vivo and in lung epithelial cells and macrophages in vitro in an iNOS-dependent manner [73] . IL-17A promotes IFNγ-induced expression of iNOS and NO release in lung epithelial cells in vivo and acts protective in the infection with C . muridarum [73] . In line with these findings , IL-17A was found to promote the expression of iNOS and the release of NO by macrophages infected with Mycobacterium bovis bacillus Calmette-Guerin ( BCG ) [74] . In addition , IL-17A directly inhibits the growth of the intracellular parasite Trypanosoma ( T . ) cruzi in infected macrophages in vitro [75] . Here , killing of intracellular T . cruzi parasites is mediated via the activation of the NAPDH oxidase [75] that produces superoxide and other reactive oxygen species ( ROS ) that can mediate killing of intracellular pathogens in macrophages and neutrophils similar to NO [76] . A protective activity of TH17 cells was further demonstrated in the infection with T . cruzi in vivo . Adoptive transfer of T . cruzi-specific TCR-transgenic TH17 cells into C57BL/6 RAG1-/- mice protects the animals against a lethal challenge with T . cruzi [75] . Finally , treatment of epithelial cells infected with the intracellular apicomplexan parasite Eimeria ( E . ) falciformis with either IFNγ , IL-22 or IL-17A reduces parasite growth in vitro [77] . Moreover , C57BL/6 IFNγR-/- as well as C57BL/6 IFNγ-/- mice infected with this parasite showed higher pathology and body weight loss but reduced pathogen burden compared to wild-type mice [77] , demonstrating that IFNγ is dispensable for the elimination of this pathogen . As observed for CD4+ T cells in the infection of BALB/c IFNγ-/- mice with R . typhi , the infection of C57BL/6 IFNγ-/- mice with E . falciformis was also associated with increased production of IL-17A and IL-22 by CD4+ T cells , and the authors further show that the neutralization of both IL-22 and IL-17A or IL-22 alone in C57BL/6 IFNγR-/- mice leads to increased parasite load in vivo [77] , indicating a dominant protective effect of IL-22 in this infection . Surprisingly , the neutralization of IL-17A in R . typhi-infected CD4+IFNγ-/- recipient mice led to a very similar outcome as the neutralization of TNFα ( milder disease , reduced body weight loss and increased probability to survive the infection ) , and the animals were still capable of eliminating the bacteria . These findings demonstrate pathological effects of IL-17A . In this context , it is interesting that the neutralization of both IL-17A and IL-22 but not one or the other cytokine alone in E . falciformis-infected C57BL/6 IFNγR-/- mice abolished body weight loss [77] . This indicates pathological effects of IL-17A as well as of IL-22 that have been also implicated in other situations such as progressive airway inflammation in a murine model of bleomycin-induced airway inflammation [78] . As mentioned before , IL-17A induces the release of inflammatory cytokines such as IL-6 , TNFα and IL-1β [66–70] . IL-17A may further synergize with these mediators to aggravate tissue inflammation and damage [79 , 80] which is strongly supported by our observations that suggest synergistic pathological effects of TNFα and IL-17A in R . typhi-infected mice . On the contrary , the release of IL-22 in combination with either TNFα or IL-17A is beneficial and sufficient to mediate protection . We suggest that both IL-17A and TNFα directly act on infected cells such as macrophages and neutrophils similar to IFNγ , activating the bactericidal activity but also inducing inflammatory mediators , while IL-22 may support bacterial elimination by the induction of antimicrobial peptides and other factors in infected non-immune cells such as fibroblasts and endothelial cells . Whether TH17 cells are as effective in bacterial elimination as TH1 cells is not clear . The observation that surviving CD4+IFNγ-/- recipients showed prolonged disease compared to animals that received wild-type CD4+ T cells may argue for a more effective bacterial killing by wild-type TH1 cells and prolonged persistence of R . typhi . Nevertheless , also CD4+IFNγ-/- T cell recipients were almost free of bacteria at the time of death . In addition , the course of disease in CD4+IFNγ-/- T cell recipients after neutralization of either TNFα or IL-17A was mild and comparable to that of animals that had received IFNγ-competent CD4+ T cells . Therefore , a more likely explanation for prolonged disease in CD4+IFNγ-/- T cell recipients is the induction of enhanced pathology due to the combined release of TNFα and IL-17A by CD4+IFNγ-/- T cells . The exact mechanisms how TH17 cytokines mediate protection and/or pathology in R . typhi infection remain to be elucidated . At least , we can conclude that IFN© which is produced by innate immune cells in R . typhi-infected CB17 SCID mice [21] is not involved . The production of IFNγ was not significantly altered upon IL-17A neutralization and reduced rather than enhanced in CD4+IFNγ-/- T cell recipients compared to control animals . Moreover , IFNγ was clearly reduced in isotype-treated CD4+IFNγ-/- T cell recipients , arguing against a contribution of innate-derived IFNγ to bacterial elimination and pathology . Collectively , we show that the cytotoxic activity of CD8+ T cells is not essential for protection against R . typhi while the release of IFNγ by CD8+ T cells seems to be even more important than the cytolytic function for long-term control of the bacteria . We further show that IFNγ-producing CD4+ TH1 as well as TH17 cells that release TNFα , IL-17A and IL-22 protect CB17 SCID mice against R . typhi , most likely by activating the bactericidal activity of macrophages , and that the combined production of TNFα and IL-17A exerts non-beneficial immunopathologic effects .
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Rickettsia typhi causes endemic typhus , a relatively mild disease . However , complications such as encephalitis , hepatitis , pneumonia and others can occur in severe cases . In immunodeficient CB17 SCID mice R . typhi causes a 100% fatal infection and we show here that adoptively transferred CD8+ as well as CD4+ T cells that acquire a TH1 phenotype protect these animals from severe disease and death . We further analyzed the requirements for T cell-mediated protection and found that the cytotoxic function of CD8+ T cells is not essential for protection and that the release of IFNγ by these cells is more critical than the cytolytic activity for long-term control of the bacteria . Surprisingly , CD4+ T cells that lack IFNγ still protect 30–90% of R . typhi-infected CB17 SCID mice . These cells show a TH17 phenotype , producing high amounts of IL-17A and IL-22 in addition to TNFα . In R . typhi-infected CB17 SCID mice that receive IFNγ-deficient CD4+ T cells the neutralization of either TNFα or IL-17A mitigates disease and leads to enhanced survival , demonstrating harmful effects of these cytokines although both of them may contribute to bacterial elimination along with IL-22 . We conclude that CD4+ TH17 cells are sufficient to protect against R . typhi if the detrimental effects of either TNFα or IL-17A can be inhibited . This is the first report demonstrating protection against an obligate intracellular bacterium by CD4+ TH17 cells .
|
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2017
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Cytotoxic effector functions of T cells are not required for protective immunity against fatal Rickettsia typhi infection in a murine model of infection: Role of TH1 and TH17 cytokines in protection and pathology
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Significantly higher prevalence of Strongyloides stercoralis has been reported in chronic alcoholic patients . The aim of this investigation was to report the prevalence of Strongyloides larvae in stools of chronic alcoholic patients with known daily ethanol intake . From January 2001 through December 2003 the results of fecal examinations and the daily ethanol intake were retrieved from the records of 263 chronic alcoholic and from 590 non-alcoholic male patients that sought health care at the outpatients unit of the University Hospital C A Moraes . Alcoholic patients were separated into four groups , with 150g intervals between the groups according to the daily ethanol intake . ( a ) The frequency of Strongyloides was significantly higher in alcoholic patients than in control group ( overall prevalence in alcoholic 20 . 5% versus 4 . 4% in control group; p = 0 . 001 ) . Even in the group with a daily intake of ethanol equal to or less than 150g the prevalence was higher than in control group , although non significant ( 9 . 5% , versus 4 . 4% in control group; p = 0 , 071 ) ; ( b ) the prevalence of Strongyloides in alcoholic patients rises with the increase of ethanol intake ( Pearson's Correlation Coefficient = 0 . 956; p = 0 . 022 ) , even in patients without liver cirrhosis ( Pearson's Correlation Coefficient = 0 . 927; p = 0 . 037 ) . These results confirm and reinforce the hypothesis that chronic alcoholism is associated with Strongyloides infection , which is in direct relationship with the severity of alcoholism , independently of the presence of liver cirrhosis .
The presence of Strongyloides stercoralis larvae was reported to be higher in the stools of chronic alcoholics than in the stools of nonalcoholic patients at the same hospital in two different studies in Brazil ( in Uberlândia , MG [1] and in Vitória , ES [2] ) . The association of ethanol abuse and Strongyloides also has been reported by other researchers , although control groups were not used in these observations . In a report on the prevalence of Strongyloides in patients with liver cirrhosis in Juiz de Fora , MG , Brazil [3] , the data presented showed a high prevalence of the larvae in patients with liver cirrhosis associated with chronic alcoholism . However , the authors did not comment on the possible effect of ethanol abuse on Strongyloides infection . Avendaño et al [4] found 5% of Strongyloides in stools of 106 chronic alcoholics in Costa Rica . These authors did not use controls . However the prevalence observed was higher than the 0 . 9% reported in a national survey for intestinal parasites , although in this survey a less sensitive method to identify Strongyloides larvae was used . Adedayo et al [5] reported 27 cases of disseminated strongyloidiasis in the Caribbean island of Dominica , and found that chronic alcoholism was a relevant factor associated with poor prognosis of infection . Recently , Silva et al . [6] , while describing the presence of Strongyloides infection in HIV/AIDS patients , emphasized that the prevalence was significantly higher in HIV/AIDS patients with abusive use of ethanol than in non alcoholic HIV/AIDS patients . Higher prevalence of Strongyloides has been reported in alcoholic patients with liver cirrhosis and chronic pancreatitis than in alcoholic patients without these complications of alcoholism [1] . Although this observation has not been confirmed in another study with a large sample of chronic alcoholic patients [2] , high prevalence of the parasite in cirrhotic is an indirect demonstration of a correlation between the severity of alcoholism and the prevalence of Strongyloides in alcoholic patients . However great number of heavy drinkers never develops liver cirrhosis or chronic pancreatitis and , in all the reports cited above , there was no attempt to establish a relationship between the severity of alcoholism and the prevalence of Strongyloides . It could be said that the higher the ethanol intake the higher the alterations in the defense mechanisms would be , which could promote the installation and survival of Strongyloides larvae . To test this hypothesis we investigated the frequency of Strongyloides larvae in the stools of chronic alcoholics , whose daily ethanol intake was also evaluated .
Records of 563 alcoholic patients at the outpatient integrated health care unit for alcoholic patients at the University Hospital C A Moraes from January 2001 through December 2003 , were reviewed . The inclusion criteria were: adult male , in which records there was result of fecal examination and sure information on presence or absence of liver cirrhosis and on daily ethanol intake . After reviewing all records , 263 chronic alcoholic patients ( 103 with liver cirrhosis and 160 without cirrhosis ) were included in the study . The results of stool examinations of 590 non alcoholic male patients , admitted to the same hospital , during the same period , were collected from the records and used as a general prevalence of Strongyloides in non-alcoholic patients that received medical care at this hospital . Records of patients who attended to different divisions of the Hospital , and who fulfilled the following inclusion criteria , were selected: adult male , 20–65 years old , in which record there was a result of fecal examination and information necessary to exclude chronic alcoholism . For all patients , alcoholic and non alcoholic , the stool examinations were carried out in three samples using the sedimentation method . The diagnosis of chronic alcoholism was according to the WHO criteria ( F10 . 2 , ICD 10 , 2002 ) . Presence of liver cirrhosis was confirmed by clinical , laboratory , image and endoscopy for esophageal varices . To calculate the correlation between the ethanol intake and the prevalence of Strongyloides larvae in stools , the chronic alcoholic patients were divided into four groups , according to their daily ethanol intake: 60 to 150g/day , 151 to 300g/day , 301 to 450g/day , and over 450g/day . Statistical calculations were carried out using the version 9 . 0 of SPSS for Windows Frequencies were calculated with 95% confidence intervals and the Pearson correlation test was used to correlate the frequency of larvae in stool examinations with the daily ethanol ingestion . To calculate the Pearson's correlation coefficient it was considered as a reference value for daily ethanol ingestion for each group the highest value for each class interval: 150g ( group 60–150g ) , 300g ( group 151–300g ) , 450g ( group 301–450g ) and 600g ( group over 451g ) . Values of p less than 0 . 05 were considered significant . This research was approved by the Ethical Committee of the Federal University of Espirito Santo . All patients signed a consent form allowing the use of data registered in their records for the present research objective .
The mean age was 46 . 5±9 . 1 years ( median 44 ) in the 263 alcoholic patients and 44 . 8±16 . 1years ( median 42 ) in the non alcoholic group . We did not collect sure information on socioeconomic pattern of each alcoholic and non alcoholic patient . The great majority of patients ( 86% ) in both groups came from the neighborhoods of the urban periphery of metropolitan Vitória where people with low income live in similar sanitary conditions . Table 1 summarizes the prevalence of Strongyloides stercoralis in stools of the chronic alcoholics according to the daily intake of ethanol and the frequency of Strongyloides larvae in stools of the non alcoholic group . The overall frequency of Strongyloides in chronic alcoholics was 20 . 5% ( 95% CI = 15 . 7–25 . 3 ) , significantly higher than 4 . 4% ( 95% CI = 2 . 8–6 . 1 ) in non alcoholic patients admitted to the same hospital . Other nematodes identified were , respectively in alcoholic and non alcoholic group: Ascaris lumbricoides ( 6 . 7%×5 . 4% ) , Trichiuris trichiura ( 1 . 5%×1 . 3% ) , Ancilostomidae ( 1 , 14%×0 . 8% ) , Schistosoma mansoni ( 0 . 25%×1 . 0% ) and Taenia sp ( 0 . 02%×0 . 015% ) . No significant differences between the frequencies were observed . Cirrhosis was diagnosed in 103 alcoholic patients . In this group the prevalence of Strongyloides was 17% versus 23% in alcoholic patients without cirrhosis ( p = 0 , 842 ) . The analysis of the frequency of Strongyloides larvae in stools of the different groups of alcoholic patients , separated by the daily ethanol intake , demonstrated a strong correlation between these two variables ( Pearson's correlation coefficient = 0 . 956; p = 0 . 022 ) . In addition , Chi square comparison demonstrated significant differences in the frequency of Strongyloides among the different groups of alcoholic patients ( Chi square = 22 . 54; p<0 . 001 ) , and between each group of alcoholic patients and the non alcoholic group , except for the group with ethanol ingestion 60 to 150g/day , as shown in Table 1 . When analyzed the correlation between the amount of daily ethanol intake and the prevalence of Strongyloides in the group without cirrhosis ( 160 patients ) a significant correlation is maintained ( Pearson's correlation coefficient = 0 . 927 , p = 0 . 037 ) .
The samples analyzed included only male alcoholics , because chronic alcoholism is more frequent in men . At the University Hospital outpatient unit for integrated health care for alcoholics , the male to female ratio for alcoholic patients is around 11∶1 [7] . Although the sedimentation method is not highly sensitivity to Strongyloides larvae identification , it was used in both alcoholic and control groups , allowing an impartial comparison . The results presented here confirm that the prevalence of Strongyloides larvae in stools of chronic alcoholic patients is higher than in non alcoholic patients admitted to the same hospital . Prevalence of other helminthes did not differ between alcoholic and nonalcoholic patients , confirming our previous report [2] . Although we did not collect accurate information on socioeconomic pattern of patients , samples were collected from patients coming from similar neighborhoods ( low-income dwellings ) , where they live in similar sanitary conditions . Thus the conclusion that the prevalence of Strongyloides larvae is higher in alcoholic than in non-alcoholic group may consider the caveats resultant from the assumption that the two samples are comparable . As we reported earlier [2] there was not a significant difference between the frequencies of S stercoralis between patient with or without cirrhosis . In addition , the data demonstrated that there is a positive correlation between the intensity of alcoholism and the prevalence of the parasite in stools and that this positive correlation persists if we consider only the patients without cirrhosis . This observation corroborates and reinforces the hypothesis that chronic alcoholism favors the infection of Strongyloides stercoralis and/or enhances the survival of the worm in the duodenum and/or enhances larval elimination , thus increasing the chance of a positive stool examination . The worm infection and/or survival or increased larval elimination is favored even in absence of liver cirrhosis . Although the observations presented here confirm and reinforce the idea that chronic ethanol intake is a factor associated with Strongyloides infection , there is not a clear-cut explanation for the high frequency of this parasite in alcoholics . We do not know if the high prevalence of Strongyloides in chronic alcoholics is due to low hygiene profiles , favoring the infection or reduction in intestinal motility , favoring autoinfection or immune deregulation induced by ethanol , favoring parasite survival and autoinfection . The reduction of gastrointestinal transit caused by effects of ethanol on muscle proteins or on vagal stimulation [8] , [9] may be a factor enhancing autoinfection . Decreased intestinal motility permits delay of rhabditiform larvae in intestinal lumen , favoring the chance of their maturing to the infective filariform larvae , thus increasing the risk of autoinfection [10] . Oliveira et al . [1] and Zago-Gomes et al . [2] emphasize immune deregulation as the main factor favoring parasite survival and autoinfection . In fact chronic ethanol abuse interferes with the innate and adaptive immune response , both in human beings and experimental animals ( review in [11]–[16] ) . There are defects in T cell functions , especially in helper T cell type 1 ( TH1 ) immune response [17] . However , it seems that helper T cell type 2 ( TH2 ) immune response is not impaired by the consumption of ethanol [18] , [19] . Mice chronically treated with ethanol presented normal response to larvae of S . stercoralis inoculated into chambers implanted in the subcutaneous tissue [20] . For these reasons it is difficult to pin point immunosuppression induced by ethanol as the major factor for the increased prevalence of Strongyloides in chronic alcoholics , because the TH2 response , the most important resistance against this worm in humans [21] , [22] , is not depressed by ethanol abuse . However , cells of the innate immune system may have functional impairment in chronic alcoholics ( review in [13]–[16] ) and some of these cells as neutrophils and eosinophils , play a role in innate and adaptive immune response to Strongyloides larvae , as demonstrated in mice [23] , [24] . Other mechanisms triggered by ethanol abuse may be involved in the development of rhabditiform larvae , increasing their differentiation into infective filariform larvae , thus favoring autoinfection . In this way , the number of females would be increased in the duodenum , increasing the number of rhabditiform larvae in the stools , enhancing their chance to be found in a stool examination . Two such mechanisms may be considered: ( a ) an increased production of steroid metabolites that resemble ecdysones [25] and ( b ) a deregulation of immune response leading to alterations in antibody or T cells functions , that can interfere with larval development , as demonstrated in experimental animals [26] and humans [27] . Ethanol influences steroid metabolism by interfering with the activity of hepatic microsome oxidases [28] and increasing the corticoid production by enhancing the activity of the hypothalamus-hypophysis-axis [29] . Both effects can increase the level of steroid metabolites and some of them are similar to ecdysones , which are hormones that regulate the development of the nematode larvae [25] . Thus the increased levels of corticoid metabolites could enhance the fecundity of female worms , increasing the number of rhabditiform larvae and the direct development of these larvae into the infective filariform larvae , favoring autoinfection . Although ethanol abuse preserves the TH2 function , it is possible that the depressed TH1 function and reduction in innate immune mechanisms may interfere with immune mechanisms that influence the developmental choice of developing rhabditiform larvae . The possible influence of host immune response on larval development is supported by the evidence , in rats , that the absence of anti-Strongyloides ratti immune response favors the direct development of larvae into the infective filariform larvae , while the indirect development into free living male and female adults is favored in the presence of such immune response [26] . An apparent opposite effect of immune dysfunction seems to affect the development of S . stercoralis larvae , as demonstrated in HIV infected patients , super-infected with the nematode . An inverse relationship between the number of CD4+ circulating T cells and the direct development of rhabditiform larvae into infective filariform larvae was demonstrated in those patients [27] , an indication that immune deregulation also interferes with the development of S . stercoralis larvae . Although the effect of host immune status on the development of Strongyloides is different between S . ratti and S . stercoralis in their respective hosts , it is possible that immune deregulation may influence the larval development . As chronic alcohol abuse interferes with the immune response , this immune deviation may be favoring the direct development of rhabditiform larvae into infective filariform larvae thus enhancing autoinfection and increasing the number of worms in the duodenum and the chance of finding larvae in the stool examination . In conclusion our results indicate that ethanol abuse is a factor associated with the high prevalence of Strongyloides larvae in stools , with a positive correlation to the intensity of alcoholism , even in absence of liver cirrhosis . The association of chronic alcoholism with Strongyloides infection may be either by the direct effect of ethanol on the immune system , or by factors closely associated to living conditions of severely alcoholized persons . Further investigations are necessary to uncover the possible mechanisms involved in the increased number of Strongyloides larvae in the stools of chronic alcoholic patients .
|
It has been reported that Strongyloides stercoralis infection is more prevalent in chronic alcoholic patients than in non alcoholics living in the same country . In a retrospective study on the prevalence of S . stercoralis infection in a large sample of alcoholic patients , we demonstrate that this prevalence is significantly higher than in non-alcoholic patients admitted at the same hospital . Moreover , the frequency of the parasite was in close relationship with the daily amount of ingested ethanol , even in the absence of liver cirrhosis , reinforcing the idea that chronic alcoholism is associated with increased susceptibility to Strongyloides infection . Beside the bad hygiene profile of alcoholic patients , which explains high risk for acquisition of the parasite , the high prevalence of S . stercoralis in alcoholics may be in relationship with other effects of ethanol on the intestinal motility , steroid metabolism and immune system , which could enhance the chance of autoinfection and the survival and fecundity of females in duodenum . In this way , the number of larvae in the stools is higher in alcoholic patients , increasing the chance of a positive result in a stool examination by sedimentation method .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/helminth",
"infections"
] |
2010
|
Alcoholism and Strongyloides stercoralis: Daily Ethanol Ingestion Has a Positive Correlation with the Frequency of Strongyloides Larvae in the Stools
|
In South America , various species of Leishmania are endemic and cause New World tegumentary leishmaniasis ( NWTL ) . The correct identification of these species is critical for adequate clinical management and surveillance activities . We developed a real-time polymerase chain reaction ( PCR ) assay and evaluated its diagnostic performance using 64 archived parasite isolates and 192 prospectively identified samples collected from individuals with suspected leishmaniasis enrolled at two reference clinics in Lima , Peru . The real-time PCR assay was able to detect a single parasite and provided unambiguous melting peaks for five Leishmania species of the Viannia subgenus that are highly prevalent in South America: L . ( V . ) braziliensis , L . ( V . ) panamensis , L . ( V . ) guyanensis , L . ( V . ) peruviana and L . ( V . ) lainsoni . Using kinetoplastid DNA-based PCR as a gold standard , the real-time PCR had sensitivity and specificity values of 92% and 77% , respectively , which were significantly higher than those of conventional tests such as microscopy , culture and the leishmanin skin test ( LST ) . In addition , the real-time PCR identified 147 different clinical samples at the species level , providing an overall agreement of 100% when compared to multilocus sequence typing ( MLST ) data performed on a subset of these samples . Furthermore , the real-time PCR was three times faster and five times less expensive when compared to PCR - MLST for species identification from clinical specimens . In summary , this new assay represents a cost-effective and reliable alternative for the identification of the main species causing NWTL in South America .
The leishmaniases are a globally widespread group of vector-borne diseases that are endemic to 88 countries and affect an estimated 12 million people , with approximately 350 million people at risk worldwide [1] . Depending on the parasite species and host genetic background , infection can range from self-healing cutaneous ulcers to disfiguring mucocutaneous forms and lethal visceral disease [2] . In South America , New World tegumentary leishmaniasis ( NWTL ) is mainly caused by species of the Viannia complex . The most prevalent species are L . ( V . ) braziliensis , L . ( V . ) panamensis , L . ( V . ) peruviana , L . ( V . ) guyanensis and L . ( V . ) lainsoni [1]–[2] . In Peru , 70% of the country's region is affected with leishmaniasis , 96% of which are caused by three species: L . ( V . ) braziliensis , L . ( V . ) peruviana and L . ( V . ) guyanensis [2]–[3] . Diagnosis of leishmaniasis is based on criteria that consider epidemiological data , clinical features and laboratory test results [2] . Most laboratory methods are based on findings of the etiological agent microscopically or by culture; however , they have relatively low sensitivity and they do not identify the infecting species [2] . Serological tests and the leishmanin skin test ( LST ) have also been used as diagnostic tools but they do not distinguish between present and past infections , and their specificity is low in endemic areas [2] . A variety of molecular approaches have been developed . For instance , PCR methods based on the detection of kinetoplastid DNA ( kDNA PCR ) are highly sensitive due to the presence of thousands of copies of these sequences in the parasite . Thus , correct diagnosis of leishmaniasis should be based on clinical suspicion of leishmaniasis with a confirmed laboratory diagnosis , where the physician must take into account the reliability of the given laboratory result . The correct diagnosis and characterization of the infecting parasite may be important for directing appropriate treatment and evolution of the disease [2] . For instance , patients infected with L . ( V . ) braziliensis have a higher risk of developing the disfiguring mucosal manifestations [4] . In addition , different species show varying response rates to therapeutic drugs [5] . Therefore , early identification of the etiological species may lead to improved patient management . The identification of Leishmania species has been traditionally performed by multilocus enzyme electrophoresis ( MLEE ) , for which mannose phosphate isomerase ( MPI ) is the only reliable marker for discrimination between the closely related species L . ( V . ) braziliensis and L . ( V . ) peruviana [6] . However , this technique presents several drawbacks: a ) it can only be applied to culture-positive cases; b ) it requires the isolation and mass growth of the parasite; and c ) it is time-consuming , taking up to six weeks for definitive parasite identification . These limitations underscore the pressing need for improved identification methods that are fast , cost-effective and more reliable for the diagnosis and characterization of leishmaniasis cases [7] . Several real-time PCR and melting curve analysis using SYBR Green or fluorescence resonance energy transfer ( FRET ) probes in combination with kDNA or 18S rDNA amplification have recently been reported for the detection and identification of Old and New World Leishmania species . L . ( V . ) braziliensis has been the only New World Leishmania species primarily included in these assays [8]–[13] . Another study revealed that a SYBR Green-based real-time PCR assay targeting the conserved region of kDNA mini-circles was able to differentiate between L . ( Leishmania ) and L . ( Viannia ) at the complex level [14] , although this assay did not distinguish species within each complex . Other molecular approaches such as PCR followed by restriction fragment length polymorphism ( PCR-RFLP ) [15]–[16] , multilocus sequence typing ( MLST ) [17]–[18] or multiplex PCR [19] have been used to discriminate species within the Viannia complex . However , these approaches present a number of limitations , including laborious procedures , complex data interpretation and long processing times for species identification [8]–[10] , [13]–[14] , [17] , [19] . We recently identified new species-specific genetic polymorphisms in the genes that confer the phenotypic variations in the MLEE assay [18] . A combination of sequencing of the MPI and 6-phosphogluconate dehydrogenase ( 6PGD ) genes was sufficient to differentiate among seven closely related species causing New World leishmaniasis [18] . In this study , we took advantage of these polymorphisms in order to devise a new real-time PCR assay based on FRET technology and melting curve analysis . The assay was highly sensitive and correctly identified each of the five species of Leishmania being evaluated . Because of its reliability , short turnaround time and simplicity , this assay could be used for species identification in routine laboratory diagnosis of leishmaniasis in endemic regions , thus allowing better management of patients affected with NWTL .
Leishmania reference strains were obtained from the World Health Organization ( WHO ) by Lucas et al . [3] . Leishmania isolates were previously obtained from patients enrolled using a written informed consent [3] and anonymized for this study . Isolates from the Instituto de Medicina Tropical “Alexander von Humboldt” ( IMTAvH ) samples were obtained from clinical cases seen in 2008 and sent to the U . S . Naval Medical Research Unit No . 6 ( NAMRU-6 ) for diagnosis confirmation and species identification . Samples from the Hospital Militar Central ( HMC ) were collected by the Peruvian Army during an investigation of an outbreak ( February-April 2010 ) , and sent to NAMRU-6 for diagnosis confirmation and species identification . The analysis of both sets of samples was approved by the Institutional Review Board ( IRB ) of NAMRU-6 in compliance with all applicable federal regulations governing the protection of human subjects . All samples were anonymized before being sent to NAMRU-6 . Five Leishmania reference strains from the WHO and 59 well-characterized Leishmania strains were assessed to determine the melting curves for each MPI and 6PGD locus . Strains and isolates belonged to species of the subgenus Viannia: L . ( V . ) braziliensis , L . ( V . ) peruviana , L . ( V . ) guyanensis , L . ( V . ) panamensis , and L . ( V . ) lainsoni . Reference strains for L . ( Leishmania ) amazonensis , L . ( L . ) mexicana and L . infantum ( syn . L . chagasi ) [20]–[21] were included as well . Species identification of all reference strains and isolates was previously performed by MLEE [3] and confirmed by MLST [18] . Clinical samples from 192 patients with leishmaniasis-like lesions were prospectively collected from the IMTAvH ( n = 117 ) and the HMC ( n = 75 ) in Lima , Peru . These clinics are the National Reference Centers for leishmaniasis diagnosis and treatment within the Peruvian Ministry of Health and Ministry of Defense , respectively . In total , lancet scrapings ( n = 14 ) or punch biopsy samples ( n = 178 ) were collected from both centers and processed for direct microscopic observation after Giemsa staining . For some patients , aspirate samples were collected from skin lesions using sterile technique and inoculated into Senekji's blood-agar medium as previously described [3] . In a subgroup of patients , the LST was performed as previously published [22] . Briefly , leishmanin antigen ( 0 . 1 ml ) from the L . ( V . ) guyanensis strain LP52 ( IPRN/PE/87/Lp52 ) was injected intradermally into the forearm , and the extent of induration and erythema measured after 48 h . The LST result was considered positive if the diameter of the induration was 5 millimeters or more . All specimens were processed for kDNA PCR , which is specific for the Leishmania Viannia complex [23] , and the FRET-based real-time PCR assay . Overall , all Leishmania strains in this study ( isolates and clinical samples ) are considered geographically diverse since they were isolated from cases occurring across the Peruvian coast ( Lima , Piura , La Libertad and Lambayeque ) , the highlands ( Ancash , Apurimac , Cerro de Pasco , Cajamarca , Cuzco and Junín ) and the Amazon rainforest ( Amazonas , Huánuco , Loreto , Madre de Dios , San Martin and Ucayali ) . Furthermore , we also analyzed 13 L . ( V . ) panamensis strains ( 4 isolated from Ecuador and 9 from Colombia ) , but these results are not included in this manuscript . DNA was isolated from parasite culture or clinical samples using the QIAamp DNA mini kit ( QIAGEN ) following the manufacturer's instructions . DNA pellets were resuspended in Tris-EDTA buffer and quantified using a NanoDrop 1000 Spectrophotometer . Conventional PCRs for kDNA targeting mini-circles DNA were carried out to diagnose all specimens as a gold standard . This kDNA PCR can detect all Leishmania species from the Viannia complex [23] and has shown high sensitivity and specificity on different type of samples [24] . The PCRs were performed using a Gene Amp PCR System 9700 thermocycler ( Applied Biosystems , Foster City , CA ) in a total volume of 20 µl containing 4 µl DNA , 1X PCR buffer ( Invitrogen ) , 0 . 5 µM of each primer ( MP1-L: 5′-TAC TCC CCG ACA TGC CTC TG-3′ and MP3-H: 5′-GAA CGG GGT TTC TGT ATG C-3′ ) , 1 U Taq DNA Polymerase ( Invitrogen , Grand Island , NY ) , 1 . 5 mM MgCl2 , and 125 µM of each dNTP . Initial denaturation at 94°C for 5 min was followed by 35 cycles of denaturation at 94°C for 45 sec , annealing at 58°C for 45 sec , and extension at 72°C for 60 sec; and a final extension at 72°C for 5 min . Amplified products were analyzed on 2% agarose gels; the expected product size is 70 bp . Conventional PCRs for MPI and 6PGD genes were carried out prior to the nested real-time PCR amplifications in order to increase sensitivity of the assays . The initial PCRs were performed using a Gene Amp PCR System 9700 thermocycler ( Applied Biosystems ) in a total volume of 50 µl containing 5 µl DNA , 1X PCR buffer ( Invitrogen ) , 1 µM of each primer ( Table 1 ) , 1 . 5 U Platinum Taq DNA Polymerase ( Invitrogen ) , 1 . 5 mM MgCl2 , and 200 µM of each dNTP . Initial denaturation at 94°C for 5 min was followed by 35 cycles of denaturation at 94°C for 45 sec , annealing at 57°C ( for MPI ) or 62°C ( for 6PGD ) for 45 sec , and extension at 72°C for 90 sec; and a final extension at 72°C for 7 min for MPI or 5 min for 6PGD . The subsequent PCR products were then used to perform the nested real-time PCR assays without further processing . Independent real-time reactions for MPI and 6PGD genes were performed in a LightCycler 480 Instrument ( Roche Applied Science ) . Reactions were carried out in a 20 µl total volume containing 1X LightCycler 480 Genotyping Master ( Roche ) , 1 . 25 µM of forward primer , 0 . 25 µM of reverse primer , 0 . 18 µM of anchor probe , 0 . 18 µM of sensor probe ( Table 2 ) , and 5 µL of DNA ( for reference strains used as positive controls ) or PCR products . The amplification setting consisted of an initial denaturation at 95°C for 5 min followed by 45 cycles of denaturation at 95°C for 10 sec , annealing at 60°C for 20 sec ( a single acquisition step ) and extension at 72°C for 20 sec . After amplification , a melting curve analysis was performed by heating the real-time PCR products at 95°C for 10 sec , cooling at 50°C for 59 sec and then increasing the temperature to 80°C while continuously monitoring the fluorescence ( one acquisition step per °C ) . Melting curves were analyzed using the LightCycler 480 Software Version 1 . 0 release 1 . 1 . 0 . 0520 ( Roche Molecular System , Penzberg , Germany ) to determine the species-specific melting temperatures ( Tm ) . To calculate and enhance the visualization of the Tm values , melting peaks were derived from the initial melting curves ( fluorescence [F] versus temperature [T] by plotting the negative derivative of fluorescence over temperature –dF/dT versus dT ) [12] . A 483–670 nm filter combination was used for MPI while a 483–610 nm combination was used for 6PGD . We followed all appropriate recommendations to avoid cross-contamination , including physical separation of PCR reaction and amplification products , use of UV light to eliminate DNA traces on work surface , aliquoting reagents and master mixes , etc [25] . For conventional PCR assays , L . ( V . ) braziliensis DNA ( 1 ng ) was used as a positive control and nuclease-free water was used as a negative control . For real-time PCR assays , DNA ( 1 ng ) from L . ( V . ) braziliensis , L . ( V . ) peruviana , L . ( V . ) guyanensis , L . ( V . ) panamensis , L . ( V . ) lainsoni , L . ( L . ) amazonensis , and L . ( L . ) mexicana strains were used as both references and positive controls and nuclease-free water was used as a negative control . Seventy-two clinical samples were assessed by MLST as described previously [8] . Briefly , MPI and 6PGD specific primers were used to amplify each product individually . Direct sequencing was performed using the BigDye Terminator v3 . 1 cycle sequencing kit ( Applied Biosystems ) and analyzed on an ABI Prism 3100×l automated DNA sequencer ( Applied Biosystems ) . Sequence analysis was performed using Sequencher v4 . 8 ( Gene Codes Corporation , Ann Arbor , MI ) . PCR products of the MPI and 6PGD genes of L . ( V . ) braziliensis , L . ( V . ) peruviana , L . ( V . ) guyanensis , and L . ( V . ) panamensis were purified and cloned into pGEM-T Easy Vector System ( Promega ) . Serial dilutions from 106 to 1 plasmid copy were evaluated in single and multiplexed reactions . The detection limit was defined as the minimum number of plasmids that could be amplified and correctly identified by melting curve analysis . Consumables , laboratory reagents and labor were considered in the cost estimations . Total costs per type of assay were calculated for a batch of 10 samples plus positive and negative controls and divided by 10 to estimate costs per individual sample . MPI and 6PGD assays were considered as a combined analysis for cost analysis . Fixed costs such as facility space , electricity , air conditioning , etc . , as well as costs and labor associated with DNA isolation were considered equivalent between the two methods and were not included for this analysis . All costs were obtained directly from U . S . -based manufacturers and shipping costs were not taken into consideration . Data were analyzed using Stata v11 . 0 for Windows ( Stata Corporation , College Station , TX ) . Descriptive statistics ( mean , standard deviation , median , range ) were calculated for continuous variables . Sensitivity , specificity and predictive values including 95% confidence intervals ( 95% CI ) were estimated for all diagnostic tests using kDNA PCR as the gold standard . Statistical differences between these values were estimated using the exact McNemar test for matched observations . Categorical variables were assessed by proportions , and differences among the groups were compared using Fisher's exact chi-square analysis . p-values less than 0 . 05 were considered statistically significant .
We designed two sets of primers and hybridization probes capable of distinguishing previously identified single nucleotide polymorphisms ( SNPs ) in the MPI and 6PGD loci [18] . Hybridization probes for the MPI gene were designed to detect the C1082G mutation , which differentiates between L . ( V . ) braziliensis and L . ( V . ) peruviana [19] . Several SNPs present in the region spanned by the anchor and sensor probes were found to differentiate L . ( V . ) lainsoni and L . infantum ( syn L . chagasi ) . The probes for the 6PGD gene were designed around the C1263G SNP , which differentiates between L . ( V . ) guyanensis and L . ( V . ) panamensis [18] ( Table 2 ) . Next , we performed melting curve analysis of the amplification products for both loci in order to assess whether intraspecific genetic variability affected correct species discrimination . For this purpose , we used five reference strains and 59 well-characterized Leishmania isolates that were previously differentiated by MLEE and MLST [3] , [18] . The MPI-based assay yielded non-overlapping Tm values calculated for the melting peaks of L . ( V . ) braziliensis ( 74 . 0±0 . 1°C , mean ± standard deviation ) , L . ( V . ) peruviana ( 70 . 1±0 . 3°C ) , L . ( V . ) lainsoni ( 67 . 8±0 . 2°C ) , and L . infantum ( 58 . 7°C ) , allowing for a method of discrimination among these species ( Table 3 and Figure 1 ) . However , this assay did not discriminate between L . ( V . ) guyanensis and L . ( V . ) panamensis or L . ( L . ) amazonensis and L . ( L . ) mexicana because their Tms overlapped ( Table 3 and Figure 1 ) . When the 6PGD-based real-time PCR assay was used , we obtained clearly different Tms for L . ( V . ) guyanensis ( 60 . 2±0 . 1°C ) , L . ( V . ) panamensis ( 56 . 2±0 . 7°C ) , and L . ( V . ) lainsoni ( 65±0 . 2°C ) , thus allowing identification of these species ( Table 3 and Figure 2 ) . In addition to being able to differentiate five Leishmania species of the Viannia complex , the MPI-based real-time PCR gives distinct Tm for L . infantum , which belongs to the Leishmania donovani complex . However , this assay cannot distinguish between L . ( L . ) amazonensis and L . ( L . ) mexicana , two species of the Leishmania mexicana complex ( Figure 1 ) . Overall , the combined use of both MPI- and 6PGD-based real-time PCR assays allows the correct identification of five New World Leishmania species of the Viannia complex and one species of Leishmania donovani complex ( Table 3 and Figures 1 and 2 ) . Melting peaks were not observed for negative controls , nuclease-free water or non-Leishmania DNA , indicating the absence of primer-dimers formation ( Figures 1 and 2 ) . Thus , Tm values could not be calculated . Following optimization of the real-time PCR conditions , we determined the analytical sensitivity of the assays . The MPI- and 6PGD-based real-time PCR assays detected as few as one copy of plasmid when carried out in separate reaction tubes ( data not shown ) . When the MPI and 6PGD assays were multiplexed into a single reaction , the detection limit was 10–100 copies ( data not shown ) . No cross-reaction was observed when the two real-time PCR assays were used to test DNA from Homo sapiens ( 20 ng DNA ) , Trypanosoma cruzi ( ATCC 30013 strain , American Type Culture Collection , 1–2 ng DNA ) , Trypanosoma cruzi Tulahuen strain ( 1–2 ng DNA ) , Plasmodium falciparum D6 strain ( 1–2 ng DNA ) , and P . vivax OBT9140 isolate ( 20 ng DNA ) . A total of 192 specimens belonging to the same number of individuals with leishmaniasis-like lesions were evaluated ( 178 biopsy and 14 lesion scraping samples ) . Of these , 178 samples ( 92 . 7% ) showed a positive result by at least one of the four diagnostic methods ( microscopy , culture , LST and kDNA PCR ) . One hundred sixty-six samples ( 86 . 5% ) were positive by kDNA PCR . When kDNA-PCR was used as the gold standard for leishmaniasis diagnosis , the real-time PCR had a sensitivity and specificity of 92% and 77% , respectively , and the positive and negative predictive values were 97% and 59% , respectively ( Table 4 ) . The sensitivity of the real-time PCR among kDNA PCR positive samples was superior to that of each of the other diagnostic tests , including microscopy ( 93% vs . 59% , p<0 . 001 ) , culture ( 93% vs . 31% , p<0 . 001 ) and LST ( 93% vs . 68% , p<0 . 001 ) ( Table 4 ) . Stratification of the analysis by site revealed that the sensitivity of the real-time PCR tended to be higher among samples collected from IMTAvH compared to those collected at HMC ( Table 5 ) . The analysis of the discordant samples showed that fourteen samples were positive by kDNA PCR but negative by real-time PCR ( false negative ) , and six samples were negative by kDNA PCR but positive by real-time PCR ( false positive ) . Of the fourteen samples classified as false negatives , one was positive by microscopy and thirteen other samples were consistently negative by both microscopy and culture , likely indicative of low levels of parasites in these specimens ( Table S1 ) . The presence of PCR inhibitors was ruled out in these fourteen samples since DNA preparations in a 10-fold dilution were tested by PCR assays targeting two human housekeeping genes: β-globin and Ribonuclease P . While both undiluted and diluted samples were positive for β-globin and Ribonuclease P , all remained negative by the MPI and 6PGD real-time PCR assays . Additionally , of the six specimens classified as false positive , four tested positive by microscopy or LST ( Table S2 ) , indicating that these four samples ( two only detected by 6PGD real-time PCR ) likely were truly positive but not detected by kDNA PCR . Among the 158 clinical samples that tested positive by real-time PCR , we were able to correctly identify 147 ( 93% ) specimens to the species level . Four Leishmania species were identified among the IMTAvH samples: L . ( V . ) peruviana ( 43% ) , L . ( V . ) braziliensis ( 30% ) , L . ( V . ) guyanensis ( 12% ) , and L . ( V . ) lainsoni ( 3% ) . Two species were identified among the HMC samples: L . ( V . ) braziliensis ( 97% ) and L . ( V . ) guyanensis ( 3% ) ( Table 6 ) . In 11 IMTAvH clinical samples ( 11% ) , the MPI real-time PCR failed to amplify but 6PGD real-time PCR did amplify . The 6PGD real-time PCR results suggested infection by either L . ( V . ) peruviana or L . ( V . ) braziliensis . However , in the absence of MPI results , we could not discriminate between these Leishmania species ( Table 6 ) . In a subset of 72 clinical samples , regions of the MPI and 6PGD genes were sequenced as previously described [18] . The concordance between the real-time PCR and the MLST analysis for species identification was 100% ( Table S3 ) . In summary , the real-time PCR assays reliably discriminated among New World Leishmania species that are highly prevalent in South America . We estimated the turnaround time and assay costs between the real-time PCR and the combination of kDNA PCR plus MLST for the specific identification of Leishmania species . The real-time PCR assay had lower costs and required less processing time compared to kDNA PCR plus MLST for the correct identification of Leishmania species ( Table 7 ) .
In this report , we describe the development and evaluation of a real-time PCR assay for the diagnosis and characterization of NWTL species from tissue samples . This assay has several advantages: 1 ) It is highly sensitive , detecting as few as one copy of DNA , i . e . half of a diploid parasite genome , 2 ) It has higher sensitivity and specificity for the diagnosis of NWTLs when compared to conventional diagnostic tests , 3 ) It can reliably identify the infecting Leishmania species , including L . ( V . ) braziliensis , L . ( V . ) panamensis L . ( V . ) peruviana , L . ( V . ) guyanensis , and L . ( V . ) lainsoni ( the most prevalent species causing NWTL in South America ) [1]–[2] , and 4 ) It is three times faster and five times less expensive compared to MLST for species identification . Because of these features , this real-time PCR assay can be a valuable contribution to the diagnosis and management of leishmaniasis in resource-limited settings . In recent years , PCR has been established as the preferred method of Leishmania diagnosis and species identification due to its higher sensitivity and short turnaround time compared to traditional diagnostic tests [26]–[28] . We recently reported the presence of polymorphisms in various MLEE markers that could discriminate New World species of the Viannia complex [18] . Based on these mutations , we designed a real-time PCR assay to identify Leishmania species of the Viannia complex by virtue of their unique melting profiles . Melting curve analysis of the MPI and 6PGD genes showed non-overlapping curves for five different Leishmania species among 59 isolates , suggesting that intraspecific genetic polymorphisms are not likely to affect correct species identification using this method . Since the strains belonged to patients from diverse regions of Peru ( and a few from Ecuador and Colombia ) , we believe that intraspecific genetic variability will not affect correct species identification by the real-time PCR assay , thus allowing identification of geographically diverse strains of Leishmania species . This is further supported by sequencing analysis showing that the SNPs in the MPI and 6PGD housekeeping genes are extremely well conserved even among genetically diverse strains [18] . The real-time PCR technique performed considerably better when compared to conventional diagnostic techniques such as microscopy , culture and LST in prospectively evaluated individuals with suspected leishmaniasis ( n = 192 ) . Additionally , our study confirmed the low sensitivity of microscopy , culture and LST , which ranged from 50 to 80% among various studies [29]–[31] . Given the high sensitivity values reported for kDNA PCR , we used this assay as a gold standard to assess the performance of the real-time PCR to diagnose NWTL [23] , [29] , [31]–[32] , producing sensitivity and positive predictive values of 92% and 96% , respectively . Fourteen samples gave a positive result for kDNA PCR but were negative by the real-time PCR assay and were , therefore , classified as false negative . We ruled out the presence of PCR inhibitors in DNA preparations . The decreased sensitivity of the real-time PCR may be explained by larger amplicon size amplified for first-round PCR ( ∼1600 bp for the MPI locus versus ∼70 bp for the kDNA PCR ) , since partially-degraded DNA is more likely to amplify shorter amplicons compared to larger amplicons [33] . In keeping with this hypothesis , by shortening the first-round PCR of the MPI real-time PCR to 721 bp , we were able to detect 4 out of 10 initially-negative real-time PCR samples ( data not shown ) . We are currently refining the assay using other strategies such as the use of alternative primers , shorter first-round PCR products and increasing the concentration of magnesium of the first PCR . The mini-circles of kDNA of Leishmania are also present as thousands of copies per parasite , whereas the MPI and 6PGD genes are present as a single copy in the parasite's genome ( Leishmania braziliensis GeneDB ) [19] , which could explain the higher sensitivity of the kDNA PCR assay over the nested PCR approach used in the real-time PCR assay [19] , [34] . The specificity and negative predictive values of the real-time PCR were 77% and 59% respectively , when compared to kDNA PCR . However , 4 out of the 6 false positive samples had a positive result by either microscopy or LST . As a positive smear alone is not sufficient as criterion for a positive diagnosis of leishmaniasis , these results together with the available clinical diagnosis suggests that at least three of these six cases could be true positives that were missed by the kDNA PCR ( Table S2 ) . When these 3 samples were considered true positives , the adjusted specificity of the real-time PCR was 87% . These results highlight the importance of defining the right gold standard criteria for the diagnosis of leishmaniasis [24] , [35] and support the hypothesis that the seemingly suboptimal specificity may be due to misclassification by the kDNA PCR rather than low specificity of the real-time PCR assay . The negative predictive value remained low ( 63% ) after correcting for misclassification . However , this may be a reflection of the very high prevalence of leishmaniasis in the studied group ( overall disease prevalence was ∼85% ) , composed mainly of suspected cases at reference centers . Further studies with larger sample size including subpopulations with lower prevalence of leishmaniasis are needed to confirm our findings and better estimate the sensitivity and specificity of the real-time PCR assay . Several methods for diagnosis and species identification have been developed but most of these procedures only discriminate a few species within the Viannia complex [9] , [13]–[17] , [19] . While the real-time PCR assay we developed has proven to be highly sensitive for the diagnosis of NWTL , its main advantage resides in its ability to simultaneously identify up to five members of the Viannia subgenus . This assay reliably identified 64 archived parasite isolates and 147 prospectively collected skin samples at the species level with 100% concordance with MLST when compared in parallel in a subset of these samples . Besides the potential impact of this assay in the clinical setting , we successfully applied this real-time PCR assay for the identification and characterization of Leishmania species in field-collected sand fly specimens [36] , underscoring the potential broad applicability of this assay . One limitation of this study was the small number of negative samples included given the high prevalence of leishmaniasis in the study settings . This may have resulted in a less precise estimation of the predictive values since only few negative subjects were included in the analysis . An additional study is being planned to provide more accurate estimates of the specificity and the negative predictive value . A second limitation was that we were unable to apply all diagnostic tests simultaneously to all study participants . As a consequence , comparisons across diagnostic tests had to be done in subsets of subjects , which could limit the comparability of the tests . Finally , although we carried out this study at two major reference clinics for leishmaniasis , it remains possible that the strains included in this study do not represent all strains in Peru . Future studies in more diverse groups , including a larger pool of negative samples and belonging to wider geographic areas , are warranted to confirm the results of this study . In summary , our real-time PCR assay can simultaneously diagnose New World leishmaniasis and identify the five causative Leishmania species most prevalent in South America , highlighting its potential regional applicability in all these countries . Thus , given its diagnostic performance , short turnaround time , scalability and relatively low costs , this assay could have great utility in the clinical setting and help to improve case management and direct appropriate therapy for patients with cutaneous and mucocutaneous leishmaniasis in resource-limited countries of South America .
|
Leishmaniasis is a neglected disease with more than two million new human infections annually worldwide . Tegumentary leishmaniasis , cutaneous and mucocutaneous , is mainly caused by five Leishmania species of the Viannia complex in South America . Different species can cause disease with similar symptoms but have dissimilar prognoses and may need different therapeutic regimens . Identification of Leishmania species traditionally relies on the multilocus enzyme electrophoresis ( MLEE ) assay , but it can only be applied to culture-positive samples and takes at least six weeks of intense laboratory work . A reliable and rapid assay for species identification can be a valuable tool . Molecular assays are the fastest and most accurate way to identify the etiological agents causing leishmaniasis . This paper describes a novel real-time PCR assay for identification of the five main species that cause tegumentary leishmaniasis in the New World . The assay correctly identified each of these five species of Leishmania directly from clinical samples . Because of its reliability , speed and simplicity , this assay could be used for species identification in routine laboratory diagnosis of leishmaniasis in endemic regions .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"parastic",
"protozoans",
"medicine",
"infectious",
"diseases",
"test",
"evaluation",
"diagnostic",
"medicine",
"leishmania",
"leishmaniasis",
"neglected",
"tropical",
"diseases",
"protozoology",
"biology",
"microbiology",
"parasitic",
"diseases"
] |
2013
|
A FRET-Based Real-Time PCR Assay to Identify the Main Causal Agents of New World Tegumentary Leishmaniasis
|
Streptococcus suis is the most common cause of meningitis in pork consuming and pig rearing countries in South-East Asia . We performed a systematic review of studies on S . suis meningitis to define the clinical characteristics , predisposing factors and outcome . Studies published between January 1 , 1980 and August 1 , 2015 were identified from main literature databases and reference lists . Studies were included if they were written in West-European languages and described at least 5 adult patients with S . suis meningitis in whom at least one clinical characteristic was described . We identified 913 patients with S . suis meningitis included in 24 studies between 1980 and 2015 . The mean age was 49 years and 581 of 711 patients were male ( 82% ) . Exposure to pigs or pork was present in 395 of 648 patients ( 61% ) while other predisposing factors were less common . 514 of 528 patients presented with fever ( 97% ) , 429 of 451 with headache ( 95% ) , 462 of 496 with neck stiffness ( 93% ) and 78 of 384 patients ( 20% ) had a skin injury in the presence of pig/pork contact . The case fatality rate was 2 . 9% and hearing loss was a common sequel occurring in 259 of 489 patients ( 53% ) . Treatment included dexamethasone in 157 of 300 ( 52% ) of patients and was associated with reduced hearing loss in S . suis meningitis patients included in a randomized controlled trial . S . suis meningitis has a clear association with pig and pork contact . Mortality is low , but hearing loss occurs frequently . Dexamethasone was shown to reduce hearing loss .
Bacterial meningitis is a severe infectious disease with a high mortality and morbidity . The estimated incidence is 2 . 6–6 per 100 , 000 adults per year in developed countries and several times higher in low-income settings [1] . Most pathogens causing bacterial meningitis are transmitted between humans ( e . g . , Streptococcus pneumoniae and Neisseria meningitidis ) , while others can be acquired through food ingestion ( e . g . , Listeria monocytogenes ) [1 , 2] . Transmission of pathogens causing bacterial meningitis can also occur directly from animals to humans , a condition referred to as zoonotic bacterial meningitis . One of the most common zoonotic pathogens causing bacterial meningitis is Streptococcus suis . This pathogen has its natural reservoir in pigs and may cause meningitis , endocarditis and sepsis in humans after contact with pig or pork [3 , 4] . Due to high pork consumption and frequent small scale pig rearing , S . suis infection is endemic in South-East Asia , where several outbreaks and cohort studies of S . suis meningitis have been reported [5–8] . Nevertheless , cases of S . suis meningitis occur all over the world [9] , particularly in patients having occupational contact with pigs or pork , such as abattoir workers and butchers [10] . The clinical manifestations , epidemiology and outcome of S . suis infection in humans were described in a recent systematic review and meta-analysis [9] . This review included studies through 2012 and did not review characteristics of S . suis meningitis separately ( the condition comprised 68% of cases ) . We performed a systematic review on studies on S . suis meningitis to define the clinical characteristics , risk factors and outcome of S . suis meningitis .
We searched the main databases ( PubMed , ScienceDirect , Google scholar ) for published articles describing cases of S . suis meningitis , published from January 1980 to August 2015 . We used the search terms “Streptococcus suis AND meningitis” , and searched the literature for cohort studies using the term “Streptococcus suis” . We also searched the reference lists of the articles identified by this search strategy and selected those that we judged to be relevant . Articles written in English , Dutch , French , German , Spanish , Italian and Portuguese were included . Articles describing at least 5 patients with S . suis meningitis were included if at least one clinical characteristics or ancillary investigation was described , unless no sub-analysis for S . suis meningitis was performed ( e . g . S . suis infection or streptococcal meningitis ) . All articles meeting the inclusion criteria were read and systematically processed into a database of clinical data . The variables were as follows: patient characteristics , predisposing factors , clinical presentation , ancillary investigations , and outcome . Predisposing factors were defined as 1 ) Contact with pigs or pork , defined as preparing pork , consumption of raw pork or other swine materials ( e . g . raw pig blood ) , occupations related to pigs or pork ( e . g . abattoir workers , butchers ) , or breeding pigs at home [4] , and/or 2 ) An immunocompromised status for bacterial meningitis caused by infection with Human Immunodeficiency Virus ( HIV ) , a history of immunosuppressive medication , cancer , splenectomy , or alcoholism [2] . When patients were reported to be ‘not immunocompromised’ , we assumed no immunosuppressive medication , splenectomy or HIV-infection in these patients . Skin injury was defined as cuts or scrapes , since skin rash could be misidentified as bruises ( as seen in meningococcal sepsis ) . As data description was heterogeneous between studies , all data are presented as number for which a characteristic was present out of the total number for which the characteristic was evaluated . We described the relevant characteristics using proportions with 95% confidence intervals ( CIs ) for categorical factors ( sex , predisposing factors ) , and mean with standard deviation ( SD ) for continuous factors ( age , laboratory parameters ) . For the latter , medians were converted to means by using proposed formulas [11] .
In total , 382 articles were screened for eligibility ( 375 by searching the databases and 7 by cross-checking references ) ( Fig 1 ) . 54 articles did not meet the inclusion criteria as they described S . suis infection in animals . 304 articles were excluded from the review as no cases were described ( 183 articles ) , reporting less than 5 cases ( 88 articles ) , no sub-analysis possible for S . suis meningitis ( 10 articles ) , no S . suis infection described ( 9 articles ) , no meningitis described ( 7 articles ) , foreign language ( 5 articles ) and duplicate articles ( 3 articles ) . The 24 articles included in the review described 913 patients [7 , 8 , 10 , 12–32] . The number of included patients per study varied between 5 and 151 ( median 21 ) . The median described time-period of the studies was 6 years ( ranging from 1 to 23 years ) . Studies were performed in Thailand ( 8 studies ) , Vietnam ( 6 studies ) , Hong Kong ( 5 studies ) , the Netherlands ( 3 studies ) , China ( 1 study ) and Japan ( 1 study ) . Studies composed 10 single center studies , 4 multi-center studies and 10 nationwide studies . 11 studies included patients prospectively and 13 were retrospective studies . The pooled mean age was 48 . 8 years ( SD 3 . 9 , reported in 715 cases ) and 581 of 711 patients ( 82% , 95% CI 79–85% ) were male ( Table 1 ) . Predisposing factors consisted of exposure to pig or pork in 395 of 648 patients ( 61% , 95% CI 57–65% ) , alcoholism in 60 of 322 patients ( 19% , 95% CI 15–23% ) , diabetes mellitus in 11 of 209 patients ( 5% , 95% CI 2–8% ) , cancer in 5 of 85 patients ( 6% , 95% CI 1–11% ) , splenectomy in 5 of 507 patients ( 1% , 95% CI 0–2% ) and immunosuppressive medication in 2 of 593 patients ( 0 . 3% , 95% CI 0–0 . 8% ) . The clinical presentation of S . suis meningitis was characterized by fever in 514 of 528 patients ( 97% , 95% CI 96–98% ) , headache in 429 of 451 patients ( 95% , 95% CI 93–97% ) , neck stiffness in 462 of 496 patients ( 93% , 95% CI 91–95% ) , an altered consciousness in 35 of 113 patients ( 31% , 95% CI 23–39% ) and nausea or vomiting in 210 of 321 patients ( 65% , 95% CI 60–70% ) . The classic meningitis triad of fever , neck stiffness and altered consciousness was present in 4 out of 43 patients ( 9% , 95% CI 0–18% ) [2] . Skin injury in the presence of pig/pork contact was present in 78 of 384 patients ( 20% , 95% CI 16–24% ) . The mean blood leukocyte count was 17 . 4 x 109/L ( SD 0 . 9 , reported in 322 cases ) . The mean blood thrombocyte count was 166 . 3 x 109/L ( SD 19 . 1 , reported in 213 cases ) . The mean cerebrospinal fluid ( CSF ) leukocyte count was 1920/mm3 ( SD 757 ) ; it was reported in 395 patients and abnormal in all 913 patients . The mean CSF protein was 2 . 4 g/L ( SD 0 . 8 , reported in 380 patients ) and the mean CSF glucose was 1 . 09 mmol/L ( SD 0 . 60 , reported in 177 patients ) . Data on cerebrospinal fluid cultures were reported in all 913 patients , and were positive in 758 ( 83% , 95% CI 81–85% ) . Blood cultures were positive in 288 of 435 cases ( 66% , 95% CI 62–70% ) . Results of cranial CT were noted in 3 studies describing 27 patients [23 , 28 , 32] and consisted of cerebral edema in 8 of 27 patients ( 30% , 95% CI 10–50% ) . The majority of patients was treated with ceftriaxone ( 250 patients ) or penicillin ( 102 patients ) monotherapy; no antibiotic resistance for these antibiotics was found in the 182 cases where the resistance pattern was determined . Antibiotic resistance for tetracycline was reported in 2 studies [7 , 33] . In some studies , patients were treated with either penicillin or ceftriaxone ( 101 patients ) , but the exact number of patients receiving either treatment was not reported [23 , 24 , 29 , 31] . In 454 patients , the type of antibiotic treatment was unknown . 157 of 300 patients ( 52% , 95% CI 44–60% ) received adjunctive dexamethasone . The majority of these patients were included in a randomized controlled trial in which 71 patients received adjunctive dexamethasone and 69 patients received placebo [7] . In the other studies , dexamethasone was given at the discretion of the treating physician . The case fatality rate was 2 . 9% ( 17 of 581 patients , 95% CI 1 . 9–3 . 9% ) and 116 of 320 patients ( 36% , 95% CI 31–41% ) recovered without sequelae . An association between dexamethasone and death could not be established because numbers of patients who died were small . Data from the RCT showed no patients died in dexamethasone group versus three in the placebo group [34] . Hearing loss was present in 259 of 489 patients ( 53% , 95% CI 49–57% ) . 68 of these patients were screened at admission for hearing loss and this was present in 60 patients ( 88% , 95% CI 80–96% ) . According to a study describing 41 patients with hearing loss in S . suis meningitis , 38 had hearing loss on admission and 3 developed hearing loss during admission [23] . Another study described 16 patients with S . suis meningitis and hearing loss , with hearing loss persisting in 7 patients ( 44% ) [28] . Other neurological sequelae were present in 35 of 286 patients ( 12% , 95% CI 8–16% ) and consisted of ataxia in 19 patients , cognitive impairment in 2 , tinnitus in 2 , and were not specified in 12 . A randomized controlled trial showed that dexamethasone was significantly associated with a reduction in hearing loss in at least one ear ( 38% to 12% , p = 0 . 003 ) and a reduction in severe ( >80 dB ) hearing loss ( odds ratio 0 . 23 [95% CI , 0 . 06–0 . 78] ) , using a multivariate analysis including age >50 and CSF bacterial load [7] . A recent case series from the Netherlands showed that despite dexamethasone treatment 6 out of 7 patients with S . suis meningitis had hearing loss upon discharge [32] .
Meningitis is the most frequently described presentation of S . suis infection , occurring in approximately 50–60% of reported S . suis infected patients [9] . Despite the geographical distribution , there were no significant differences for clinical presentation and outcome in S . suis meningitis between the different studies and low-/high-income countries . In our meta-analysis the main risk factor for S . suis meningitis was exposure to pigs or raw pork . This confirms the findings by a single center case-control study from Vietnam of 101 patients with S . suis infection which showed an odds ratio of 6 . 33 for occupations related to pigs [16] . Another previously reported potential risk factor was alcoholism , which we identified in 16% of patients . Alcoholism was not an independent risk factor for contracting S . suis meningitis when corrected for other predisposing factors in Vietnam [16] . However , alcoholism has been associated with an increased risk of infection in general and of an unfavorable outcome of bacterial meningitis [35] . Skin injury in the presence of pig/pork contact was described in 20% of the cases , which is similar to the previously observed 25% skin injuries in all types of S . suis infections [9] . S . suis may directly pass into the blood stream after exposure to pigs or pork in the presence of skin injuries , even without visible wound infection [10 , 16 , 36] . Patients with an increased risk of infection , e . g . because of splenectomy or use of immunosuppressive medication , should avoid direct pig or pork contact when skin lesions , particularly on the hands , are present . Skin protection has been suggested to reduce the incidence of S . suis infection [16] . Direct exposure to pigs or pork was described for 61% of meningitis cases . Direct pig exposure was documented in the majority of the European cases of S . suis infection , but was reported in less than half of the Asian cases , suggesting that other mechanisms may be involved in those patients [16] . A recent study showed that the gastro-intestinal tract is an entry site for S . suis [37] , supporting the epidemiological evidence that ingestion of S . suis contaminated food is a risk factor for infection [9 , 16 , 38] . The sensitivity of the classic triad of bacterial meningitis consisting of fever , neck stiffness , and altered mental status was low ( 9% ) . This was mainly due to the low frequency of altered mental status , since other symptoms and signs of bacterial meningitis were present in a large proportion of patients . In patients with a history of regular pig exposure or pork consumption , hearing loss and these symptoms , meningitis due to S . suis should be suspected , and CSF examination should be performed to get diagnostic certainty [3] . We found that the mortality of S . suis meningitis was low ( 3% ) , especially when compared to pneumococcal meningitis ( 20% ) and Listeria monocytogenes meningitis ( 36% ) [39 , 40] . The mortality rate was also lower than reported for general invasive infection caused by S . suis ( 13% ) [12] . The difference between mortality in S . suis meningitis and other types of S . suis infection ( such as sepsis ) has been noted before [6 , 8 , 9 , 19] , but the mechanism causing this difference needs to be further elucidated [9] . Similar differences between meningitis and sepsis case fatality rates have been reported for invasive meningococcal disease [41] . The mortality rate was low but many surviving patients have sequelae . The most common sequel is hearing loss occurring in 53% of the patients; variable rates of hearing loss have been reported in other types of bacterial meningitis , with 8% in meningococcal meningitis and 22% in pneumococcal meningitis [2] . Hearing loss in S . suis meningitis may be a presenting symptom or develop during admission [23] , and does not always persist [28] . Different hypotheses for hearing loss in S . suis meningitis are described in the literature such as direct infection of the auditory nerve and suppurative labyrinthitis [42] . For patients with meningitis in whom S . suis is identified , it is important to consult the otorhinolaryngologist early in the clinical course for audiometry and evaluate whether cochlear implantation is possible [43] . Dexamethasone has been shown to decrease mortality in pneumococcal meningitis and to decrease hearing loss and neurological sequelae in all bacterial meningitis cases [44 , 45] . For S . suis meningitis , an effect on mortality has not been established [34] . One randomized controlled trial on dexamethasone in bacterial meningitis , performed in Vietnam , included a substantial number of S . suis meningitis [34] . A subsequent analysis of all S . suis patients showed dexamethasone reduced hearing loss in a multivariate analysis [7] . As a recent case-series showed , hearing loss is still observed in patients treated with dexamethasone [32] , additional randomized clinical trials on the effect of dexamethasone in S . suis meningitis would be desirable to further evaluate whether there is a benefit . However , it is unlikely such a trial is going to be performed for practical and financial reasons . Based on the available evidence , dexamethasone treatment in regions with high rates of S . suis as cause of meningitis appears reasonable to potentially reduce the very high rate of post-meningitic hearing impairment . This review has several limitations . First , most included studies show a selection bias due to a retrospective character . A recent study showed evidence of publication bias in S . suis meningitis [9] . S . suis meningitis is probably underreported , and often in numbers of less than 5 cases , which was an exclusion criterion for this study . Second , reporting of clinical characteristics , ancillary investigations and outcome was highly diverse between the included studies . We have presented the total number of patients in whom the specific characteristic was reported , but we could not perform a risk factor analysis due to heterogeneity in data . Third , cases of S . suis meningitis might have been missed due to a negative CSF culture caused by pre-treatment with antibiotics . In conclusion , S . suis meningitis is predominantly seen in men after contact with pigs or pork and is endemic in pig rearing and pork consuming countries such as Vietnam , Thailand and China . The typical clinical presentation consists of hearing loss , fever , headache and neck stiffness , and skin injury in the presence of pig/pork contact is present in 20% of the cases . Although the mortality of S . suis meningitis is low compared to S . suis infection in general and other causes of bacterial meningitis , 53% of patients end up with hearing loss .
|
Meningitis is a common manifestation of Streptococcus suis infection . S . suis is endemic in pork consuming and pig rearing countries . We systematically reviewed the clinical characteristics , predisposing factors and outcome of S . suis meningitis . We identified 913 patients included in 24 studies , with a mean age of 49 years and a majority of male patients ( 82% ) . Exposure to pigs or pork was present in 61% , with a skin injury being present in 20% . Fever was present in 97% of patients , headache in 95% , neck stiffness in 93% . The mortality was 2 . 9% and hearing loss was common occurring in 53% of patients . Dexamethasone was associated with reduced hearing loss .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Streptococcus suis Meningitis: A Systematic Review and Meta-analysis
|
Neural circuits are wired by chemotactic migration of growth cones guided by extracellular guidance cue gradients . How growth cone chemotaxis builds the macroscopic structure of the neural circuit is a fundamental question in neuroscience . I addressed this issue in the case of the ordered axonal projections called topographic maps in the retinotectal system . In the retina and tectum , the erythropoietin-producing hepatocellular ( Eph ) receptors and their ligands , the ephrins , are expressed in gradients . According to Sperry’s chemoaffinity theory , gradients in both the source and target areas enable projecting axons to recognize their proper terminals , but how axons chemotactically decode their destinations is largely unknown . To identify the chemotactic mechanism of topographic mapping , I developed a mathematical model of intracellular signaling in the growth cone that focuses on the growth cone’s unique chemotactic property of being attracted or repelled by the same guidance cues in different biological situations . The model presented mechanism by which the retinal growth cone reaches the correct terminal zone in the tectum through alternating chemotactic response between attraction and repulsion around a preferred concentration . The model also provided a unified understanding of the contrasting relationships between receptor expression levels and preferred ligand concentrations in EphA/ephrinA- and EphB/ephrinB-encoded topographic mappings . Thus , this study redefines the chemoaffinity theory in chemotactic terms .
During development , neurons extend axon and dendrites [1–3] and axonal growth cones chemotactically migrate in response to extracellular guidance cue gradients and connect to their target sites . Because this axon guidance is a fundamental process in wiring neural circuits , many guidance cues and receptors have been identified and their functional roles ( e . g . , attraction or repulsion ) have been extensively investigated [4–6] . The growth cone’s chemotactic properties are thus being unveiled at the molecular level , but the chemotactic mechanisms of neural circuit construction remain mysterious at the macroscopic level . I addressed this issue by investigating topographic maps , the ordered axonal projections ubiquitous in the sensory nervous system . The best-studied example is in visual system , where retinal ganglion cells ( RGCs ) project their axons to the optic tectum and/or superior colliculus ( SC ) while keeping an initial positional relation [7] . The most important concept of topographic map formation is the “chemoaffinity theory” proposed by Roger Sperry in 1940s [8] . Sperry proposed that chemical labels form gradients in source and target areas , allowing a projecting axon to recognize its target site . The theory’s molecular basis was identified with the discovery of gradients of erythropoietin-producing hepatocellular ( Eph ) receptors and their ligands , ephrins , in the retina ( source area ) and tectum ( target area ) [9 , 10] . Ephs and ephrins are classified into two families , A and B , that encode orthogonal topographic maps in the retina and tectum ( Fig 1 ) . The EphA receptor gradient along the retina’s nasal-temporal axis topographically corresponds to the ephrinA gradient along the tectum’s rostral-caudal axis ( Fig 1A ) . On the orthogonal coordinates , the EphB receptor gradient along the retina’s dorsal-ventral axis corresponds to the ephrinB gradient along the tectum’s medial-lateral axis ( Fig 1C ) . These facts suggest that RGC growth cones chemotactically migrate to their terminal zones guided by ligand concentrations reflective of receptor expression levels . Because ephrinA and ephrinB act as both attractants and repellents in a concentration-dependent manner [11–13] , it is possible that growth cones switch between attraction and repulsion around the terminal zone , but the chemotactic mechanism for decoding destination from dual gradients ( i . e . , receptor and ligand ) is unknown . The EphA/ephrinA- and EphB/ephrinB-encoded topographic maps differ in that the RGCs with higher EphA receptor expression prefer lower tectal ephrinA concentrations ( Fig 1B ) , whereas the RGCs with higher EphB receptor expression prefer higher tectal ephrinB concentrations ( Fig 1D ) . In other words , the retinotectal system’s two kinds of topographic mapping have opposite receptor expression-dependent ligand concentration preferences . How the growth cone’s chemotactic system implements these opposite preferences is also unknown . Topographic mapping has been extensively investigated with computational models for four decades [14] , but all previous models featured growth cones reaching their terminal zones by heuristically-designed chemoaffinity [15–25] . While these models provided insights into the outcomes of surgical experiments in the retinotectal system [15–17] and the abnormal maps resulting from misexpression of Ephs or ephrins [15 , 18–25] , none addressed how the intracellular mechanism of growth cone chemotaxis achieves chemoaffinity . I sought to determine the underlying mechanism of topographic mapping implemented by growth cone chemotaxis . To this end , I focused on the growth cone’s unique chemotactic property of being attracted and repelled by the same guidance cues in different biological environments [26 , 27] . By mathematically modeling growth cone migration regulated by intracellular signaling , I attempted to demonstrate how the growth cone reaches its terminal zone in the tectum by switching attraction and repulsion around a preferred ligand concentration . Through this model , I redefined Sperry’s chemoaffinity theory in terms of chemotaxis .
The model growth cone was equipped with an intracellular activator ( A ) and inhibitor ( I ) of their effector ( E ) , where A and I were upregulated by guidance cues and E regulates the growth cone motility ( Fig 2A and 2B ) . This activator-inhibitor framework has been commonly observed in both neural and non-neural chemotactic cells [28–31] . For simplicity , a one-dimensional coordinate ( x ) across the growth cone was modeled as {x| − L/2 ≤ x ≤ L/2} , where L indicates its length . The reaction-diffusion dynamics of A and I were described by ∂A∂t=DA∂2A∂x2−kAA+cA+αAG ( x ) ∂I∂t=DI∂2I∂x2−kII+cI+αIG ( x ) ( 1 ) with reflecting boundaries at both ends ( x = ±L/2 ) , where A and I represent the activities of A and I , respectively , DZ , kZ , cZ , and αZ ( Z ∈ {A , I} ) denote the diffusion constant , decay rate , constant input , and efficacy , respectively , of the guidance cue’s signal transmission , and G ( x ) represents the guidance cue concentration at x . The activity of E was determined by the ratio of A’s activity to I’s , i . e . , E ( x ) = A ( x ) /I ( x ) , which is reasonable if E is regulated by a push-pull enzymatic reaction between A and I [32 , 33] . The growth cone’s migration was driven by the relative spatial polarity of E as ΔE/E* , where ΔE and E* indicate the spatial difference of E across the growth cone ( i . e . , E ( L/2 ) − E ( −L/2 ) ) and the baseline activity of E ( i . e . , E ( 0 ) ) , respectively . This property was stated as the Weber-Fechner law , in which the detectable spatial polarity of E varies because of the scale of the concentration of E [34] . Indeed , the Weber-Fechner law has been found in several types of chemotactic cells [35–40] . By analytically solving the model ( see Methods ) , I demonstrated that it produced opposite polarities for ΔE depending on the parameters ( Fig 2C and 2D and Table 1 ) ; when ΔE > 0 , the growth cone was attracted and migrated along the gradient , but when ΔE < 0 , the growth cone was repelled and turned against the gradient . I examined how chemotactic responses vary with absolute concentrations in the gradient . My previous study [27] showed that the steady-state response of ΔE/E* was presented by ΔEE*=ΔAA*−ΔII* , ( 2 ) where A* and I* denote the baseline activities of A and I , respectively ( i . e . , A* = A ( 0 ) and I* = I ( 0 ) ) , and ΔA and ΔI denote the spatial differences of A and I , respectively , across the growth cone ( i . e . , ΔA = A ( L/2 ) − A ( −L/2 ) and ΔI = I ( L/2 ) − I ( −L/2 ) ) ( see Fig 2D ) . Z* and ΔZ ( Z ∈ {A , I} ) were analytically derived ( see Methods ) . By substituting these into Eq ( 2 ) , I found that four chemotactic response patterns were generated depending on parameters ( Fig 3A and Table 1 ) : unidirectional repulsion , unidirectional attraction , bidirectional repulsion-to-attraction , and bidirectional attraction-to-repulsion ( BAR ) . In the former two patterns , the growth cone always exhibited attraction or repulsion , meaning that it preferred higher or lower concentrations , respectively ( Fig 3C and 3D ) . In bidirectional repulsion-to-attraction , the growth cone preferred either higher or lower concentrations depending on the initial concentration ( Fig 3B ) . Finally , in BAR , the growth cone avoided both higher and lower concentrations but preferred a specific concentration by switching attraction and repulsion at that concentration ( Fig 3E ) . I hypothesized that this BAR pattern could play a fundamental role in topographic map formation . Assuming that the growth cone exhibited the BAR pattern , I studied how receptor expression levels affected the preferred concentration . To this end , the receptor was incorporated into the model as follow: ∂A∂t=DA∂2A∂x2−kAA+cA+αAf ( R , G ( x ) ) ∂I∂t=DI∂2I∂x2−kII+cI+αIf ( R , G ( x ) ) , ( 3 ) where R represents the expressed receptor’s density , and f ( R , G ) represents the density of the receptor’s active form depending on the guidance cue concentration . By analyzing this model based on Eq ( 2 ) ( see Methods ) , I found that whether the preferred concentration , Gpref , decreases or increases with R was determined by the sign of derivatives of f ( R , G ) with respect to R and G: dGprefdR=−∂f/∂R∂f/∂G . ( 4 ) Therefore , f ( R , G ) , which represents how the guidance cue signal is transmitted to A and I through the receptor , is a crucial factor in the receptor expression level-dependent preferred ligand concentration . I next studied specific examples of f ( R , G ) . I considered a scenario in which the receptors were activated by guidance cue binding ( Fig 4A ) , which is described by f ( R , G ) = RG/ ( K + G ) , where K is the dissociation constant of binding reaction between the receptor and guidance cue ( i . e . , a ratio of unbinding rate to binding rate ) . I then calculated the preferred concentration based on Eq ( 2 ) and found that it decreased with the receptor expression level ( Fig 4B ) as Gpref∝1R−γ , ( 5 ) where γ is a positive constant determined by the model parameters . This is consistent with type 1 topographic mapping in which higher EphA levels result in the growth cone preferring smaller ephrinA concentrations ( Fig 1A and 1B ) . If the receptor expression level is greater than γ , this relationship produces a linearly ordered topographic map with exponential distributions of retinal EphA and tectal ephrinA ( Fig 4C ) . For the mechanism of type 2 EphB/ephrinB-encoded topographic mapping , I tested two biologically plausible hypothetical f ( R , G ) expressions . First , guidance cue-unbound receptors might trigger intracellular signaling , which can be expressed by f ( G ) = RK/ ( K + G ) ( Fig 4D ( i ) ) ( see Discussion for its biological relevance ) . For this hypothesis , I found that the preferred concentration increases with the receptor expression level ( Fig 4E ) as Gpref∝R−γ . ( 6 ) This is consistent with the fact that higher EphB levels result in the growth cone preferring higher ephrinB concentrations ( Fig 1C and 1D ) ( see Methods ) . This linear relationship ( Eq ( 6 ) ) produced a linearly ordered topographic map with exponential distributions of retinal EphB and tectal ephrinB ( Fig 4F ) . In the second hypothesis , I assumed that two kinds of receptor competitively bind the limited ligands ( Fig 4D ( ii ) ) ( see Discussion for its biological relevance ) . One kind is uniformly expressed across the retina and the guidance cue-bound form triggers intracellular signaling . The other is expressed in gradients across the retina and indirectly inhibits the uniformly expressed receptor by competitively binding the ligand . This case is described by f ( R , G ) = RcG/ ( K + Rc + R ) ( Methods ) , where R and Rc indicate densities of the receptors expressed in gradients and uniformly , respectively , and K indicates the dissociation constant of the receptor and ligand . For this hypothesis , I also found that the preferred concentration increases with the receptor expression level as Gpref∝R+ρ , ( 7 ) where ρ is a positive constant determined by the model parameters . Thus , this hypothesis also explained the type 2 EphB level-dependent preferred ephrinB concentration .
If ephrinA is a repellent , as classically thought [41] , then all RGC growth cones must project to the tectum’s rostral end , which has the lowest ephrinA concentration . However , this is not the case; even without tectal space competition between projecting axons , the RGC axons project to the correct terminal zone in the tectum [42] . This contradiction can be resolved simply by regarding ephrinA as both an attractant and a repellent . In fact , ephrinA has been reported to be an attractant or a repellent in a concentration-dependent manner [11] . EphrinB has been regarded as both an attractant and a repellent [12 , 13] . However , their underlying mechanism was largely unknown . In this study , I demonstrated how ephrinA and ephrinB could indeed work as both attractants and repellents for the chemotactic growth cone . I demonstrated that whether the preferred ligand concentration decreases or increases with the receptor expression level is determined by whether the guidance cue and the receptor positively or negatively affect intracellular signaling ( Eq ( 4 ) ) . As the mechanism of EphA/ephrinA-encoded type 1 topographic mapping , I reasonably assumed that ephrinA-bound EphAs trigger intracellular signaling ( Fig 4A ) , but for type 2 topographic mapping , I tested two hypothetical EphB/ephrinB regulation schemes . The first hypothesis was that ephrinB-unbound EphBs , rather than bound ones , trigger intracellular signaling ( Fig 4D ( i ) ) . This seems inconsistent with a property of tyrosine kinase-type receptors , which are activated by ligand binding through phosphorylation [43 , 44] , but it has recently been reported that Ephs can be ligand-independently activated by hemophilic Eph-Eph interactions [45] , suggesting that ephrinB-bound and -unbound EphBs could generate different signals . The first hypothesis was thus biologically feasible , but further experimental investigation is needed . The second hypothesis was that two kinds of receptor , which are expressed uniformly or in gradients across the retina , competitively bind the ligand ( Fig 4D ( ii ) ) . This fits the expression profiles of EphB subtypes in the chicken retina well; EphB2 and EphB3 are expressed in gradients across the retina , whereas EphB1 is uniformed expressed [7] . My hypothesis thus offers experimentally testable predictions concerning EphB/ephrinB regulation . It is worth mentioning functional difference between type 1 and type 2 of topographic mappings . I deduced that local accuracy of axonal projection is determined by multiplication of three factors: 1 . spatial derivatives of receptor expression profile in retina ( upper panels in Fig 4C and 4F ) , 2 . steepness of mapping function from receptor expression to preferred ligand concentration ( Fig 4B and 4E ) and 3 . spatial derivatives of ligand in tectum ( right panels in Fig 4C and 4F ) . In type 1 topographic mapping , while multiplication of the first and third factors , i . e . , ( dRNT/dxNT ) ( dGRC/dxRC ) , is constant ( Fig 4C ) , the second factor , i . e . , the steepness of mapping function , increases as EphA expression decreases ( Fig 4B ) . On the other hand , in type 2 topographic mapping , while the second factor , i . e . , the steepness of mapping function , is constant ( Fig 4E ) , multiplication of the first and third factors , i . e . , ( dRDV/dxDV ) ( dGML/dxML ) , increases with EphB expression . Thus , it can be predicted that axonal projection from nasal ventral retinal region associated with lower EphA and higher EphB expression could be more precise than that other retinal region . RGCs’ axonal projection patterns in the optic tectum or SC are species-dependent . In higher vertebrates ( i . e . , mammals and birds ) , the axons overshoot their terminal zones and subsequently form branches [7] , while in lower vertebrates ( i . e . , fish and amphibians ) , the growth cones directly reach and stop in their terminal zones [7] despite being initially misrouted [46] . The latter case suggests that the chemotactic system implements chemoaffinity , which I investigated as the mechanism of topographic mapping . The growth cone’s chemotaxis might therefore play a fundamental role in topographic mapping , while axonal overshoot and branching might facilitate exploration of the terminal zone . My model could understand the axonal overshoot by incorporating transient dynamics of activator and inhibitor , instead of steady state assumption . On the other hand , how the axon generates branches is out of scope of my model . Chemotactic gradient sensing has been computationally studied mainly for non-neural chemotactic cells [40 , 47–51] like Dictyostelium discoideum and immune cells , though attraction to guidance cues has been only paid attention . On the other hand , there are a couple of computational models for the growth cone chemotaxis alternating attraction and repulsion [27 , 52] . These models , whether applied to neural or non-neural cells , primarily addressed intracellular signaling consisting of activators and inhibitors . In the non-neural cells , the activator and inhibitor were thought to be PI3K and PTEN [28] , respectively , or RasGEF and RasGAP [29] , respectively . In the growth cone , CaMKII and PP1 were thought to work as the activator and inhibitor , respectively [27 , 30 , 31 , 52] , which regulate cellular motility via Rho GTPases [53] . In short , chemotactic responses could be understood from the activator-inhibitor framework [54] , so I hypothesized that RGC chemotaxis is also regulated by an activator-inhibitor system , although the intracellular signaling pathway of Eph/ephrin has not been fully identified . There have been many computational studies on topographic mapping [14] . These studies did not focus on the intracellular mechanism of growth cone chemotaxis , but instead developed models with heuristically designed chemoaffinity ( e . g . , optimization of energy function ) by which the growth cone reaches its correct terminal zone . Given such chemoaffinity , these models potentially gave insights into more system-level phenomena , such as abnormal maps resulting from surgical experiments in the retinotectal system [15–17] and from the misexpression of Eph or ephrin [15 , 18–25] . These models included several factors not included in my model , such as axon competition for tectal space [55] and counter-gradients of Ephs and ephrins in the retina and tectum [56] . Several models have also addressed a question of how synaptic connection is refined by activity-dependent synaptic plasticity mechanism after activity-independent axon guidance [20 , 57–59] . Therefore , I must stress that my model does not compete with previous models , but rather can explain the underlying mechanism by which growth cones can chemotactically implement the previous models’ heuristically designed chemoaffinity .
Suppose a shallow extracellular gradient because growth cones are known to detect few percent difference of concentrations across the growth cone [60–64] . I then assumed that the intracellular gradients of A and I , A ( x ) and I ( x ) , were shallow and slightly perturbed from their activities at x = 0 . The activity of E at x could be linearized as E ( x ) ≃E*+1I*[A ( x ) −A*]−A*I*2[I ( x ) −I*] , ( 8 ) where A* = A ( 0 ) , I* = I ( 0 ) , and E* = E ( 0 ) = A*/I* . The relative spatial difference of E across the growth cone was calculated by ΔEE*≡E ( L/2 ) −E ( −L/2 ) E ( 0 ) =ΔAA*−ΔII* , ( 9 ) where ΔA and ΔI indicate the spatial differences of A and I , respectively , across the growth cone . For both A and I , I calculated the intracellular distribution exposure to an extracellular gradient , G ( x ) . Green’s function of ∂Z/∂t = Dz ( ∂2Z/∂x2 ) − kzZ was analytically derived using the method of separation of variables: H ( x , ξ , t ) =1Lexp ( −kZt ) +2L∑n=1∞cos[nπL ( ξ+L2 ) ]cos[nπL ( x+L2 ) ]exp[−{kZ+ ( nπL ) 2DZ}t] . ( 10 ) A steady-state solution of Eq ( 3 ) was thus obtained by Z∞ ( x ) =∫0∞dτ∫−L/2+L/2dξH ( x , ξ , τ ) {cZ+αZf ( R , G ( ξ ) ) } , ( 11 ) where Z represents either A or I . Note that f ( R , G ( x ) ) = G ( x ) in Eq ( 1 ) . Because the growth cone is so small that G ( x ) could be modelled as a shallow linear gradient , f ( R , G ( x ) ) can be linearized by f ( R , G* ) + gx , where G* = G ( 0 ) and g = ( ∂f/∂G|G = G* ) ( dG/dx|x = 0 ) . This led to Z∞ ( x ) =Z*+2gL∑n=1∞ ( L/nπ ) 2[ ( −1 ) n−1]kZ+ ( nπ/L ) 2DZcos{nπL ( x+L2 ) } , ( 12 ) where Z* indicates baseline activity , i . e . , Z* = Z∞ ( 0 ) : Z*=αZf ( R , G* ) +cZkZ . ( 13 ) By numerical simulation of the reaction-diffusion dynamics , I confirmed that Eq ( 12 ) was exact . The spatial difference of Z then becomes ΔZ=Z∞ ( L/2 ) −Z∞ ( −L/2 ) =8gL3π4h ( DZ/kZ ) kZ , ( 14 ) where h ( s ) =∑n=1∞1/ ( 2n+1 ) 2 ( 2n+1 ) 2s+ ( L/π ) 2 , ( 15 ) which is a monotonically decreasing function converging to 0 ( inset of Fig 3A ) . I calculated the growth cone’s concentration-dependent chemotactic responses . By substituting Z* as described by Eq ( 13 ) for A* and I* in Eq ( 2 ) and substituting ΔZ as described by Eq ( 14 ) for ΔA and ΔI in Eq ( 2 ) , I obtained ΔEE*=8gL3π4[h ( DA/kA ) αAf ( R , G* ) +cA−h ( DI/kI ) αIf ( R , G* ) +cI] . ( 16 ) Eq ( 16 ) exhibits four response patterns to G*: all positive , all negative , negative-to-positive , and positive-to-negative , which correspond to unidirectional attraction , unidirectional repulsion , bidirectional repulsion-to-attraction , and BAR , respectively ( Fig 3B–3E ) . The response patterns’ parameter regions were derived under the condition of ∂f/∂G > 0 ( Fig 3A ) . For example , the BAR response pattern is characterized by attraction at lower concentrations ( i . e . , ΔE/E*|G* = 0 > 0 ) and repulsion at G* = ∞ ( i . e . , ΔE/E*|G* = 0 < 0 ) , which leads to cAcI<η<αAαI , ( 17 ) where η = h ( DA/kA ) /h ( DI/kI ) . Growth cones with the BAR response pattern prefer a specific concentration of G* at which ΔE/E* = 0 . In the Eq ( 3 ) model , setting ΔE/E* = 0 in Eq ( 16 ) leads to f ( R , Gpref ) =γ , ( 18 ) where γ = ( ηcI − cA ) / ( αA − ηαI ) . The preferred concentration with a specific f ( R , G ) can be calculated with Eq ( 18 ) . In the Eq ( 1 ) model , f ( R , G ) = G , thus Gpref = γ . If f ( R , G ) = RG/ ( K + G ) , Gpref = γK/ ( R − γ ) ( Eq ( 5 ) ; Fig 4A ) . If f ( R , G ) = RK/ ( K + G ) , Gpref = ( K/γ ) ( R − γ ) ( Eq ( 6 ) ; Fig 4D ( i ) ) . If f ( R , G ) = RcG/ ( K + Rc + R ) , Gpref = ( γ/Rc ) / ( R + K + Rc ) ( Eq ( 7 ) ; Fig 4D ( ii ) ) . Total differentiation of Eq ( 18 ) leads to ( ∂f/∂R ) dR + ( ∂f/∂Gpref ) dGpref = 0 , which in turn leads to dGprefdR=−∂f/∂R∂f/∂G . ( 19 ) I assumed a scenario in which two kinds of RGC-expressed receptors competitively bind limited ligands with identical kinetics . Note that the two assumed kinds are expressed either uniformly or in gradients across the retina . Such dynamics are described by dRc*dt=kf ( Rc−Rc* ) Gf−kbRc*dRg*dt=kf ( Rg−Rg* ) Gf−kbRg* , ( 20 ) where Rj , Rj* , and Gf ( j ∈ {c , g} ) indicate densities of the total receptors , guidance cue-bound receptors , and free guidance cues , respectively , and kf and kb indicate forward and backward reaction rates , respectively . The total guidance cue concentration is conserved as G=Gf+Rc*+Rg* . At steady state , Rj*=RjGf/ ( K+Gf ) , where K = kb/kf . If K ≫ Gf , Rj* can be approximated as ( Rj/K ) Gf , and the steady state of Rc* depending on G is then described by Rc*=RcGK+Rc+Rg . ( 21 )
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This study revisited the chemoaffinity theory for topographic mapping in terms of chemotaxis . According to this theory , the axonal growth cone projects to specific targets based on positional information encoded by chemical gradients in both source and target areas . However , the mechanism by which the chemotactic growth cone recognizes its proper terminal site remains elusive . To unravel this mystery , I mathematically modeled a growth cone exhibiting concentration-dependent attraction and repulsion to chemotactic cues . The model identified a novel growth cone guidance mechanism in topographic mapping , highlighting the importance of the growth cone’s unique ability to alternate between attraction and repulsion . Furthermore , an extension of the model provided possible molecular mechanisms for contrasting two types of topographic mappings observed in the retinotectal system .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
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2017
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Revisiting chemoaffinity theory: Chemotactic implementation of topographic axonal projection
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Among primates , genome-wide analysis of recent positive selection is currently limited to the human species because it requires extensive sampling of genotypic data from many individuals . The extent to which genes positively selected in human also present adaptive changes in other primates therefore remains unknown . This question is important because a gene that has been positively selected independently in the human and in other primate lineages may be less likely to be involved in human specific phenotypic changes such as dietary habits or cognitive abilities . To answer this question , we analysed heterozygous Single Nucleotide Polymorphisms ( SNPs ) in the genomes of single human , chimpanzee , orangutan , and macaque individuals using a new method aiming to identify selective sweeps genome-wide . We found an unexpectedly high number of orthologous genes exhibiting signatures of a selective sweep simultaneously in several primate species , suggesting the presence of hotspots of positive selection . A similar significant excess is evident when comparing genes positively selected during recent human evolution with genes subjected to positive selection in their coding sequence in other primate lineages and identified using a different test . These findings are further supported by comparing several published human genome scans for positive selection with our findings in non-human primate genomes . We thus provide extensive evidence that the co-occurrence of positive selection in humans and in other primates at the same genetic loci can be measured with only four species , an indication that it may be a widespread phenomenon . The identification of positive selection in humans alongside other primates is a powerful tool to outline those genes that were selected uniquely during recent human evolution .
The respective contribution of neutral and advantageous mutations to genetic differences between species has been a pivotal question in molecular evolution for more than half a century [1] . Until recently , results were based on typically small genetic samples leading to controversial conclusions . Only during the past decade did large genetic variation datasets make it possible to estimate reliable distributions of fitness effects [2] for a series of species such as drosophila [3] and human [3] , [4] . Although estimating this distribution is not trivial under complex demographic histories and although differences remain between studies on details , different approaches now converge to conclude that a substantial proportion of non-deleterious mutations are indeed weakly to strongly advantageous [4]–[9] . In drosophila , it was found that between approximately 25% and 50% of amino-acid substitutions [6] , [7] and 20% of intergenic substitutions [8] may be adaptive . In human where effective population size is smaller , estimated proportions vary from 10% to 20% [4] , [10] . Such substantial proportions agree well with several scans for selective sweeps in the human genome concluding that selective sweeps are common and affect human genetic diversity [9] , [11]–[19] . This may however seem contradictory with results from methods based on non-synonymous versus synonymous divergence analyses in coding sequences , such as PAML site and branch-site likelihood ratio tests for positive selection . Indeed , the PAML branch-site test 2 infers positive selection in the human lineage following divergence with chimpanzee for far fewer genes than scans for selective sweeps [20]–[24] , despite the fact that such scans examine a comparatively much narrower evolutionary period . However , site and branch-site tests for positive selection are generally conservative and coding sequences represent only a small part of mammalian genomes , thus explaining much of the differences between the two approaches . Despite their conservativeness , site tests for positive selection were recently able to show that hundreds of coding sequences experienced multiple rounds of positive selection during mammalian evolution [22] . Scans for selective sweeps nevertheless capture many more adaptive events and , together with an increasing number of striking cases of parallel/convergent adaptive evolution [25]–[32] , suggest that the current view of the quantitative importance of positive selection acting at the same locus independently in distinct species is still underestimated . This question is of particular interest in the context of recent human evolution . Here and in the rest of this manuscript “recent” means detectable at the genetic intraspecific variation level , as opposed to positive selection detectable at the divergence level . It is currently unknown ( i ) which proportion of genes were positively selected recently in human but also experienced positive selection in other primate lineages , either recently or within a more extensive evolutionary time and ( ii ) which genes were in contrast positively selected only in modern human . The distinction is important to unravel the plausible nature and “uniqueness” of adaptive changes that underlie selective sweeps . For example if a selective sweep is found for a gene in human , it is often tempting to first examine if this gene governs a specifically human phenotype , and if so to interpret the sweep in terms of a strictly human-specific adaptation . But knowing that orthologs of this gene are also associated with positive selection in other primates , although not excluding the possibility of a human-specific adaptation ( same gene , human-specific nature of phenotypic change ) , might more accurately redirect interpretations on the nature of adaptation towards scenarios that are not restricted to human-specific phenotypes ( same gene , similar nature of phenotypic change in human and other primates ) . Here , we estimated the quantitative importance of positive selection acting on the same genes independently in human evolution and three other primate lineages , either recently or across more extensive evolutionary times ( chimpanzee , orangutan and macaque ) . Because genome wide genotyping datasets such as those provided by the HapMap [33] and Perlegen [34] projects for human populations are not available for non-human primates , we have developed an empirical method that detects candidate selective sweeps using complete genomes of single individuals from natural populations ( see Methods ) . We exploited the fact that alleles linked to a positively selected mutation also increase in frequency through genetic hitchhiking [35] , which results in a loss of variants unlinked to the selected haplotypes and thus in a reduction of the surrounding genetic diversity that defines a selective sweep . Our results show that positive selection affecting the same genes independently in human and other primates is ( i ) a common phenomenon and ( ii ) is not restricted to specific functions such as defence against pathogens or reproduction .
Our method is inspired by the HKA test [19] , [36] and contrasts the heterozygosity measured in a local genomic window with the value measured within its surrounding genomic context , while using inter species divergence to control for variable neutral mutation rate ( including selective constraint ) . The corrected level of heterozygosity is indicated by the value of a statistic K computed for each window ( 0≤K≤1 ) . The method thus exploits the localized nature of the hitchhiking effect of an adaptive mutation , and controls for natural and experimental factors known to influence the observed genetic diversity in primate genome sequences obtained by shotgun sequencing ( see Methods; Text S1 and Table S1 ) . We first validated the method using extensive forward population simulations [37]–[40] ( Text S2 , Figures S1 , S2 , S3 ) , and applied the method independently to two individual human genome sequences [41] , [42] ( respectively J . Craig Venter ( CV ) and James Watson ( JW ) ) . First , we find extensive overlap ( 24%; co-occurrence test P<10−5; see Methods ) between the 2 , 244 and 2 , 193 genes identified in the respective genomes of CV and JW with K≤0 . 05 . This is expected if the method correctly identifies genes with reduced heterozygosity due either to selective sweeps or shared demographic history . Second , genes detected with K≤0 . 05 when averaging both individuals include well known examples of recent positive selection in Europeans , such as the FOXP2 [43] , OCA2-HERC2 [12] , [44] and SLC24A5 [14] , [45] loci ( Figure 1 , Table S2 ) . Interestingly , we identified a sweep across the lactase locus LCT [12] , [32] , [46] in CV but not in JW , in line with the fact that the latter is heterozygous for the European lactase advantageous mutation ( Figure 1 ) , while the former is homozygous . Third , we found that candidate genes detected empirically by our method ( K averaged over the two individuals scanned independently ) significantly overlap with those identified by alternative approaches [11]–[15] , which include methods aimed at detecting partial or complete sweeps ( Table 1; see Methods ) . Visual inspection of our data in comparison with previous scans ( e . g . Williamson et al . , Carlson et al . and Pickrell et al . ; see Methods ) in the UCSC Genome Browser provides additional examples of convergent detection of positively selected loci by different methods ( Table S3 ) . Fourth , genes located in candidate sweeps tend to be more strongly expressed in cerebellum , spleen and testes comparatively to their expression in other tissues [47] ( Figure S4 ) , and generally K is significantly lower for several Gene Ontology biological processes [48] , [49] already highlighted in previous scans for selective sweeps such as defence response or transcription [12] , [16] , [50] ( Table S4 ) . Therefore , despite the lower specificity and sensitivity expected from using data from individual genomes , the set of candidate sweeps detected by this method are highly enriched in positively selected loci ( Text S2 ) . Our method is applicable to individual genomes and was used to scan the genomes of chimpanzee , orangutan and macaque that were all sequenced from single outbred individuals [51] , [52] . We first used ssahaSNP2 [53] to identify 875 , 182 , 1 , 364 , 646 and 2 , 294 , 239 heterozygous SNPs in chimpanzee , orangutan and macaque respectively ( see Methods ) . In order to estimate how frequently recent positive selection has independently targeted the same gene in human and these other primate lineages , we computed the number of orthologous genes candidates for selection in human and at least one of the three other tested primates , and compared the results with random expectations . We first selected the 9 , 972 four-way protein coding orthologous genes tested in all primates , and identified candidate genes with K lower than threshold values ranging from 0 to 0 . 1 in human , chimpanzee , orangutan and macaque ( see Methods; Figure 2 and Table S5 ) . For each K threshold the number of candidates in non-human primates is higher than in human , because the use of two human individuals substantially increases specificity in this species and because non-human primate candidate genes are the sum of several scans with different window sizes . We devised a co-occurrence test that compares the observed numbers of genes found in candidate selective sweeps simultaneously in human and in one , two or three non-human primate species with the numbers expected if all genes are equally likely to experience positive selection ( see Methods and Figure S5 ) . The difference between observed and expected values thus reflects the excess or deficit of positive selection co-occurring at orthologous genes in multiple primates relative to the rate of positive selection in each primate . The ability of the test to detect an excess of co-occurrence , i . e . hotspots of positive selection , depends on three factors . The first is the “usage” frequency of a given hotspot by positive selection during evolution , which will affect the magnitude of the excess of co-occurrence . Indeed , hotspots might be used rarely enough that four species may not be sufficient to observe a significant excess . The second factor is the rate of false positive candidates in each tested species , which tend to occur randomly across genomes . False positives thus lead to underestimating the relative difference between real and random co-occurrences . However , we show that detecting hotspots of positive selection is possible even with a high rate of false positive candidates within each species ( Texts S2 , S3; Figure S6 ) . The third factor is the power of the test to correctly identify , within each species , candidates for positive selection . Obviously , the lower the power the higher the risk of missing hotspots . For instance a hotspot active in three species where the power to detect positive selection events is only 30% will be identified with a power of only 2 . 7% ( 0 . 33 ) . Because the second and third factors act in opposite directions , the optimal K threshold to identify the footprint of hotspots through a statistically significant excess of co-occurring candidates of positive selection is therefore not the most stringent ( few false positives but low power ) , but the one with the best compromise between power and the rate of false positives . Using different K thresholds and controlling for several potentially confounding factors ( see below ) , we find that although genes in candidate sweeps are mostly specific of a given primate , genes found in candidate sweeps in human and two or more other primates are systematically in significant excess ( Figure 2 and Table S6 ) . Overall , the relative co-occurrence excess increases with lower , more stringent K thresholds , as expected if false positives partly dissipate the signal of positive selection hotspots . Although we cannot precisely estimate their rate for different K values due to the approximate nature of population simulations , we expect the false positive rate to be high when using one or two individuals to detect sweeps , and most likely always above 50% of genes identified in selective sweeps ( Text S2 , Figures S1 , S2 , S3 ) . Notably , the excess observed at the most stringent K = 0 is slightly lower than at the second most stringent K≤0 . 005 , which likely reflects a loss of power to detect hotspots . Such a loss of power is also observed for the most stringent realisations of our test presented throughout this analysis ( see below ) . Finally , we also analysed a set of 70 genes identified in common in at least three out of four previous human genome scans for selective sweeps [11]–[14] and thus likely to have a very low rate of false positives . Strikingly , 22 of the 70 genes ( 31% ) , nearly three times more than expected ( 11% expected , co-occurrence test P = 2 . 10−3; Table S7 ) , are also in candidate sweeps in at least two non-human primates . These results are obtained by examining a short period of recent primate evolution and by comparing only four species with few individuals . We are therefore likely to underestimate the true frequency of positive selection hotspots active in human and other primates , which could plausibly be common and thus significantly impact the biological interpretations of human selective sweeps . The level of co-occurrence could also be in principle the consequence of the presence of coldspots of positive selection instead of hotspots , where a fraction of genes are constantly under low rates of positive selection in all the lineages studied , leading to selective sweeps concentrating on the remaining genes . However , using a simple analytical model , we show that the level of co-occurrence observed here is most likely explained by hotspots of positive selection ( Text S3 and Figure S6 ) . Although false positives lead to underestimating the relative excess of co-occurring candidate genes , several genomic factors known to correlate with diversity could in contrast lead to overestimate the observed excess of co-occurrences . Such factors include sequencing depth , local divergence used in the estimation of K , base composition , gene density and recombination . As expected if our method correctly controls for sequencing heterogeneity , neutral mutation rate and composition ( Text S1 , Table S1 ) , these factors explain a very small fraction of the variance of K ( n = 18 , 605; human-chimpanzee divergence: Spearman's ρ = −0 . 015 , P = 0 . 04; human-orangutan divergence: ρ = 0 . 026 , P = 3 . 10−4; human-macaque divergence: ρ = 0 . 006 , P = 0 . 43; human base composition: ρ = −0 . 014 , P = 0 . 055; sequencing depth: ρ = −0 . 009 , P = 0 . 19 ) . In contrast , correlation of K is higher with gene density and recombination ( n = 18 , 605; recombination rate: ρ = 0 . 18 , P<10−15; gene density: ρ = −0 . 07 , P<10−15 ) . In order to quantify separately the effect of these factors on the relative excess of observed co-occurring candidate genes , we divided genes into ten classes of equal sizes according to the 10-quantiles of one specific factor , and ran separate randomizations in each class during the co-occurrence test ( see Methods ) . Only gene density and recombination have a notable yet moderate impact on the expected level of co-occurrence , in agreement with correlations observed with K ( Figure 3 ) . The effect of recombination on co-occurrence supports findings that recombination rates tend to be conserved at a scale of 100 to 1 , 000 kb between closely related primates [54] , [55] . We may however be exaggerating the effect of recombination in these controls , because the measures of recombination rates used here may be locally underestimated in the presence of selective sweeps [56] . As a consequence , low recombination classes tend to concentrate more candidate sweeps , thus leading to an overall inflated expected number of co-occurrences in the control . We nevertheless tested the impact of recombination and gene density simultaneously by further defining 100 different gene density/recombination combinations classes ( see Methods ) . Although reduced , the relative excess of co-occurring candidates remains significant ( Figure 2 and Figure 3 ) . We also tested the effect of using human-orangutan and chimpanzee-orangutan divergence instead of human-chimpanzee and chimpanzee-human divergence to compute K in human and chimpanzee , respectively . Measures of K with the two approaches show a 95% correlation coefficient , and more than 80% of genes are systematically below the same K threshold . None of the co-occurrence tests we conducted are affected , including the most compelling cases discussed below . If a fraction of genes with a low K in multiple primates represent positive selection hotspots , then those genes may have been positively selected not only during recent primate evolution , but also for longer evolutionary times . We therefore used the PAML branch-site likelihood ratio test 2 [20] , [21] to analyse positive selection in orthologous coding sequences along five distinct branches of a phylogenetic tree including the four primates studied here , and using mouse as an outgroup [57] ( see Methods ) . Using the co-occurrence test ( Figure S5 ) , we find a significant excess of co-occurrence between positive selection in coding sequences and thresholds of K ranging from 0 to 0 . 1 in human alone or human and at least one additional primate ( Figure 4 ) , thus extending our analysis to a much wider evolutionary time scale . Notably , the excess of co-occurrence increases substantially when using both more stringent K and more stringent inference of positive selection in coding sequences ( with the exception of the most stringent conditions yielding slightly lower excess , again reflecting a plausible loss of power ) . Potentially biasing factors including recombination and gene density have no effect on this result . This therefore confirms positive selection hotspots independently from comparisons based only on recent selection , and shows that genes positively selected recently in human evolved similarly during more ancient primate evolution ( Table S8 ) . In order to further validate the evidence for positive selection hotspots , we first compared the results obtained with our statistic K in chimpanzee , orangutan and macaque with recently published scans for selective sweeps in seven worldwide human populations using the XP-EHH test [15] . XP-EHH based scans of a representative set of human populations have several advantages over our own scan based only on two European individuals . First , XP-EHH shows a good power when identifying close to complete or recently completed selective sweeps even at low fixed false discovery rates [15] , [17] . In line with this , XP-EHH has an excellent overlap with other scans in our comparison ( Table 1 ) . This should make the comparison of human with other primates more powerful despite a smaller absolute number of high confidence human candidates . Second , using seven human populations instead of one makes the comparison more representative . Using different K thresholds in non-human primates and increasingly high XP-EHH thresholds at genomic centres of human genes to isolate candidates , we confirm the previously observed excess of co-occurring selective sweeps candidates after controlling for recombination and gene density ( Figure 5 ) . In line with our previous observation that a comparison between recent ( our test ) and ancient ( PAML's branch-site test 2 ) candidates for positive selection show an excess of co-occurring cases , we also find significant co-occurrence between worldwide human population XP-EHH candidates and PAML branch-site test 2 positive selection candidates in non-human branches of the primate tree ( Figure 6 ) . This last comparison further confirms the existence of “primate” positive selection hotspots recently active in human evolution . In particular , it does so independently of our own statistic K , and neither gene density nor recombination had an effect in this configuration of the co-occurrence test . The existence of positive selection hotspots is also supported by functional gene annotations . Several typical candidates of positive selection are over-represented among the functions of the 72 genes with K≤0 . 05 in human and at least two other primates , including defence response , gametogenesis and forebrain development ( Table 2 ) . Importantly however , these functions are represented alongside a wide variety of other biological processes and molecular functions , which cannot be due exclusively to false positives . Indeed overrepresented functions encompass only 2 . 1% of all GO terms included in hotspots , making it highly unlikely that these would concentrate all true positives . Recent positive selection hotspots are thus not limited to a few specific functions , but instead cover a diverse functional repertoire [58] . A finer inspection of candidate hotspots within over-represented functions reveals particularly interesting cases . Among defence response candidates , we infer independent selective sweeps in human , chimpanzee and orangutan for the cluster of Toll-like receptors ( TLR ) 1 , 6 and 10 ( Figure 7 and Figure S7 ) . These receptors are involved in the non-specific recognition of a wide variety of bacteria during the first steps of the innate immune response . Interestingly , TLR 1 , 6 and 10 are three of the nine strongest candidates with K = 0 in human , chimpanzee and orangutan in our analysis ( Table S6 ) , and were recently found as a strong case of local adaptation in the northern European population [15] , [59] , to which the two individuals used in the present study also belong . In addition , the role of TLR 1 , 6 and 10 in the response to a broad spectrum of bacteria in multiple species is consistent with these receptors being hotspots of positive selection . Within gametogenesis SPIN1 ( Figure 7 and Figure S7 ) codes for spindlin , a protein involved in oocyte maturation [60] . SPIN1 is one of the top ten candidates of the European population using the XP-EHH test [15] , and is the strongest of our 13 strongest hotspot candidates ( Table S6 ) with K = 0 in human , chimpanzee , orangutan and macaque , and is therefore a very strong positive selection hotspot candidate . The fact that several of the best candidates from the present analysis can be found within over-represented GO functions and , most importantly , are also found as top candidates in human using other methods argues strongly in favour of positive selection hotspots . Gametogenesis candidates also include the ACVR2A and SPA17 ( Figure 7 ) genes which both play a role during spermatogenesis [61] , [62] . These examples show how multi-species comparisons may order priorities for deeper functional and evolutionary analyses among genes with positive selection during recent human evolution . More surprisingly , we found the FOXP2 gene in candidate selective sweeps in human ( K = 0 . 049 ) , chimpanzee ( K = 0 ) and orangutan ( K = 0 . 049 ) ( Figure 7 and Figure S7 ) . FOXP2 is an archetype of positively selected genes [43] , [63] interpreted in the context of human-specific phenotypes , in this case linguistic processing . Yet our data suggests that positive selection on FOXP2 recently occurred in other primates . Thus , recent positive selection on FOXP2 may need to be also considered in the context of other FOXP2 functions that could be shared among primates . This example further illustrates how interpretation of selective sweeps in human evolution can be guided by a broader and comparative view of positive selection among primates . Could co-occurring candidate genes be due to conserved background selection hotspots instead of recent positive selection hotspots [64] , [65] ? Although we cannot completely exclude an effect of background selection , our results are better explained by positive selection instead of recurrent deleterious mutations hotspots . First , functional analyses suggest that regions with low K in the human genome are dominated by positive selection . Indeed , several of the Gene Ontology biological processes found here with downwardly biased values of K were previously found over-represented for positively , not negatively selected genes [16] . Second , we find an excess of co-occurrence when comparing genes with K≤0 . 05 in non-human primates with positive selection candidates found in at least two of three human genome scans for selective sweeps that are insensitive to background selection [11] , [12] , [14] ( relative co-occurrence score C3+C4 excess = 91% , co-occurrence test P = 7 . 10−3; Figure S5 ) . Third , the co-occurrence between recent or coding sequence positive selection can be explained by positive , not background selection hotspots . Although background selection might have an influence , the presence of positive selection hotspots in primates active during recent human evolution remain the only reasonable explanation for our results .
We have performed a comparative analysis of positive selection in human and non-human primate genomes . Because non-human primate genomes do not benefit from genotyping data , we developed a new test to identify selective sweeps in single individual genomes . As shown by population simulations , this test is only moderately sensitive to demographic changes , and is thus widely applicable to genomes from single outbred individuals . The systematic comparison of genes positively selected during recent human evolution with candidates for positive selection in chimpanzee , orangutang and macaque shows a clear excess of genes that were positively selected independently in multiple primate lineages . This is independently confirmed by comparing recently published human positively selected genes [11]–[15] either to candidates identified by our test in non-human primates or to genes positively selected at the level of their coding sequence during more extensive evolutionary times . All these independent lines of evidence converge towards the same conclusion: primate genomes share hotspots of positive selection , including during recent human evolution . Positive selection hotspots in primates raise several questions . First , do they mainly represent cases of parallel/convergent evolution , or cases of adaptations in different phenotypic directions involving the same locus ? In the first scenario , hotspots could be seen as recurrent targets of positive selection for species under similar selective pressures . In the second scenario , hotspots would rather be considered as a toolbox to fine tune shared molecular functions according to different selective pressures . Second , we do not know how many genes can be described as positive selection hotspots in a primate genome . Each primate genome scan produced a high number of false positives , thus preventing unbiased attempts at estimating the minimal number of positive selection hotspots needed to explain our results . Third , the phylogenetic depth at which hotspots can be detected remains to be investigated . Our analysis in primates does not preclude that a subset of primate hotspots may be found under positive selection also in other mammals , or in other vertebrates . Indeed , isolated cases of parallel evolution have been observed between birds and rodents and human and fish [29] , [31] , raising the possibility that some hotspots may be shared among distant vertebrates . A deeper knowledge of positive selection hotspots has additional practical and conceptual implications . Most scans for positively selected genes deliver false positive results [58] , [66] , thus making it difficult to interpret the evolution of any given gene in a biological context . Yet genes inferred to be positively selected independently in multiple scans or alternatively in multiple species within the same evolutionary period reduce this uncertainty because they are more likely to be true positives . More importantly , hotspots provide a means to identify positive selection events more specific to one species , and in particular positive selection events specific to human . For instance , 19 of the 70 human genes identified in common between at least three previous scans for selective sweeps [11]–[14] are not candidates in any of the three other primates , and are therefore more likely to be linked to specific human changes . For instance several of the 19 candidates belong to the same sweep as the lactase gene , an adaptive event associated with the emergence of cattle breeding and thus expected to be human-specific ( Table S9 ) . However , because our test and most of the published scans used for comparisons ( except the scan of Voight et al . ) aim at detecting complete sweeps , we cannot exclude that the most recent events of positive selection , those with still ongoing selective sweeps , reflect more specific human adaptation events . We show here through independent comparisons based on a diverse array of methods that positive selection hotspots have been frequent during primate evolution , and in particular that genes positively selected during recent human evolution were also positively selected in other primate lineages , either recently or not . Yet importantly , our sampling of species and individuals is necessarily limited to sequenced genomes , and it is likely that the identification of hotspots of positive selection will increase in power as the genomes and variation information of more species become available . We predict that this will greatly facilitate the interpretation of biological changes underlying human selective sweeps . In particular , the identification of genes positively selected in human but never or infrequently in other primates will help to better outline truly specific aspects of recent human evolution . Our results provide a glimpse of the benefits of a hypothetical “1 , 000 Primate Genomes” project for the understanding of human adaptive evolution .
We developed a new method inspired by the HKA test [36] to estimate the probability that a given locus has recently been subject to positive selection , using genome wide heterozygous sites and divergence data from single individuals . K values are computed for a given region L of size q ( here 100 kb≤q≤300 kb ) by first computing the ratio rl between the number of heterozygous sites and the number of divergent sites observed with a closely related species ( e . g . human-chimpanzee when scanning the human and chimpanzee genomes , human-orangutan when scanning the orangutan genome and human-macaque when scanning the macaque genome ) . The same ratio rg is then computed for a region G extending 10 times q on both sides of L . A weighing scheme is applied to ratio rg to control for repeats , shotgun coverage and nucleotide composition ( see next paragraph ) . The ratio Robs = rl/rg then expresses the local reduction ( Robs<1 ) or increase ( Robs>1 ) in heterozygosity given its level in the surrounding genomic region and the local divergence . Next , the ratio R is computed for 5 , 000 additional windows of size q randomly sampled within G but at a distance at least five times q from L ( this is done with replacement , meaning that the same position in the background window can be represented in several random q sized windows ) . This generates an empirical distribution of R across the region G , thus providing an empirical means to estimate the probability of observing R lower than Robs in this region: K is the proportion of random windows with R lower than Robs . Because randomly sampled windows have globally comparable demographic histories , increases or decreases of the local diversity variance due to demographic expansions or contractions should be partially accounted for in the estimation of K ( Text S2 ) . We introduced a weighing scheme to account for varying base composition and repeat density , two major factors that are known to affect genetic diversity in primate genomes . This scheme also controls for the random nature of genome shotgun sequencing , where variable numbers of reads covering a given position affect the probability of detecting the two alleles . To do so , we first compute nij the number of positions of the tested window L occupied by a specific base i ( A , T , C or G ) identified or not in a repeat by RepeatMasker and covered by a number of reads j . For instance if a given window includes 4 , 500 positions occupied by nucleotide G outside of a repeat and covered by four reads then nG4 = 4 , 500 . The same procedure is applied for DNA inside repeats . Next , we compute rg for the genomic background window weighted by nij in the tested window:where pHij and pDij are respectively the proportions of heterozygous and divergent sites for all sites of class nucleotide i and coverage j in the genomic background window . We show , using simulations , that this weighing scheme removes the effect of a diverse range of factors that may bias measures of genetic diversity ( Text S1 ) . In this study , K was measured for local window sizes of 200 kb for human , 100 , 150 and 200 kb for orangutan , 200 kb and 300 kb for chimpanzee , 70 kb , 100 kb and 140 kb for macaque . Larger windows were used for chimpanzee to account for its lower level of heterozygosity , while smaller windows were used for macaque due to its higher heterozygosity . The power to detect sweeps depends on the average initial level of heterozygosity ( a lower initial level of heterozygosity means that there will be less contrast between a selective sweep and the neutral background ) , which can be compensated by adjusting window size so that the average number of heterozygous sites per window remains similar between species . Windows were excluded if they did not meet specific criterions: at least 60% of sites must be sequenced in the two species and be covered by less than 20 shotgun reads . For each measure of K , the genomic background window was adjusted to span 20 times the size of local windows , 10 times downstream and 10 times upstream . K was measured using 5 , 000 random resamplings for analyses with windows centred on genes ( method validation and co-occurrence test ) , and 1 , 000 random samplings for windows sliding along chromosomes ( Figure 1 , Figure 7 , Figure S7 , Table S3 ) . The four primate species possess 14 , 480 mutual orthologs , of which 9 , 972 were tested in all four species . The remaining genes either reside on sexual chromosomes ( human and chimpanzee genomes are sequenced from males , and thus provide no heterozygosity data for the X chromosome ) or are located in windows that did not meet the required criterions to measure K . To test for co-occurrence , the following datasets were used for each species . In human , the average K between CV and JW was used to select candidate genes with K lower than a fixed threshold in 200 kb windows centred on the genomic centres of genes . These criterions showed the best overlap with published scans for selective sweeps [11]–[14] . In other primates , a candidate gene was selected if one of the three window sizes centred on this gene had K lower than the same threshold used for human . We then computed the sum of pairs ( C2 ) , triplets ( C3 ) and quartets ( C4 ) of genes seen in a putative sweep respectively in human and one , two or three species simultaneously ( Figure S5 ) . The human genome was then shuffled randomly and C2 , C3 and C4 computed for 100 , 000 iterations . More specifically , for each iteration , the human genome was randomly divided into 20 intervals , with the gene order preserved within a given interval . Intervals were then rearranged in a random order and the sum C2 , C3 and C4 computed across the randomized genome . The preservation of gene order within the intervals accounts for the clustered organisation of candidate sweep-associated orthologous genes . Clustering reflects the fact that a selective sweep in primate genomes often spans several neighbouring genes . This increases the variance , while leaving the average sum of co-occurrences unaffected . Increasing the number of intervals used for shuffling genes at each iteration did not change the results . Several genomic factors such as recombination or gene density are correlated with K and have to be accounted for in our co-occurrence test . Such factors are indeed likely to increase the expected co-occurrence if they are conserved across species . We controlled for these factors separately by dividing the genes into n classes delimited by the n−1 quantiles of the factor to account for , and then running permutations within each class separately . We found that dividing the genes into 10 classes is sufficient in each case , since no gain of co-occurrence was observed when using more classes . Introducing classes however requires two corrections . First , dividing genes into classes can destroy clusters of contiguous candidate genes , thus reducing the variance of co-occurrence obtained after 100 , 000 permutations . Since the distribution of random co-occurrences is normal and since clustering does not affect the mean of this distribution but only its variance , we can address this issue by allocating the variance measured on the distribution without any class , to the distribution with 10 classes ( Figure 3 ) . Second , the simple fact of dividing genes into classes inflates the expected average level of co-occurrence in the presence of hotspots . This is due to the fact that each hotspot once “trapped” into a specific class will be randomized across a much smaller number of genomic locations and thus reconstructed randomly more frequently than when no class is defined . We accounted for this effect as follows: measures of K were first randomly permuted across genes , thus effectively removing the specific effect of any putative correlated genomic factors . This step was followed by 100 iterations of the co-occurrence test , and the two successive operations were repeated 1 , 000 times . The difference between the resulting average co-occurrence score and the average score when no classes are used finally represents the effect of using classes , independently of any genomic factor . This difference was therefore substracted from the average co-occurrence score every time classes were defined to account for genomic factors . Finally , two factors could be tested simultaneously based on the information that a specific gene may for instance be in class 3 ( out of 10 ) for factor 1 and in class 6 for factor 2 , thus belonging to the class ( 3 , 6 ) used together with 99 other combinations in the co-occurrence test . This was done in particular to test the effect on co-occurrence of recombination and gene density considered simultaneously . Genome assemblies with softmasked repeated sequences identified by RepeatMasker , for human ( HG18 ) , chimpanzee ( PanTro2 ) , orangutan ( PonAbe2 ) and macaque ( RheMac2 ) were downloaded from the UCSC genome browser ( http://hgdownload . cse . ucsc . edu/ ) . Human-chimpanzee , chimpanzee-human , orangutan-human , human-orangutan , macaque-human and human-macaque Blastz alignments [67] in axt . net format were also downloaded from the UCSC genome browser . Levels of shotgun sequencing coverage were measured using different strategies depending on the availability of read location information . For chimpanzee , orangutan and macaque , coverage was directly deduced from read positions downloaded from the Washington University Genome Sequencing Center WUGSC website ( http://genome . wustl . edu/pub/organism/Primates/ ) in reads . placed files . For the two human individuals , reads were first downloaded from the NCBI Trace archive site ( ftp://ftp . ncbi . nih . gov/pub/TraceDB/ ) and mapped on the NCBI36 human genome assembly using Blat [68] with the -fastMap and minimal 95% identity options activated . Only those reads mapped on more than 80% of their length were retained to measure coverage . Heterozygous sites for the two human individuals were retrieved from the J . Craig Venter Institute web site ( http://www . jcvi . org/ ) and from the Jim Watson Sequence website at CSHL ( ftp://jimwatsonsequence . cshl . edu/jimwatsonsequence/ ) , respectively . For chimpanzee , orangutan and macaque reads were first downloaded from the NCBI Trace archive ( ftp://ftp . ncbi . nih . gov/pub/TraceDB/ ) . Reads were then mapped on genome assemblies using ssahaSNP2 [53] ( parsing parameters -identity = 92 -match = 80 -copy = 20 -cover = 20 ) to detect heterozygous SNPs . All analyses were conducted using Ensembl v48 annotations for protein coding genes and their homology relationships , except for PAML analysis where Ensembl v52 were used [69] ( http://www . ensembl . org/ ) . A total of 14 , 480 human-chimpanzee-orangutan-macaque four-way orthologs were found . We used the likelihood ratio test 2 of the PAML package [20] , [21] to detect positive selection separately in the five # labeled branches of the following phylogenetic tree: ( ( ( human # , chimp # ) # , orangutan # ) , macaque # , mouse ) We first retrieved protein and coding sequences of all Ensembl v52 human-chimp-orangutan-macaque-mouse five-way one-to-one orthologs . When a gene had multiple protein and coding sequences , only the longest were considered for further analysis . Nucleotides in chimpanzee , orangutan and macaque coding sequences with a Phred quality lower than 20 were excluded together with their 10 downstream and 10 upstream nucleotides neighbours . Downstream and upstream positions were also excluded because we noticed that nucleotides with quality lower than 20 were often found close to each other , thus raising doubts about the quality of interspaced nucleotides . This procedure indeed reduced the rate of false positives due to sequence inconsistencies ( data not shown ) . Protein sequences were aligned with MAFFT [70] with high accuracy options activated . Coding sequence alignments were then obtained by projection on protein alignments . Only those 11 , 293 alignments containing at least 50 codons with no excluded nucleotide , starting with a start codon in one of the species and with at least one synonymous substitution between each pair of species were finally tested for positive selection . Affymetrix Human Exon microarray expression data for eleven tissues ( breast , cerebellum , heart , kidney , liver , muscle , pancreas , prostate , spleen , testis and thyroid ) was downloaded from the UCSC genome browser database . Values of expression for each Ensembl gene correspond to the average deduced from all probesets mapping the exons of a gene . Expressions of human genes candidates for positive selection were compared with expressions of the remaining genes using the log of Relative Abundance [47] . Gene Ontology annotations [71] of biological processes were analysed using the FatiGO and FatiScan [48] , [49] software available at http://babelomics . bioinfo . cipf . es/ . Recombination and gene densities were controlled for in our test of co-occurrence . Recombination rates from the HapMap release 22 build 36 and estimated by LDHat [33] , [54] were downloaded at http://www . hapmap . org/ . The average recombination rate ( cM/Mb ) was calculated for 200 kb windows centred on genes . Gene densities were measured as the number of genes present within 100 kb downstream and 100 kb upstream of every gene . Other sizes from 200 kb to 1 Mb and from 100 kb to 2 Mb were investigated for gene density and recombination , respectively , but 100 kb and 200 kb are the ones showing the strongest effects when testing co-occurrence , respectively . Average sequencing depth , proportion of divergent sites for every possible pair of species , and average GC content were measured for windows ranging from 100 to 1000 kb , none of which had an impact on co-occurrence . Correlations shown in Results are for 200 kb windows . We compared our set of candidate positively selected genes ( K≤0 . 05 ) in human with those found in four published scans for selective sweeps in the European population [11]–[15] . To compare several sets of genes and measure the level of observed overlap versus the level of expected overlap , the numbers of genes involved in the comparison must be of the same order of magnitude and large enough to avoid exceedingly high variance in estimated overlap . For these reasons and when needed , we use relaxed criteria to include larger numbers of genes in a given set than provided in highly specific shortlists in the original publications . By doing so , the frequency of potential false positive might increase , but this makes our conclusions conservative since the objective is only to compare the sets of genes relative to each other . In the study by Voight et al . [12] the 460 selected genes overlap regions where at least 20 out of 50 SNPs show an |iHS|≥2 in the HapMap phase II data . In the analysis by Williamson et al . [11] 444 genes with an associated p-value lower than 10−4 were selected . The 1 , 030 genes from the Tang et al . study [14] are those found within the candidate genomic intervals provided as supporting material of this publication . The 986 genes selected from the Carlson et al . scan [13] are those found within the 200 largest areas of negative Tajima's D in the genome . XP-EHH values were downloaded from the UCSC Genome Browser [15] . 516 genes with XP-EHH ≥2 at their genomic centre were used for comparison with other scans .
|
An advantageous mutation spreads from generation to generation in a population until individuals that carry it , because of their higher reproductive success , completely replace those that do not . This process , commonly known as positive Darwinian selection , requires the selected mutation to induce a new non-neutral heritable phenotypic trait , and this has been shown to occur unexpectedly frequently during recent human evolution . Although the exact advantageous mutation is difficult to identify , it leaves a wider footprint on neighbouring linked neutral variation called a selective sweep . We have developed an empirical method that uses whole-genome shotgun sequences of single individuals to detect selective sweeps . By doing so , we were able to extend to chimpanzee , orangutan , and macaque individuals analyses of recent positive selection that until now were only available for human . Comparisons of genes candidates for positive selection between human and non-human primates then revealed an unexpectedly high number of cases where a selective sweep at a gene in humans is mirrored by independent positive selection at the same gene in multiple other primates . This result has future implications for understanding the nature of biological changes that underlie selective sweeps in humans .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"computational",
"biology/population",
"genetics",
"genetics",
"and",
"genomics/genomics",
"genetics",
"and",
"genomics/comparative",
"genomics",
"evolutionary",
"biology/evolutionary",
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2010
|
Human and Non-Human Primate Genomes Share Hotspots of Positive
Selection
|
Brassica napus ( canola ) cultivars and isolates of the blackleg fungus , Leptosphaeria maculans interact in a ‘gene for gene’ manner whereby plant resistance ( R ) genes are complementary to pathogen avirulence ( Avr ) genes . Avirulence genes encode proteins that belong to a class of pathogen molecules known as effectors , which includes small secreted proteins that play a role in disease . In Australia in 2003 canola cultivars with the Rlm1 resistance gene suffered a breakdown of disease resistance , resulting in severe yield losses . This was associated with a large increase in the frequency of virulence alleles of the complementary avirulence gene , AvrLm1 , in fungal populations . Surprisingly , the frequency of virulence alleles of AvrLm6 ( complementary to Rlm6 ) also increased dramatically , even though the cultivars did not contain Rlm6 . In the L . maculans genome , AvrLm1 and AvrLm6 are linked along with five other genes in a region interspersed with transposable elements that have been degenerated by Repeat-Induced Point ( RIP ) mutations . Analyses of 295 Australian isolates showed deletions , RIP mutations and/or non-RIP derived amino acid substitutions in the predicted proteins encoded by these seven genes . The degree of RIP mutations within single copy sequences in this region was proportional to their proximity to the degenerated transposable elements . The RIP alleles were monophyletic and were present only in isolates collected after resistance conferred by Rlm1 broke down , whereas deletion alleles belonged to several polyphyletic lineages and were present before and after the resistance breakdown . Thus , genomic environment and exposure to resistance genes in B . napus has affected the evolution of these linked avirulence genes in L . maculans .
The fungus Leptosphaeria maculans causes blackleg ( phoma stem canker ) and is the major disease of Brassica napus ( canola ) worldwide [1] . The major source of inoculum is wind-borne ascospores , which are released from sexual fruiting bodies on infected stubble ( crop residue ) of previous crops and can be transmitted several kilometres . This fungus has a ‘gene for gene’ interaction with its host ( canola ) such that pathogen avirulence alleles render the pathogen unable to attack host genotypes with the corresponding resistance genes [2] . Twelve genes conferring resistance to L . maculans ( Rlm1-9 , LepR1-3 ) have been identified from Brassica species [3] , [4] . Nine of these genes have been mapped but none have yet been cloned [5] , [6] . Of the corresponding avirulence genes in L . maculans , seven have been mapped to two gene clusters , AvrLm1-2-6 and AvrLm3-4-7-9 , located on separate L . maculans chromosomes [7] . Three genes , AvrLm1 , AvrLm6 and AvrLm4-7 , have been cloned and characterised [8] , [9] , [10] . Avirulence proteins belong to a class of molecules called effectors . Effectors are small molecules or proteins produced by the pathogen that alter host-cell structure and function , facilitate infection ( for example , toxins ) and/or induce defence responses ( for example , avirulence proteins ) , and are generally essential for disease progression [11] . Many effectors are small , secreted proteins ( SSPs ) , which are cysteine-rich , share no sequence similarity with genes from other species , are often highly polymorphic between isolates of a single species and are expressed highly in planta [12] . The AvrLm1 , AvrLm4-7 and AvrLm6 proteins of L . maculans are effector molecules with an avirulence function . AvrLm6 and AvrLm4-7 encode SSPs with six and eight cysteine residues , respectively , whilst the AvrLm1 protein , which is also a SSP , has only one cysteine [8] , [9] , [10] . Leptosphaeria maculans undergoes sexual recombination prolifically and populations can rapidly adapt to selection pressures imposed by the host such as exposure to resistance conferred by single or major genes . This situation increases the frequency of virulent isolates and can cause resistance to break down , often resulting in severe yield losses [13] . Three examples of this breakdown are discussed below , the most dramatic one occurring in Australia in 2003 . Prior to 2000 the Australian canola industry relied on ‘polygenic’ cultivars with multiple resistance genes . Yield losses were generally low . In 2000 , cultivars with major gene resistance , termed ‘sylvestris’ resistance , were released commercially and grown extensively in some areas of Australia . These cultivars were derived from a synthetic B . napus line produced by crossing the two progenitor species , B . oleracea subsp . alboglabra and an accession of B . rapa subsp . sylvestris that had a high level of resistance to L . maculans [14] . For several years these cultivars showed little or no disease but in 2003 , resistance failed , resulting in up to 90% yield losses in the Eyre Peninsula , South Australia , costing the industry between $5–10 million AUD [15] , [16] . These cultivars contained resistance genes Rlm1 and RlmS , suggesting that both sources of resistance failed simultaneously [17] . After 2004 , these cultivars were withdrawn from sale although they were still grown in yield trial sites around Australia . A similar but less dramatic situation occurred in France when resistance conferred by Rlm1 was rendered ineffective within five years of commercial release of Rlm1-containing cultivars [13] , [18] . Another breakdown of resistance was observed in field trial experiments in France that had been designed to assess the durability of a resistance gene Rlm6 . In these experiments B . napus lines containing Rlm6 were sown into L . maculans-infected stubble of a Rlm6–containing line over a four year period [19] . After three years of this contrived selection , the frequency of virulence in fungal populations towards Rlm6 was so high that this resistance was rendered ineffective and the lines suffered extremely high levels of disease . The three avirulence genes , AvrLm1 , AvrLm6 and AvrLm4-7 , cloned from L . maculans are located within AT-rich , gene-poor regions that are riddled with degenerated copies of transposable elements [8] , [9] , [10] . These transposable elements appear to have been inactivated via repeat-induced point ( RIP ) mutations [20] . RIP is an ascomycete-specific process that alters the sequence of multicopy DNA . Nucleotide changes CpA to TpA and TpG to TpA are conferred during meiosis , often generating stop codons , thereby inactivating genes [21] . Additionally , RIP mutations have been inferred bioinformatically in various transposable elements throughout the L . maculans genome and in AvrLm6 [22] . AvrLm1 and AvrLm6 are genetically linked and different types of mutations leading to virulence have been reported . The entire AvrLm1 locus was deleted in 285 of 290 ( 98% ) isolates that were virulent towards Rlm1 [20] . Fudal et al . characterised the AvrLm6 locus in a different set of 105 isolates , most of which were cultured from Rlm6–containing lines during the field trial in France described above [22] , [23] . Deletions and RIP were responsible for virulence in 45 ( 66% ) and 17 ( 24% ) isolates , respectively , that were virulent towards the Rlm6 gene [22] . In this paper we relate changes in the types and frequencies of mutations in genes including AvrLm1 and AvrLm6 to the selection pressure imposed by extensive regional sowing of B . napus cultivars with sylvestris resistance .
In preliminary experiments to see if the breakdown of ‘sylvestris resistance’ in B . napus seen in the field [15] correlated with changes in frequency of virulence towards Rlm1 in individual L . maculans isolates , 11 isolates collected prior to the breakdown ( before 2004 ) and 12 isolates collected after the breakdown ( 2004 onwards ) were screened for virulence on B . napus cultivars Q2 ( with Rlm3 ) and Columbus ( Rlm1 , Rlm3 ) . Cotyledons of 14 day old plants were inoculated with individual isolates and symptoms were scored 17 days later . All isolates were virulent on the susceptible control , cv . Q2 . Ten of the 11 isolates collected prior to 2004 were avirulent on the Rlm1-containing cultivar , Columbus . However , seven of ten isolates collected from 2004 onwards were virulent towards Rlm1 ( Table 1 ) . A subset of these isolates was inoculated onto the B . napus cultivar Aurea ( Rlm6 ) . Of seven of the 11 isolates collected prior to 2004 , only one was virulent towards Rlm6 . However , of 12 isolates collected from 2004 onwards , eight were virulent towards Rlm6 ( Table 1 ) . These results suggested that there was a significant change in frequencies of virulent alleles of AvrLm1 and AvrLm6 associated with breakdown of ‘sylvestris resistance’ . These isolates were then genotyped at the AvrLm1 , AvrLm6 and mating type loci using PCR-based screening [20] , [22] , [24] and as expected , the virulence phenotypes corresponded with avrLm1 and /or avrLm6 genotypes ( Table 1 ) . Changes in allele frequencies were then examined in a total of 295 Australian isolates . Of these 137 were collected between 1987 and 2003 , prior to the breakdown of sylvestris resistance , whilst the remaining 158 isolates were collected between 2004 and 2008 , after the resistance breakdown ( Table S1 ) . These isolates were collected from stubble of a range of canola cultivars with different resistance genes . One third of the isolates had a deletion of AvrLm1 ( Table 2 ) , whilst 63% had the allele of isolate v23 . 1 . 3 , whose genome has been sequenced . Alleles of this isolate hereafter are referred to as wild type alleles ( e . g . AvrLm1-0 ) . As expected , isolates with AvrLm1-0 were avirulent towards Rlm1 ( Tables 1 and 2 ) . The remaining eight isolates comprised four alleles with coding sequence changes conferring non-synonymous substitutions ( I125K and/or H155Y ) . Isolates harbouring these alleles were avirulent on the Rlm1- containing B . napus line ( Table 1 ) . Thirteen alleles of AvrLm6 were detected ( Table 2 ) . AvrLm6 was deleted in 20% of isolates , thus conferring virulence towards Rlm6 , whilst 24% had the allele of the sequenced isolate and conferred avirulence towards Rlm6 ( Tables 1 and 2 ) . Other isolates virulent towards the Rlm6-containing cultivar had alleles with stop codons conferred by base changes reminiscent of RIP mutation . Accordingly , allele sequences were analysed by RIPCAL , a software tool that visualises the physical distribution of RIP mutation and reports a RIP dominance score indicating the degree of RIP in each sequence . Sequences with RIP dominance scores >1 are highly RIP-affected having a high proportion of CpA to TpA , or TpG to TpC RIP mutations [25] . RIPCAL analysis did not detect RIP in any AvrLm1 alleles , but detected RIP in seven AvrLm6 alleles . These RIP mutations resulted in numerous non-synonymous changes as well as premature stop codons ( between 4 and 6 ) , which were within all RIP-affected alleles ( Table 2 ) . Southern hybridisation results suggest that there is only a single copy of the AvrLm6 locus within isolates harbouring the RIP alleles ( Figure S1 ) . The remaining alleles of AvrLm6 ( AvrLm6-1 , -2 , -3 and -4 ) harboured single or few nucleotide changes leading to synonymous or amino acid substitutions ( G123C , K127E , F54L ) compared to isolate v23 . 1 . 3 ( AvrLm6-0 ) . These amino acid substitutions were generated via non-RIP like mutations ( henceforth referred to as non-RIP amino acid substitutions ) . Isolates harbouring AvrLm6-1 , AvrLm6-3 or AvrLm6-4 alleles were avirulent towards cv . Aurea , whilst the four isolates harbouring the AvrLm6-2 allele ( G123C ) were virulent ( Table 1 ) . No RIP-affected alleles were present in isolates collected prior to the breakdown of sylvestris resistance . However , the 158 isolates collected after the breakdown had seven AvrLm6 RIP alleles ( frequency of 8 . 9% ) and there was a very large increase in the frequency of deletion alleles of AvrLm1 ( 22 . 6 to 41 . 8% ) and AvrLm6 ( 4 . 4 to 38 . 7 ) . This was a nine-fold increase in frequency of avrLm6 ( Table S2 ) . Thirty four ( 11 . 5% ) isolates had deletions of both AvrLm1 and AvrLm6 and only one of these isolates was collected prior to the breakdown . All 295 isolates were grouped into four genotypic classes ( AvrLm1 , AvrLm6; AvrLm1 , avrLm6; avrLm1 , Avrlm6; avrLm1 , avrlm6 ) . The frequency of AvrLm1 , AvrLm6 isolates was 73 . 7% prior to the breakdown , but decreased to 37 . 3% afterwards ( Table 3 ) . Conversely , the frequency of avrLm1 , avrlm6 isolates was only 0 . 8% prior to the breakdown , but increased to 28 . 5% afterwards . Nine of the 14 isolates harbouring RIP alleles ( 64% ) had a deletion allele at the AvrLm1 locus and all these isolates were cultured from stubble of cultivars with sylvestris resistance . Additionally , all four isolates harbouring the virulent AvrLm6-2 allele ( G123C ) had a deletion allele at AvrLm1 ( Table S3 ) . When the isolates cultured from 2004 onwards were categorised in terms of the stubble from which they were derived , all those cultured from ‘sylvestris stubble’ had the avrLm1 allele ( Table 3 ) as expected , due to the presence of Rlm1 in these cultivars [17] . Conversely , only 15% of isolates cultured from stubble of polygenic cultivars , which do not have Rlm1 nor Rlm6 , had the avrLm1 allele . The frequency of avrLm6 was 38 . 7% in isolates cultured from ‘polygenic’ stubble , compared to 68 . 7% of isolates cultured from ‘sylvestris’ stubble ( Table 3 ) . For all comparisons of isolates , there were no significant differences in allele frequencies at the mating type locus . Since the mating-type locus is not under selection pressure , a 1∶1 ratio of each allele suggests that sampling of isolates has been random ( data not shown ) . Because of the marked difference in the AvrLm1 and AvrLm6 allele frequencies before and after the breakdown of sylvestris resistance , the region flanking these genes was characterised to identify any features that might have influenced allele frequency . A 520 kb AT-rich genomic region bordered by AvrLm1 and LmCys2 ( Figure 1A ) was examined . Part of this region has been described previously in isolate v23 . 1 . 3 [8] and includes two additional genes encoding SSPs , LmCys1 and LmCys2 . Three other genes , LmTrans , LmGT and LmMFS were present; LmCys1 , LmTrans , LmGT , LmMFS and LmCys2 have been reported previously [8] , [9] but sequence data are only in the form of BAC clones . The features of the proteins are listed below and in Table S4 . The predicted LmCys1 protein ( 220 aa ) contained eight cysteine residues and its single match was to a ‘secreted in xylem’ Six1 effector ( also known as Avr3 ) of Fusarium oxysporum ( 26% identity , 40% similarity , accession number CAE55870 . 1 ) [26] . The predicted LmCys2 protein ( 247 aa ) , also containing eight cysteine residues , had no matches within the NCBI database . Both LmCys1 and LmCys2 were predicted to be secreted with signal peptides of 19 and 18 aa , respectively . To determine whether LmCys1 and LmCys2 were expressed highly in planta , which would be expected of genes encoding effector-like proteins , quantitative reverse transcriptase PCR ( RT-PCR ) analyses were performed . Transcript levels of LmCys1 and LmCys2 in planta were six times higher than those of actin at seven days after inoculation of cotyledons of a susceptible B . napus cultivar ( Figure 2 ) . LmCys1 and LmCys2 were expressed at 0 . 1 and 0 . 01 times , respectively , that of actin in seven day in vitro cultures . Similar results were obtained with a second isolate ( data not shown ) . This extremely high level of expression in planta compared to in vitro is similar to that seen for AvrLm1 and AvrLm6 ( Figure 2 ) [8] . These characteristics ( small cysteine-rich secreted proteins with no or few matches in databases and high in planta expression ) strongly suggested that LmCys1 and LmCys2 , like AvrLm1 and AvrLm6 , encode effector-like proteins . The LmTrans protein ( 373 aa ) contained a DDE superfamily endonuclease domain predicted to be involved in efficient DNA transposition , and its best match was a putative transposase from Stagonospora nodorum ( 61% identity , 68% similarity , accession number CAD32687 . 1 ) . The predicted LmGT protein ( 414 aa ) was a putative glycosyltransferase with best matches to hypothetical protein PTRG_04076 of Pyrenophora tritici-repentis ( 89% identity , 95% similarity , EDU46914 . 1 ) . The LmMFS protein ( 591 aa ) belonged to the Major Facilitator Superfamily ( MFS ) . This protein had best matches to putative protein SNOG_04897 from S . nodorum ( 74% identity , 82% similarity , EAT87288 . 2 ) . Since LmCys1 and LmCys2 had effector-like properties and thus putative roles in the plant-fungal interaction , these genes were sequenced in the 295 isolates . Five and two alleles were detected for LmCys1 and LmCys2 , respectively ( Table 1 ) , including the wild type alleles obtained from published BAC sequences . RIPCAL analysis showed that only one isolate , which was collected after the breakdown of sylvestris resistance had a RIP allele of LmCys1 , and there were no deletion alleles , whereas there were no RIP alleles of LmCys2 , but two isolates collected before breakdown of sylvestris resistance had a deletion of LmCys2 ( Table 1 ) . Overall , the frequencies of individual alleles ranged from 99% for LmCys2-0 to 0 . 3% for AvrLm1-4 . The 295 isolates comprised 34 haplotypes based on alleles of AvrLm1 , AvrLm6 , LmCys1 and LmCys2 ( Table S3 ) . Four non-coding , non-repetitive regions ( Figure 1A ) ranging in size between 247 and 657 bp were analysed , to see whether single copy non-coding regions , like the single copy genes , were affected by RIP mutation . These regions and the three other genes LmTrans , LmGT and LmMFS in this region ( Figure 1A ) were sequenced in a subset of 84 of the 295 isolates , which included isolates representing all 34 haplotypes described above ( Table S3 ) . Three , four , 14 and two alleles were detected for NC1 , NC2 , NC3 and NC4 , respectively , whilst two , one , and three alleles were detected for LmTrans , LmGT and LmMFS , respectively ( Table 4 and Table S5 ) . The mutations giving rise to the alleles of NC1 , NC2 and LmMFS were single non-RIP-like nucleotide substitutions ( both synonymous and non-synonymous ) , and single base pair deletions . No polymorphisms were detected in LmGT and no RIP alleles were detected for NC1 , NC2 , LmMFS or LmGT . A single isolate had RIP alleles for NC4 and LmTrans in addition to NC3 , AvrLm6 and LmCys1 . The NC3 region had an extremely high frequency of RIP mutation and the ten RIP alleles were all associated with AvrLm6 RIP alleles . Based on all seven genes and four non-coding regions , 51 haplotypes were identified among the 84 isolates ( Table S6 ) . The distribution and degree of RIP mutation across each allele of each gene was determined . This was represented as a ratio of the number of mutated CpA and TpG sites relative to number of potential RIP sites in isolate v23 . 1 . 3 in a 100 bp rolling window . The seven RIP alleles of AvrLm6 had the highest frequency of RIP towards the 3′ end ( Figure 3A ) . The frequency of RIP was higher within the 3′ UTR and the exons , than in the introns and 5′ UTR ( Figure 3B ) . The ten RIP alleles of NC3 and single RIP allele of LmCys1 showed the highest frequency of RIP at their 5′ ends , whilst RIP mutations were evenly distributed throughout the single RIP allele of LmTrans ( Figure S2 ) , the latter gene being the furthest 3′ characterised single copy sequence affected by RIP mutation within the AvrLm1-LmCys2 genomic region ( Figure 3C ) . The NC3-Avrlm6 and LmCys1-NC4-LmTrans single copy regions in which RIP mutations were detected , were separated by 37 kb of repetitive elements ( Figure 1B ) . Both these single copy regions were closer to upstream repetitive elements ( <342 bp ) than to downstream repetitive elements ( >1 kb ) ( Figure 1B ) . The degree of RIP within these single copy regions was proportional to their proximity to repetitive elements , and thus a gradient of RIP mutations was apparent in a 5′ to 3′ direction ( Figures 1B and 3B ) . Since amongst these single copy regions the degree of RIP mutations was highest in NC3 , a repeat region ( 620 bp ) directly upstream was analysed to see if it was severely RIP-affected and thus might act as a point of ‘leakage’ of RIP into NC3 and AvrLm6 ( Figure 1B ) . BLAST analysis of the genomic sequence of isolate v23 . 1 . 3 revealed 293 copies of this repeat . RIPCAL analysis showed that the repeat directly upstream of NC3 was amongst the most highly RIP-affected copy of that repeat in the genome . The mutations of AvrLm1 , AvrLm6 , LmCys1 and LmCys2 ( deletions or RIP mutations including stop codons ) would be expected to lead to lack of transcription of these alleles . This hypothesis was assessed in a range of isolates seven days after inoculation of cotyledons of B . napus cv . Beacon , which all the isolates could attack . Primers designed to amplify 500–700 bp products within the coding region of these genes were used in end-point RT-PCR ( Table 5 ) . Isolates with either AvrLm1-0 or AvrLm1-1 allele had an AvrLm1 transcript of the appropriate size . As expected , isolates with the deletion allele had no transcript . The AvrLm6-0 , AvrLm6-1 or AvrLm6-2 alleles were expressed , whilst the RIP alleles , AvrLm6-6 , AvrLm6-7 , AvrLm6-8 or AvrLm6-9 were not . As expected , no expression of AvrLm6 or of LmCys2 was detected in isolates with the deletion alleles of these genes . Isolates with LmCys1-0 , LmCys1-1 or LmCys1-2 alleles had an LmCys1 transcript , whilst the isolate with the RIP allele , LmCys1-4 , did not . Actin transcripts were detected in all isolates . To determine the rates of mutations , genes were analysed by phylogeny-based likelihood ratio tests ( LRT ) implemented in the program HyPhy [27] . These tests suggested that nucleotides of AvrLm1 and LmMFS mutated at a constant rate , i . e . mutations did not deviate significantly ( p = 0 . 544 ) from a clock-like rate of evolution . In contrast , an accelerated mutation rate was detected for AvrLm6 and LmCys1 loci ( P<0 . 001 ) compared to expectations under constant ( i . e . clock-like ) evolution . However , when RIP alleles were excluded , mutations evolved at a clock-like rate ( Table 6 ) . Genetic divergence between haplotypes was calculated using Molecular Evolutionary Genetics Analysis ( MEGA ) software [28] . Relative rates of sequence evolution of non-RIP alleles were much lower than those of RIP alleles ( Table 6 ) . To determine whether the seven proteins were undergoing positive selection , the rates of non-synonymous and synonymous substitutions were compared . All RIP alleles were excluded from this analysis since they contained multiple stop codons . Evolution of codon changes within AvrLm1 and LmCys1 was best explained by a model of positive selection , as shown by a likelihood ratio test implemented using two complementary approaches , the sitewise likelihood-ratio ( SLR ) and phylogenetic analysis by maximum likelihood ( PAML ) methods ( Table 7 ) . This interpretation was supported by the finding of positive selection at a single codon site in AvrLm1 and at three sites in LmCys1 ( Table 7 ) . Analysis of the AvrLm6 protein using the SLR approach suggested a single amino acid ( codon 54 ) may be undergoing positive selection; however , this finding was not supported by the PAML analysis . Similar analyses could not be performed on LmCys2 , LmGT or LmTrans as these genes had fewer than three alleles . Two hypotheses for the evolution of RIP alleles , monophyly ( having a single origin or having evolved only once ) and polyphyly ( multiple origins or having evolved several times independently ) were tested by comparing tree topologies using the Kishino–Hasegawa ( KH ) test [29] . For both the Maximum Parsimony ( MP ) and Maximum Likelihood ( ML ) approach , trees based on the assumption of a monophyletic origin of RIP alleles performed significantly better than those based on polyphyletic origin ( Table 8 ) . The phylogenetic relationship of all detected haplotypes is depicted in Figure 4 . The RIP and non-RIP associated alleles form two distinct clusters , supporting the hypothesis of a single origin of RIP alleles . In contrast , haplotypes associated with gene deletions are associated with multiple clades of the tree and have probably arisen several times .
Breakdown of disease resistance has been observed in other plant fungal pathogen systems where fungal populations evolve rapidly ( for review see [30] ) . In the canola- L . maculans interaction described here , strong selection pressure was exerted on the AvrLm1 locus , due to extensive sowing of sylvestris cultivars with Rlm1 , which was consistent with the finding of a rapid increase in the frequency of isolates with virulent ( avrLm1 ) alleles after the breakdown of resistance . Surprisingly the frequency of isolates virulent ( with the avrLm6 allele ) towards Rlm6 increased , although cultivars with polygenic or with sylvestris resistance have not been shown to contain Rlm6 [17] , [31] . The linkage and genomic location of AvrLm1 and AvrLm6 may have led to a selective sweep whereby selection at AvrLm1 affected the frequency of avrLm6 alleles through ‘hitchhiking’ . Thus strong selection imposed by wide-spread deployment of a plant resistance gene that favors a complementary effector allele in a pathogen could affect evolution of closely-linked effector genes . It is intriguing that in France recurrent sowing of Rlm6-containing cultivars in localised field trials led to an increase in avrLm6 isolates and a corresponding increase in isolates avirulent at the AvrLm1 locus [19] . This difference may be due to the different targets of selection pressure - Rlm6 in France and Rlm1 in our study . This situation whereby selection pressure on one gene affected allele frequencies of another may be partly due to the presence of these two effector genes in a repeat-rich region , where there is a low recombination frequency . Effectors in other fungi are present in repeat-rich regions . In the rice blast fungus , Magnaporthe oryzae , avirulence gene Avr-Pita is located 48 bp from telomere repeats whilst Avr1-CO39 is associated with transposable elements [32] , [33] . An avirulence gene ( SIX1 ) of Fusarium oxysporum f . sp . lycopersici is flanked by transposable elements [34] and other effectors are localised on a single , transposon-rich chromosome [35] . Toxin-encoding genes , Tox3 and ToxA , are located next to repetitive elements in Stagonospora nodorum [36] , [37] , [38] and effectors are in repeat-rich regions of the genome of the oomycete , Phytophthora infestans [39] . In the sequenced isolate of L . maculans , AvrLm1 and AvrLm6 are located amongst multiple copies of long terminal repeat retrotransposons , namely Pholy , Olly , Polly and Rolly , which are generally incomplete . Furthermore the distribution and number of these elements within this genomic location varies considerably between isolates [20] , [22] . The presence of effector genes within such regions is suggested to promote their adaptation and diversification when exposed to strong selection pressure [40] . Rep and Kistler have speculated that the presence of highly repetitive regions containing transposons , may promote mutation of resident effector genes [41] . The genes and non-coding regions undergoing RIP within the AvrLm1- LmCys2 gene region are single copy and therefore are not expected to be targeted by RIP . Two explanations for the presence of RIP mutations in these genes are as follows . Firstly , if these genes are the result of an ancestral duplication , RIP mutation may be acting directly on them . However , Fudal et al showed that no closely related paralogs of AvrLm6 exist in the genome of isolate v23 . 1 . 3 suggesting that RIP would not be targeting these sequences due to a duplication event [22] . Alternatively , the AvrLm6 locus may be completely or partially duplicated in isolates where RIP was detected . However , southern hybridisations suggest only a single copy of the Avrlm6 locus in isolates where RIP was detected , which does not support the possibility of RIP being targeted to this locus . A more likely explanation is that RIP mutations ‘leak’ from adjacent repetitive sequences . As RIP mutation is traditionally observed to be restricted to repetitive regions and not single copy regions , leakage of RIP mutation might occur within a relatively short distance of a RIP-affected repeat , as suggested by Fudal et al [22] . Indeed , this has been reported in N . crassa whereby leakage of RIP was detected in single copy sequences at least 930 bp from the boundary of neighbouring duplicated sequences [42] . This is consistent with our finding of a high frequency of RIP mutations in single copy regions of L . maculans with the degree of RIP mutation being proportional to the proximity of flanking repetitive elements . The potential ‘leakage’ of RIP mutations into closely linked effector genes highlights the power of this process to lead to major evolutionary changes to genes such as effectors that play an important role in the lifestyle of an organism . All haplotypes associated with RIP alleles of AvrLm6 and LmCys1 appeared to have a single origin indicating that the event that led to the RIP occurred only once . Furthermore , haplotypes associated with the virulent AvrLm6-2 allele ( cysteine substitution ) arose as a single clade . These single origins suggest that events leading to both the RIP mutations and non-RIP amino acid substitutions are much rarer than those leading to the deletion mutations . The RIP event occurred after the breakdown of sylvestris resistance , as isolates with RIP mutations were not detected before this time . This rapid appearance of RIP mutations is not unprecedented; such mutations have been shown to occur after one generation in L . maculans . When transformants with several tandemly linked copies of a hygromycin resistance gene were crossed with a wild type strain , none of the progeny were resistant to hygromycin due to the presence of multiple stop codons generated by RIP mutations in the hygromycin resistance gene [43] . Mutations associated with resistance to azole fungicides in Mycosphaerella graminicola also are derived from a single origin . Resistance emerged only once following strong selection due to widespread use of azole fungicides [44] . In both the M . graminicola and L . maculans examples , a similar phylogeny was found despite differences in origin and type of evolutionary pressure . In L . maculans haplotypes associated with deletion alleles conferring virulence towards AvrLm1 and AvrLm6 appeared to have a polyphyletic origin . Isolates with these deletions were detected prior to 2004 when the resistance breakdown occurred , albeit at a much lower frequency than afterwards . Haplotypes associated with deletion of both AvrLm1 and AvrLm6 might have been derived directly from the ancestral wild type rather than via the deletion of one gene followed by that of the second . Deletions , RIP mutations and non-RIP amino acid substitutions conferred virulence at the AvrLm6 locus , whilst only deletions were responsible for virulence at the AvrLm1 locus . Similar types of mutations were detected in French populations of L . maculans isolates [22] . The finding of a virulence allele of AvrLm6 arising from a non-synonymous , non-RIP like mutation , of a glycine to cysteine substitution , was intriguing . In other avirulence proteins the loss rather than the gain of cysteine through non-synonymous substitutions confers virulence . For example , in the AVR4 protein of Cladosporum fulvum , loss of cysteine residues renders the isolate virulent towards tomato cultivars with the corresponding Cf-4 resistance gene [45] . The AvrLm6-2 allele of L . maculans gives rise to a protein with seven rather than six cysteine residues in the protein encoded by AvrLm6-0 . This latter protein is proposed to have two disulphide bridges between C109 and C130 , and C103 and C122 [8] on the basis of the SCRATCH disulphide bond prediction program [46] . The program predicts that the presence of the additional cysteine ( C123 ) in the AvrLm6-2 allele would result in a third disulphide bridge , between C26 and C123 . As well as the mechanisms leading to inactivation of alleles described above , some of the proteins were undergoing non-RIP amino acid substitutions which did not lead to a change in phenotype . Some of these mutations , in AvrLm1 and LmCys1 , were the results of positive selection , which favours new mutations that confer a fitness advantage and thus lead to an increase in gene diversity [12] , [47] . Positive selection has been detected in pathogen effector genes including the avirulence gene that encodes NIP in Rhynchosporium secalis [47] and in genes encoding host-specific toxins such as S . nodorum ToxA [36] , [48] . In contrast to AvrLm1 , AvrLm6 and LmCys1 , the remaining genes in the 520 kb AT- rich region , including LmCys2 showed very little variation . Despite positive selection driving amino acid substitutions within some of the effector-like proteins , deletion and RIP mutations are by far the major mechanisms leading to virulence at the AvrLm1 and AvrLm6 loci .
Stubble of cultivars of B . napus and B . juncea infested with L . maculans was collected each year from 1997 to 2008 from 25 locations across Australia ( Table S1 ) . For instance , stubble from a crop sown in 2003 was collected from the field in 2004 and isolates were then cultured from it . Cultivars with ‘polygenic’ resistance ( Beacon , Dunkeld , Emblem , Grace , Hyden , Jade , Pinnacle , Skipton , Pinnacle and Tornado TT ) had one or more Rlm genes , but none had Rlm1 nor Rlm6 . The identity of resistance genes in some of these cultivars has been reported [44] . The category of ‘sylvestris resistance’ refers to cultivars ( Surpass 400 , Surpass 501TT , Surpass 603CL , 45Y77 and 46Y78 ) with resistance derived from B . rapa spp . sylvestris [14] have Rlm1 and RlmS [17] . The category of ‘juncea’ resistance refers to cultivars and lines of B . juncea ( cv . Dune and lines JC05002 , JC05006 and JC05007 ) . Stubble of the latter two categories was not collected prior to 2004 . From 2004 onwards although cultivars with sylvestris resistance were withdrawn from sale , these lines were grown in yield trials across Australia , and stubble was collected from them . Isolates ( 287 ) were cultured from individual ascospores discharged from stubble collected the previous year as described previously [15] . In addition , eight Australian isolates collected in 1987 and 1988 were analysed . All isolates were maintained on 10% Campbell's V8 juice agar . Virulence of a subset of isolates was tested on three B . napus and one B . juncea cultivars . The B . napus cv . Beacon and cv . Q2 are susceptible controls that all isolates could attack . Cultivar Columbus contains Rlm1 and Rlm3 and B . juncea cv . Aurea contains Rlm5 and Rlm6 [3] . No Rlm1-only or Rlm6-only cultivars were available . Cotyledons of 14-day old seedlings were wounded and inoculated with conidia of individual isolates representing different haplotypes for AvrLm1 and AvrLm6 . Symptoms were assessed at 10 , 14 and 17 days post-inoculation ( dpi ) and pathogenicity scores determined at 17 dpi by scoring lesions on a scale from 0 ( no darkening around wounds ) to 9 ( large grey-green lesions with profuse sporulation ) . Mean pathogenicity scores ( determined from 40 inoculation sites ) ≤3 . 9 were assigned as an avirulent phenotype whilst scores ≥4 . 0 were assigned as a virulent phenotype [17] . Non-coding , non-repetitive regions in the AvrLm1-LmCys2 genomic region and genes 3′ of AvrLm6 ( Figure 1 ) were identified using published information [22] and by BLAST searches . Primers were designed upstream and downstream of start and stop codons to allow analysis of the sequences of entire open reading frames . For transcriptional analyses , primers were designed to amplify a 500–700 bp region of the coding sequence , flanking an intron where possible . All primers were designed using the program Primer3 [49] ( Table S7 ) . Primers to amplify the mating type locus and actin have been described previously [8] , [24] . The sequence information for all genes has been deposited in GenBank with the following accession numbers; AvrLm1 , AM084345 [1] , AvrLm6 , AM259336 [2] , LmCys1 , GU332625 , LmTrans , GU332626 , LmGT , GU332627 , LmMFS , GU332628 and LmCys2 , GU332629 . Genomic DNA was isolated from mycelia . Conditions for all PCR experiments were 95°C for 3 min; 35 cycles of 95°C for 30 sec , 59°C for 30 sec and 72°C for 1 min; 72°C for 6 min . PCR products were purified using QIAquick PCR purification kit ( Qiagen ) and sequenced using BigDyeTM terminator cycling conditions . Sequences were analysed using Sequencher v 4 . 0 . 5 . Deletion genotypes were assigned if no band was produced following amplification with the AvrLm1 , AvrLm6 or LmCys2-specific primers . Amplification of the mating type locus was a positive control for DNA quality . In a subset of eight isolates , deletion alleles were confirmed by Southern analysis of genomic DNA that had been digested with HindIII and hybridised with the appropriate probe ( Figure S1 ) . Additionally , PCR screens were performed on genomic DNA from two ( for LmCys2 ) to 25 ( for AvrLm6 ) isolates using multiple primer sets that amplify specific regions of the AvrLm1 , AvrLm6 and LmCys2 gene regions . These amplifications confirmed that the entire locus was deleted in all isolates tested ( data not shown ) . Allele sequences were analysed by RIPCAL for the presence of RIP mutations [25] . RIPCAL generates a RIP dominance score , which is the frequency of the dominant dinucleotide RIP mutation ( in this case CpA→TpA ) relative to the sum of the alternative mutations ( CpC→TpC , CpG→TpG and CpT→TpT ) . Sequences with RIP dominance scores >1 are considered to be highly RIP-affected . The ‘model’ sequence used for all RIPCAL analyses was the ‘wild type’ allele ( designated with a -0 suffix ) from the isolate ( v23 . 1 . 3 ) whose genome has been sequenced . The spatial distribution of RIP was assessed for each gene and four non-coding regions by comparing the ratio of mutated CpA or TpG sites detected by RIPCAL , relative to the number of available CpA and TpG nucleotides present within the wild type allele over a 100 bp rolling window . Ten infected cotyledons of B . napus cv . Beacon were harvested at 7 dpi . Necrotic tissue surrounding the inoculation wounds of each cotyledon was harvested using a hole punch ( diameter 8 mm ) . Total RNA was purified from this tissue using the RNeasy Plant Mini Kit ( Qiagen ) and was treated with DNaseI ( Invitrogen ) before cDNA was synthesized using a first strand cDNA synthesis kit and an oligo-dT primer . End point RT-PCR was used to assess expression of AvrLm1 , AvrLm6 , LmCys1 , LmCys2 and actin . Quantitative RT-PCR was used to determine levels of expression of AvrLm1 , AvrLm6 , LmCys1 and LmCys2 in planta and in vitro culture . RNA was prepared from seven day old cultures of isolates with the wild type alleles of these genes , which were growing in 10% V8 juice . Additionally RNA was prepared from cotyledons of B . napus cv . Beacon 7 dpi infected with isolates with the wild type alleles of these genes . Total RNA and cDNA synthesis was performed as described above . Controls lacking reverse transcriptase were included . Quantitative RT-PCR was performed using Rotor-Gene 3000 equipment ( Corbett Research , Australia ) and QuantiTect SYBR Green PCR kit ( QIAgen ) . A standard curve of amplification efficiency of each gene was generated from purified RT-PCR products [50] . Diluted RT product ( 1 μl ) was added to 19 μl of PCR mix and subjected to 40 cycles of PCR ( 30 s at 94°C , then 60°C and then 72°C ) . All samples were analysed in triplicate . The amplified product was detected every cycle at the end of the 72°C step . Melt curve analysis after the cycling confirmed the absence of non-specific products in the reaction . The fluorescence threshold ( Ct ) values were determined for standards and samples using the Rotor-Gene 5 software . Ct values were exported to Microsoft Excel and analysed [51] . Actin was used as a reference gene . Deviation from a constant rate of molecular evolution within the data sets ( i . e . a “molecular clock” ) was assessed using the phylogeny-based likelihood ratio test ( LRT ) implemented in the program HyPhy [27] . To estimate the contribution of the RIP alleles , likelihoods were calculated both for the total data sets and for data sets excluding RIP alleles . MEGA was also used to infer relative rates of sequence evolution by calculating means of genetic distances ( Kimura-2-Parameter ) between haplotypes . Evidence for non-neutral evolution was assessed using two complementary approaches by comparing the rate of non-synonymous substitutions with the rate of synonymous substitutions ( dN/dS = ω ) . Firstly , the analysis was based on the “sitewise likelihood-ratio” method as implemented in the SLR software package [52] . The test consists of performing a likelihood-ratio test on a site-wise basis , testing the null model ( neutrality , ω = 1 ) against an alternative model ω≠1 ( i . e . purifying selection ω<1; positive selection ω>1 ) . Secondly , dN/dS = ω was tested using a phylogenetic analysis based on maximum likelihood as implemented in the PAML software package [53] . Two codon substitution models were compared via likelihood ratio tests ( LRT ) . The comparison included the likelihood estimates of the neutral null model ( M7 ) and the alternative model of positive selection ( M8 ) . RIP alleles were excluded from these analyses since such alleles encode sequences with stop codons . To test different hypotheses of emergence of haplotypes associated with RIP alleles ( Table S6 ) , tree topologies using concatenated DNA sequences of all the genes ( AvrLm1 , AvrLm6 , LmCys1 , LmTrans , LmGT , LmMFS and LmCys2 ) and non-coding , non-repetitive regions ( NC1-4 ) were generated and compared using the Kishino–Hasegawa ( KH ) test [29] as implemented in PAUP* 4 . 0b 10 . Since the RIP mechanism produces the same mutations at specific sites , it is likely that formerly unrelated nucleotide sequences converge , leading to the false impression of similarity due to common descent . To avoid this bias , all CpA to TpA and TpG to TpA nucleotide changes were removed from the data set prior to inferring the phylogenetic relationships of haplotypes . Two alternative hypotheses were then compared; ( i ) haplotypes containing RIP alleles were monophyletic , i . e . they emerged only once . Trees representing this hypothesis were “constrained” by restricting RIP alleles to cluster only amongst each other ( ii ) haplotypes containing RIP alleles were polyphyletic , i . e . they emerged several times independently . Trees representing this alternative hypothesis were “unconstrained” , i . e . the pairing of particular alleles in the topology was not restricted . One thousand trees were generated representing each hypothesis , and the probabilities ( P ) of obtaining better trees were assessed using two-tailed tests , the full optimization criterion and 1000 bootstrap replicates . The KH test was conducted for trees constructed under the maximum likelihood and the maximum parsimony criterion . The phylogenetic relationship among isolates based on the concatenated DNA sequences of all genes and non-coding , non-repetitive regions was assessed by PAUP* using maximum likelihood . Tree searches were conducted with the “fast-stepwise-addition” option and 1000 bootstrap replicates to assess statistical significance of nodes . The GTR-model with estimated substitution-rate matrix was used to evaluate molecular rate constancy .
|
The fungus Leptosphaeria maculans causes blackleg , the major disease of canola worldwide . Populations of this fungus rapidly adapt to selection pressures such as the extensive sowing of canola with particular disease resistance genes . This can lead to a breakdown of resistance and severe economic losses . We describe mutations in key fungal genes involved in the interaction with canola , and we report the first large scale study of evolutionary processes affecting such genes in any fungal plant pathogen . We relate these changes to the genomic environment of these genes and to the breakdown of disease resistance in canola .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"plant",
"biology/plant-biotic",
"interactions",
"infectious",
"diseases/fungal",
"infections",
"evolutionary",
"biology/microbial",
"evolution",
"and",
"genomics"
] |
2010
|
Evolution of Linked Avirulence Effectors in Leptosphaeria maculans Is Affected by Genomic Environment and Exposure to Resistance Genes in Host Plants
|
Several metastrongyloid lungworms are unreported pathogens in Colombia . Angiostrongylus vasorum and Crenosoma vulpis target the cardiopulmonary system of domestic and wild canids . Aelurostrongylus abstrusus and Troglostrongylus brevior infect felids and considering that six wild felid species exist in Colombia , knowledge of feline lungworm infections is important for their conservation . The zoonotic metastrongyloids Angiostrongylus costaricensis and Angiostrongylus cantonensis can cause severe gastrointestinal and neurological diseases . Angiostrongylus costaricensis has been reported in Colombia , while Ang . cantonensis is present in neighbouring countries . Research on the epidemiology of metastrongyloids in Colombia and South America more broadly requires evaluating the role that gastropods play as intermediate hosts in their life cycles . This study assessed the prevalence of metastrongyloid larvae in populations of the invasive giant African snail , Lissachatina fulica , in Colombia . A total of 609 Lissachantina fulica were collected from 6 Colombian municipalities . The snails were then cryo-euthanized , artificially digested and the sediments examined microscopically for the presence of metastrongyloid larvae . Based on morphological characteristics 53 . 3% ( 56/107 ) of the snails from Puerto Leguízamo ( Department of Putumayo ) were infected with Ael . abstrusus larvae , 8 . 4% ( 9/107 ) with Ang . vasorum larvae , 6 . 5% ( 7/107 ) with T . brevior larvae and 5 . 6% ( 6/107 ) with C . vulpis larvae , being the region with highest prevalences of the four species . Snails from Andes ( Department of Antioquia ) and Tulúa ( Department of Valle del Cauca ) were positive for Ang . vasorum larvae with a prevalence of 4 . 6 ( 11/238 ) and 6 . 3% ( 4/64 ) , respectively . Species identifications were confirmed by PCR and sequencing . This epidemiological survey reports for first time the presence of Ael . abstrusus , T . brevior , C . vulpis and Ang . vasorum in L . fulica in a number of regions of Colombia .
The giant African snail , Lissachatina ( = Achatina ) fulica , is originally native to east Africa [1] . It is now one of the most widely distributed and invasive snail species in tropical and subtropical terrestrial ecosystems , and consequently is included among 100 of the world’s worst invasive alien species [2] . As in other South American countries [3] , its presence in Colombia has been reported , in 27 of the 32 departments , with the departments of Meta , Valle del Cauca , Putumayo and Caquetá facing a critical ecological threat because of its presence [4] . The giant African snail is a highly invasive alien species in Colombia and is targeted by national campaigns or eradication [4] . Besides the ecological , agricultural and economic threats associated with this introduced snail , it acts as intermediate host of many metastrongyloid nematode species that can cause disease in animals and humans [5–6] . This invasive species constitutes an important intermediate host in the epidemiology of metastrongyloid parasites and contributes to their global dissemination [7–8] . Since 2000 approximately , parasites such as the canine cardio-pulmonary nematode Angiostrongylus vasorum and the feline lungworm Aelurostrongylus abstrusus have gained increased attention of the veterinary scientific community because of their detection in domestic and wild animals in many countries and their spread into previously non-endemic regions [5 , 9–12] . Symptoms of canine Ang . vasorum infections can vary from asymptomatic subclinical infections to cases exhibiting severe cardiopulmonary disorders and coagulopathies that can be fatal [13] . Cases have been reported in Europe , Africa and North and South America [5 , 9 , 11 , 14 , 15] . It is considered one of the most pathogenic cardiopulmonary nematodes in canids as well as other carnivores [10 , 16] . Several intermediate gastropod hosts have been reported [17–19] , including experimentally infected L . fulica [20] . Aelurostrongylus abstrusus is distributed worldwide and is one of the most important lung parasites in felids [21] . Clinical manifestations of feline aelurostrongylosis are typical of most other respiratory diseases [22] . In addition to domestic cats , Ael . abstrusus infections are also reported in several wild felid species that may serve as definitive hosts [23 , 24] . Various species of gastropods have been reported as intermediate hosts [19 , 25–28] . Troglostrongylus brevior is another feline-infecting lungworm that can cause signs ranging from subclinical to severe life-threatening conditions [29] . Its life cycle , symptoms and morphology are very similar to those of Ael . abstrusus , which has caused confusion in the past [30] . As far as we know , the only intermediate host species reported for T . brevior is Cornu aspersum by experimental infection [31] . There is little information on the biology , epidemiology , pathogenesis and immunology of T . brevior [32] . Crenosoma vulpis is another globally-distributed metastrongyloid , known as the fox lungworm , that invades the bronchi , bronchioles and trachea of wild and domestic canids [33] . Canine crenosomosis is generally characterized by bronchitis with a dry , unproductive cough [34] . However , high parasite burdens can be manifested as a chronic and productive cough [35] . This helminth is endemic in the European and North American red fox ( Vulpes vulpes ) populations [36 , 37] . In South America C . vulpis has been recently reported in Chile , where 1% ( 2/200 ) of dogs tested were positive for the parasite [38] . Various gastropod species have been reported to be involved in the life cycle of C . vulpis [19 , 39] . Lissachantina fulica is a known intermediate host of potentially life-threatening human metastrongyloid parasites , the zoonotic species Angiostrongylus cantonensis and Angiostrongylus costaricensis [40] . In Colombia , Ang . cantonensis has not been reported , while at least 10 human infections by Ang . costaricensis have been diagnosed [41] . Neural angiostrongylosis and abdominal angiostrongylosis are infrequently diagnosed because of the poor knowledge of clinicians resulting from the neglected status of these diseases . Nevertheless , in some cases these parasitoses are well tolerated and their subclinical presentation has been described [42 , 43] . The parasitic diseases discussed here are considered neglected and their prevalence underestimated in Colombia and South America in general , and therefore further research evaluating their epidemiological status in the region is urgently needed , particularly given the increasing spread of L . fulica . Thus , the aim of the present study was to determine the epidemiological status of metastrongyloid parasites in L . fulica populations from the Andean , Pacific and Amazonian Colombian biogeographic regions .
This study was approved on February 2 , 2016 by the ethics committee for animal experimentation of the University of Antioquia , Medellín , Colombia , order N° 101 . The giant African snail is not among the specially protected fauna regulated by the Act on Nature Conservation and Landscape Management of Colombia . In total , 609 L . fulica were collected between February and October of 2016 . Of these , 438 were from the Andean region municipalities of: Andes ( 5° 39′ 20″ N , 75° 52′ 49″ W ) ( n = 238 ) , Ciudad Bolívar ( 5° 50′ 58″ N , 76° 1′ 13″ W ) ( n = 100 ) , and Cañasgordas ( 6° 44′ 59″ N , 76° 1′ 33″ W ) ( n = 100 ) , located in the Department of Antioquia . Snails from the Pacific region were collected in the town of Tuluá , Department of Valle del Cauca ( 4° 5′ 5″ N , 76° 11′ 55″ W ) ( n = 64 ) , and the Amazonian region of Puerto Leguízamo , Department of Putumayo ( 0° 11′ 38″ S , 74° 46′ 50″ W ) ( n = 107 ) . The specific locations within the Colombian map are shown in Fig 1 . The snails were frozen and sent to the parasitology laboratory of the Veterinary Medicine School at the University of Antioquia in Medellín . The snails were morphologically identified , weighed , cut into small pieces and immersed in a digestion solution ( 10 g pepsin powder 2000 FIP-U/g , 8 . 5 g NaCl , 30 mL HCl 37% and distilled water to complete 1 L of solution ) overnight at 40°C in 50 mL Falcon tubes under constant shaking . Then , the samples were sieved through a 300 μm-metal sieve to remove any undigested material and further passed through a 25 μm-metal sieve . The remnants retained in the last sieving were transferred to 15 mL Falcon tubes and centrifuged at 400 g for 10 min . The pellets were re-suspended and examined microscopically with a light microscope at 4x , 20x and 40x magnification . Metastrongyloid larvae were counted and collected by pipetting . In cases of high larval burden ( more than 50 larvae per snail ) only 10% of the larvae were viewed at higher magnifications . Larval stages of metastrongyloids were identified by means of body measurement ( length/width ratio ) and the form ( non-rhabditiform ) and ratio of oesophagus to body lengths ( 1:3–1:2 ) in comparison to other nematodes [10] following Lange et al . 2018 [20] . To distinguish between different metastrongyloid larval stages , the distinct tail morphology of each genus was examined . For illustration of L1 , L2 and L3 of Ang . vasorum , Ael . abstrusus and Crenosoma sp . please see Lange et al . 2018 , Fig 2 [20] and regarding L1 see Fig 2 . The lungworm species were identified by typical morphometric characteristics [5 , 9 , 19 , 31 , 39 , 44–48] . One general feature of metastrongyloid larvae is the non-rhabditiform oesophagus , which forms 1 ⁄ 3–1 ⁄ 2 of the total larval length [9] . Angiostrongylus vasorum ( 310–400 μm × 14–16 μm ) is characterized by a small cup as a cephalic button which emerges on the oral extremity and its tail tip with a dorsal spine and sinus wave curve [5 , 36 , 43 , 44 , 48] . Aulerostrongylus abstrusus ( 300–415 μm × 18–19 μm ) has a slender anterior extremity with a short/terminal oral opening leading into a narrow vestibule and its tail is S-shaped with visible dorsal kink , distinct deep dorsal , ventral , incisures , a terminal knob-like extremity [5 , 19 , 35 , 45 , 48] . Troglostrongylus brevior ( 300–357 μm × 16–19 μm ) has a clear and pointed anterior extremity with a sub-terminal oral opening and its tail gradually tapered to dorsal incision , dividing the extremity into two appendices ( shallow ventral one , slender dorsal one ) S-shaped tail is not as obvious as in Ael . abstrusus , ending straight , gradually tapered [5 , 31 , 46 , 47] . Crenosoma vulpis ( 240–310 μm × 13 μm ) has a pointed and straight tail without indentations and entirely pointed [5 , 19 , 39] . The lengths of metastrongyloid larvae vary strongly depending on lungworm species , developmental stage and size of the respective intermediate host [9 , 44] . and were thus not considered as reliable inter-species differentiation feature . The previously described morphological tail characteristics allowed larvae identification to the genus level . Therefore , for these larvae , those of each genus from a single snail were pooled and underwent additional PCR analyses to identify them at species level . After digestion with proteinase K , DNA from the pooled larvae was isolated using the Qiagen DNeasy Blood and Tissue Kit according to the manufacturer’s protocol , with a final elution volume of 50 μL . To enhance the sensitivity of the molecular diagnosis , nested PCRs were performed . Following a conventional PCR with the universal nematode specific primers NC1/NC2 [49] specific real-time PCR analyses for individual species were performed as described in the literature [19] . Molecular confirmation was attained with the duplex-real-time PCR for Ael . abstrusus and T . brevior with melt analysis was carried out , amplifying the internal transcribed spacer 2 ( ITS-2 ) region from the ribosomal DNA ( rDNA ) of 220 bp ( Ael . abstrusus ) and 370 bp ( T . brevior ) . This PCR was conducted using the forward primers TrogloF , AeluroF and the single reverse primer MetR [50] . For confirm the infections by Ang . vasorum and C . vulpis a probe-based duplex-real-time PCR was performed amplifying a partial ITS-2 region as reported by Jefferies et al . 2011 [51] . To account for the inhibitory effects deriving from snail tissue [18 , 52] , samples were diluted 10 fold with sterile water and tested again with the above PCR conditions . In those cases in which the real-time PCRs were negative or inconclusive and the quantity of the amplicon-DNA from the first PCR was low we performed a second nested conventional PCR with primers NC1/MetR followed by direct sequencing or sequencing after cloning . In addition , we also sequenced some of the samples identified by real-time PCR for confirmation . Hence , 21 samples were purified and sent to a commercial sequencing service ( LGC Genomics , Berlin , Germany ) . The sequences obtained were verified by eye with the software Chromas Lite ( version 2 . 01 ) and the TCG microsatellite triplet repeat ( as part of the ITS-2 region ) and nucleotide polymorphisms were used to discriminate between the different reported genotypes by comparing sequences in GenBank via the BLAST algorithm ( http://www . ncbi . nlm . nih . gov/BLAST/ ) . The statistical analysis ( p-value and Pearson’s correlation coefficient ) were carried out using Free Statistics Software ( v1 . 2 . 1 ) , Office for Research Development and Education ( https://www . wessa . net/rwasp_correlation . wasp/ ) . The value was considerate significant if p < 0 . 05 .
In the 609 processed samples , metastrongyloid larvae were identified morphologically , and were found to belong to the following genera: Aelurostronglyus , Troglostrongylus , Crenosoma and Angiostrongylus ( Fig 2 ) . Molecular analyses with PCR allowed the identification of each lungworm species and overall prevalence in snails from each Colombian region is shown in Table 1 . The distinction between different Angiostrongylus species was difficult by means of microscopy . Several of the Angiostrongylus spp . positive samples contained larvae that resembled Ang . vasorum ( n = 24 ) and some showed characteristics of Ang . cantonensis ( n = 2 ) . However , larvae identified as probable A . cantonensis were not molecularly confirmed because DNA could not be amplified . For larvae of Ael . abstrusus , T . brevior , Ang . vasorum and C . vulpis morphological identification was confirmed by PCR and sequencing . Molecular biological analyses revealed total prevalences that varied with location and species ( Table 1 ) . Larval burden ranged from 1 to 314 larvae per snail for Ael . abstrusus and from 1 to 286 larvae per snail for T . brevior ( Figs 3 and 4; to see larval burden of each region see S1 Fig ) . Larval burden for C . vulpis ranged from 1 to 208 larvae per snail . No relationships between the larval burden and the snail weight were found ( Figs 3 and 4 ) . Co-infections involving two species were detected by means of microscopy and PCR in 19 . 1% ( 16/84 ) of all lungworm positive snails ( Fig 5 ) . Co-infections consisting of more than two species were not detected . From the 84 snails positive for metastrongyloid larvae by microscopy , 29 yielded sufficient DNA from larvae pools for sequence analysis , and 25 of those 29 were selected for identification or confirmation by sequencing . Specimens of Ael . abstrusus , Ang . vasorum , C . vulpis and T . brevior could be confirmed via sequencing with an identity of 99–100% to known GenBank entries ( Table 2 ) . Regarding samples positive for Ael . abstrusus in PCR , sequencing revealed genotype variation with similarities to European genotypes of Ael abstrsus of 99% ( genotype A ) , 94% ( genotype AB ) and 92% ( genotype B , Table 2 ) . Aelurostrongylus abstrusus genotype A isolates ( n = 11 ) contained a microsatellite sequence with 7–10 times TCG from ITS2 sequence . In contrast to genotype B ( n = 3 ) , which among other nucleotide variations , contained 4 TCG repeat units in the microsatellite and has never been reported before . Genotype AB showed an intermediate number of TCG repeats of 15 ( see S1 Table ) . In addition to these four species one sample contained a sequence for which no match was found in GenBank . It showed 82% similarity to European Ael . abstrusus isolates , thus probably belonging to the genus of Aelurostrongylus ( Table 2 ) .
To the best of our knowledge , this is the first large-scale survey confirming by molecular analysis the presence of Ael . abstrusus , Ang . vasorum , T . brevior and C . vulpis infections in intermediate hosts in Colombia . The records of T . brevior and C . vulpis represent the first report of these parasites in this country and the first confirmation via molecular techniques of these parasites in South America , demonstrating both 100% similarity to the European genotypes . Interestingly , a hotspot of Ael . abstrusus ( with high genetic variability ) and 1 potentially undescribed nematode which could belong to the genus Aelurostrongylus were here reported in the Amazonian region , specifically in Putumayo . On the basis of records of Ang . cantonensis in neighbouring countries and previous reports in Colombia of Ang . costaricensis , further research on L . Fulica as well as other natural populations of gastropods such as veronicellid slugs and Cornu aspersum should be undertaken in Colombia . The biology of invasive species in the region and their interactions with the native fauna requires more attention and investigation by the national authorities . Thus , more epidemiological and basic research on all these parasites in natural populations of paratenic hosts ( such as birds , amphibians , crabs , amongst others ) and intermediate hosts in other geographic areas is needed . In the same way data regarding prevalence in humans , domestic animals and wild definitive hosts are required in Colombia as well as in other countries of South , Central and North America to increase knowledge of the impact , dynamics , genetic variation and environmental factors associated with these neglected parasitoses .
|
Several lungworm species are neglected pathogens in Colombia . Angiostrongylus vasorum and Crenosoma vulpis target the cardiopulmonary system of domestic and wild canids . Aelurostrongylus abstrusus and Troglostrongylus brevior infect domestic cats as well as wild felids . Angiostrongylus costaricensis and Angiostrongylus cantonensis may cause severe gastrointestinal or neurological diseases in humans , respectively . Snails/slugs are necessary intermediate hosts in the life cycles of these parasites . We assessed the prevalence of metastrongyloid larvae in 609 specimens of the giant African snail , Lissachatina fulica , from 6 Colombian municipalities . In Puerto Leguízamo , 53 . 3% of the snails were infected with Ael . abstrusus larvae , 8 . 4% with Ang . vasorum larvae , 6 . 5% with T . brevior larvae and 5 . 6% with C . vulpis larvae . Snails from Andes and Tulúa were positive for Ang . vasorum larvae with a prevalence of 4 . 6 and 6 . 3% , respectively . This epidemiological study reports for first time the presence of Ael . abstrusus , T . brevior , C . vulpis and Ang . vasorum in the invasive giant African snail in various parts of Colombia .
|
[
"Abstract",
"Introduction",
"Material",
"and",
"methods",
"Results",
"Discussion"
] |
[] |
2019
|
The invasive giant African snail Lissachatina fulica as natural intermediate host of Aelurostrongylus abstrusus, Angiostrongylus vasorum, Troglostrongylus brevior, and Crenosoma vulpis in Colombia
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The contribution of different host cell transport systems in the intercellular movement of turnip mosaic virus ( TuMV ) was investigated . To discriminate between primary infections and secondary infections associated with the virus intercellular movement , a gene cassette expressing GFP-HDEL was inserted adjacent to a TuMV infectious cassette expressing 6K2:mCherry , both within the T-DNA borders of the binary vector pCambia . In this system , both gene cassettes were delivered to the same cell by a single binary vector and primary infection foci emitted green and red fluorescence while secondarily infected cells emitted only red fluorescence . Intercellular movement was measured at 72 hours post infiltration and was estimated to proceed at an average rate of one cell being infected every three hours over an observation period of 17 hours . To determine if the secretory pathway were important for TuMV intercellular movement , chemical and protein inhibitors that blocked both early and late secretory pathways were used . Treatment with Brefeldin A or Concanamycin A or expression of ARF1 or RAB-E1d dominant negative mutants , all of which inhibit pre- or post-Golgi transport , reduced intercellular movement by the virus . These treatments , however , did not inhibit virus replication in primary infected cells . Pharmacological interference assays using Tyrphostin A23 or Wortmannin showed that endocytosis was not important for TuMV intercellular movement . Lack of co-localization by endocytosed FM4-64 and Ara7 ( AtRabF2b ) with TuMV-induced 6K2-tagged vesicles further supported this conclusion . Microfilament depolymerizing drugs and silencing expression of myosin XI-2 gene , but not myosin VIII genes , also inhibited TuMV intercellular movement . Expression of dominant negative myosin mutants confirmed the role played by myosin XI-2 as well as by myosin XI-K in TuMV intercellular movement . Using this dual gene cassette expression system and transport inhibitors , components of the secretory and actomyosin machinery were shown to be important for TuMV intercellular spread .
Plant viruses move from the initially infected cell to neighboring cells during local spread and then over long distances through vascular tissues to establish a systemic infection in the plant . Transport of viruses between cells first involves the intracellular movement of the viral RNA from the site of replication to plasmodesmata ( PDs ) and then its delivery into neighboring cells through PDs . PDs are tunnels in the cell wall that connect the cytoplasm , the endoplasmic reticulum ( ER ) and the plasma membrane between adjoining cells ( reviewed in [1] ) . The size exclusion limit ( SEL ) of PD is normally too small to allow passive transport of large molecular complexes , but plant viruses encode movement proteins ( MPs ) that increase the SEL of PDs to allow passage of the viral RNA ( reviewed in [2] , [3] ) . Intracellular movement likely involves a membrane-associated viral RNA-host and viral protein complex , but the exact configuration of the viral entity that enters the neighboring cells has not yet been determined ( reviewed in [2] , [4] ) . In the case of tobacco mosaic virus ( TMV ) , the viral RNA appears to spread between cells as membrane MP-associated viral replication complexes ( VRCs ) [5] . For members of the comovirus and caulimovirus genera , viral particles transit through MP-induced tubules that go through PDs for their delivery into non-infected cells [6]–[10] . Although MPs and other viral protein components are important for viral RNA intra- and intercellular movement , it is clear that host factors also are required for these activities . The cytoskeleton is an essential component of organelle trafficking in plant cells ( reviewed in [11] , [12] ) and it has been shown to be involved in vertebrate virus intracellular movement ( reviewed in [13] ) . In the case of TMV , several studies have shown that microtubules and microfilaments are necessary to anchor and release , or aid the movement of the VRC or MP granules often associated with ER ( reviewed in [2] , [4] , [14] ) . Microfilaments influence the intracellular or intercellular transport of other MPs or viruses [15]–[20] . Myosin motors are also required for MP or viral trafficking [6] , [21]–[24] . The secretory pathway is further involved in intra- and intercellular trafficking by several viruses [16] , [17] , [20] , [21] , [25] , [26] . Finally , recent studies suggest that the endocytic transport pathway may be involved in viral movement [27] , [28] . However , not all viruses or their components use the cytoskeleton or the secretory pathway for movement . For example , PD targeting of the tubule-forming MP of cowpea mosaic virus ( CPMV ) is not affected by either the disruption of ER-Golgi transport or by cytoskeleton disruption [29] . Similarly , the targeting of the triple gene block protein 3 ( TGBp3 ) of poa semilatent virus to PD does not require a functional cytoskeleton or the secretory pathway for its intracellular transport [30] . The genome of potyviruses is a single ∼10 kb RNA molecule that codes for a polyprotein , which is processed into ten mature proteins . In addition to polyprotein-derived polypeptides , an ∼7 kDa protein termed PIPO is produced in infected cells [31] and is also found as a trans-frame protein consisting of the amino-terminal half of P3 fused to PIPO ( P3N-PIPO ) [32] . Potyviruses have no designated MP but many viral proteins have been reported to have MP-related functions . For instance , HCPro and the coat protein ( CP ) can increase PD SEL [33] . In addition , CP and cylindrical inclusion ( CI ) protein are required for virus intercellular movement [34]–[36] and are associated with PD [37] , [38] . Recently , the targeting of CI to PD was shown to be mediated by P3N-PIPO [39] , which itself is targeted to the plasma membrane through an interaction with the host protein PCaP1 [32] . One last protein involved in viral movement is the 6 kDa membrane-associated 6K2 protein . It induces the production of motile vesicles that contain viral RNA and have been proposed to be the vehicle for intracellular trafficking of potyviral RNA [40] , [41] . These findings , while providing a partial understanding of the mechanism of TuMV intracellular and intercellular movement , do not sufficiently explain the involvement of the host secretory pathway or the cytoskeleton in potyvirus movement . In this study , we used a novel dual gene cassette construct that differentiated primary infected cells from cells infected after virus intercellular movement to show that the early as well as the late secretory pathway , but not endocytosis , was important for TuMV transport . We also determined that myosin XI-2 and XI-K , but not myosin XI-F and VIIIs , influenced TuMV intercellular movement . Although these cellular components were required for intercellular movement of TuMV , they did not appear to be involved in the virus protein production in primary infected cells .
A recombinant tobacco etch virus ( TEV ) ( genus Potyvirus ) engineered to express the reporter protein β-glucuronidase ( GUS ) allowed direct observation of virus spread in leaves [34] . Virus spread is influenced by the rates of virus RNA replication and virus intercellular movement . Hence , the use of the above TEV-GUS construct to fully interpret results from virus spread studies is limited since virus replication in live tissue cannot be quantitated through GUS staining . In order to discriminate initially infected cells from later infected cells in live tissue , we introduced within the T-DNA borders of a binary vector a gene cassette expressing the ER-localized GFP-HDEL adjacent to a TuMV infectious genome cassette expressing 6K2:mCherry ( Fig . 1A ) . Since both gene cassettes are delivered to the same cells and GFP-HDEL does not move between cells [28] , primary infected cells should display concomitant green and red fluorescence while secondary infected cells should display red-only fluorescence . This system also allows differentiation between virus RNA replication and virus intercellular movement . A single infiltration with an A . tumefaciens suspension containing the above plasmid was performed on leaves of three-week old N . benthamiana plants , resulting in an agroinfiltrated area of 5–10 mm in diameter ( Fig . 1B ) . Fluorescence emitted by GFP-HDEL was generally observed at approximately 36 hrs post infiltration ( hpinf ) and mCherry fluorescence resulting from TuMV replication was detected at approximately 60 hpinf . Systemic TuMV infection was observed at 4–5 days post infiltration ( dpinf ) in leaves above the infiltrated one by Western blot analysis using a rabbit serum against the CP of TuMV ( data not shown ) . A similar systemic movement was obtained when more dilute agrobacterium suspensions ( e . g . 0 . 01–0 . 001 ) were infiltrated , indicating the bacterial load did not elicit a plant defense response that might have affected virus infection rate . N . benthamiana cells displayed the expected green polygonal ER pattern and virus-induced 6K2-tagged red vesicles ( Fig . 1C ) . Virus intercellular movement was assayed by observing red and green fluorescence at the perimeter of the infiltrated area . At 72 hpinf , a majority of cells in the infiltrated area emitted both green and red fluorescence and just a few red-only cells were observed ( Fig . 1D ) , indicating that viral movement was just starting . Viral movement was followed in the same agroinfiltrated region at 4 , 5 , and 6 dpinf ( Fig . 1E–G ) . At the end of the observation period , the surface area of green fluorescence did not changed , indicating that GFP-HDEL did not moved into neighboring cells . Intercellular movement of fluorescent signal was also followed by observing spread of green and red fluorescence for 17 consecutive hours starting at 72 hpinf in order to evaluate the rate of cell-to-cell movement . This was achieved by securing an infiltrated leaf still attached to the plant on the confocal microscope stage for observation . When the perimeter of the agroinfiltrated area was initially imaged , intercellular movement have already begun as indicated by the presence of red-only fluorescent patches ( Movie S1 ) . To calculate the rate of cell-to-cell movement in cells/hour , we first measured the number of leaf epidermal cells for every linear 1 mm in three-week-old N . benthamiana plants and we calculated that 1 mm corresponded to 17 . 6 cells ( n = 20 ) . We then measured the distance from the agroinfiltrated front to the limit of expansion of the red fluorescence at the end of the observation period . In this experiment , the red-only fluorescence , indicative of TuMV secondary infection , had spread an average distance of 311 µm ( n = 20 ) in the xy plane in 17 h . This expansion corresponded to a rate of one new infected cell every 3 hours . We repeated this experiment five times and we observed the same rate of cell-to-cell movement . This rate of intercellular movement was similar to that observed for TEV expressing GUS and for TMV [5] , [34] . The increase in red fluorescence surface area was not due to the diffusion of 6K2:mCherry from agroinfected cells in the absence of virus spread because replacement of the TuMV cassette with a cassette expressing only 6K2-mCherry did not produce red-only fluorescent foci ( Fig . S1A ) . These findings validated the use of the double cassette , GFP-HDEL and TuMV 6K2:mCherry to follow intercellular movement by TuMV . Chemical and protein inhibitors were used to evaluate the role of the early and late secretory pathways in the intercellular movement of TuMV using the dual gene cassette system . The plant secretory pathway consists of the ER , the Golgi apparatus , various post-Golgi intermediate compartments ( e . g . trans-Golgi network , endosomes ) , the vacuoles/lysosomes and the small vesicular transport carriers that shuttle between these compartments . The early secretory pathway embraces the ER–Golgi interface while the Golgi apparatus and the various post-Golgi organelles that control plasma membrane or vacuolar sorting is categorized as the late secretory pathway ( reviewed in [42] ) . Brefeldin A ( BFA ) is an inhibitor that interferes with protein transport between the ER-Golgi interface [43] . Concanamycin A ( CMA ) inhibits protein transport at the trans-Golgi network ( TGN ) [44] by inhibiting the function of TGN-localized proton-ATPases , which leads to the acidification of the TGN lumen [45] . To evaluate the influence of these secretory inhibitors on TuMV intercellular movement , N . benthamiana leaves were treated with DMSO , 10 µg/mL BFA or 0 . 5 µM CMA 4 h before pCambiaTuMV/6K2:mCherry//GFP-HDEL agroinfiltration . BFA at this concentration effectively blocked the secretory pathway ( Fig . S1B ) . At 4 dpinf , DMSO alone had no inhibitory effect on TuMV movement ( Fig . 2A ) . On the other hand , BFA and CMA treatment reduced cell-to-cell movement of TuMV ( Fig . 2B–C ) . The surface area for mCherry-only expressing foci for each treatment was measured and the statistical analysis confirmed the inhibitory effect of BFA and CMA on TuMV intercellular movement ( Fig . 2D ) . This experiment was repeated two more times and the statistical data are presented in Fig . S2A . To assess whether or not BFA or CMA inhibited TuMV replication , we quantified mCherry fluorescence intensity over GFP fluorescence intensity in primary infection foci for all treatments . Fig . 2E shows that that there was no significant difference in the ratio of red over green fluorescence during BFA and CMA treatments compared with the no inhibitor treatment ( TuMV alone ) at 4 dpinf , indicating that viral protein production in the primary infected cells was not affected by the drug treatments . Green fluorescence levels were also similar between DMSO- and BFA- or CMA-treated primary cells indicating that steady state level of GFP , which was not associated with virus replication , was not affected by the treatments ( data not shown ) . Protein inhibitors were used to further support the role of the secretory pathway in TuMV intercellular transport . The ADP-ribosylation factor 1 ( ARF1 ) is a small GTPase regulating the recruitment of COPI coatomer proteins . A dominant negative mutant of ARF1 [ARF1 ( NI ) ] impaired in GTP/GDP binding inhibits the transport of soluble markers from the ER to Golgi , and causes a re-absorbance of Golgi membrane proteins into the ER [46] . RAB-E1d is a small Rab GTPase acting at a post-Golgi trafficking pathway and the dominant negative mutant RAB-E1d ( NI ) inhibits trafficking from the Golgi apparatus to the plasma membrane [47] . These two dominant-negative mutants were co-infiltrated with pCambiaTuMV/6K2:mCherry//GFP-HDEL . Four days post-agroinfiltration , red-only foci were reduced in the tissue co-infiltrated with the two mutant protein cassettes compared with those not infiltrated with these constructs , indicating reduced intercellular movement of TuMV in the presence of these secretory pathway inhibitors ( Fig . 3B–C ) . Surface area measurements for mCherry-only expressing patches confirmed the inhibitory effect of both ARF1 ( NI ) and RAB-E1d ( NI ) on TuMV intercellular movement ( Fig . 3D ) . This experiment was repeated two more times and the statistical data are presented in Fig . S2B . Expression of these two mutant proteins did not hamper virus protein production in primary infected cells as measured by red over green fluorescence ratios in the dual expressing regions of the infected leaves ( Fig . 3E ) . We therefore concluded that inhibition of both early and late secretory pathways inhibited TuMV intercellular movement but did not affect virus replication in primary infected cells . The last assertion is in line with the prior observation that BFA treatment did not affect the production of TuMV-induced 6K2-tagged perinuclear structures and peripheral vesicles [48] . We examined if endocytosis was involved in TuMV intercellular movement . To this end , we first used a pharmacological interference assay with Tyrphostin A23 , Tyrphostin A51 and Wortmannin . In mammalian cells , Tyrphostin A23 inhibits the recruitment of endocytic cargo into clathrin-coated vesicles formed at the plasma membrane by preventing the interaction between the clathrin-binding AP-2 adaptor complex μ2 subunit and the sorting motif within the cytoplasmic domain of plasma membrane proteins [49] . Tyrphostin A51 is a structural analog of Tyrphostin A23 but has no inhibitory effect and is routinely used as negative control [49] . Tyrphostin A23 is active in plant cells [50] and it has been shown that the drug inhibits endocytosis of some plasma membrane proteins [51] . Wortmannin is a phosphatidylinositol 3-kinase inhibitor that inhibits in mammalian cells receptor sorting and/or vesicle budding required for delivery of endocytosed material to “mixing” endosomes [52] . In plant cells , it has been shown that the drug inhibits endocytosis of FM4-64 ( an amphiphilic styryl dye used to monitor endocytosis ) [53] and morphogenesis of MVB/PVCs [50] , [54] but it does not affect protein transport from the trans-Golgi network ( TGN ) to the plasma membrane [55] , [56] . N . benthamiana leaves were infiltrated either with Tyrphostin A23 , Tyrphostin A51 , Wortmannin or DMSO 4 hrs prior to agroinfiltration with A . tumefaciens Agl1 containing pCambiaTuMV/6K2:mCherry//GFP-HDEL . The drug concentrations used previously were shown to block endocytotic pathway in plants [50] , [54] and inhibition of endocytosis of FM4-64 in the presence of Wortmannin was confirmed in our system ( Fig . S1C ) . Four days post agroinfiltration , TuMV movement was examined ( Fig . 4B–E ) . TuMV intercellular spread was not significantly inhibited in the presence of the different drugs that modify cellular endocytotic activity . This experiment was repeated two more times and data are presented in Fig . S2C . Thus maintenance of TuMV intercellular movement does not require endocytic activity within the four day period of observation . We also investigated whether TuMV-induced 6K2-tagged vesicles were associated with Ara7 ( AtRAB-F2b ) and internalized FM4-64 . Intracellular trafficking of 6K2-tagged vesicles , which contain viral RNA [40] , [41] , has been shown to be dependent on the secretory pathway and microfilaments [40] , [48] , [57] . Ara7 is a plant Rab protein similar to Rab5 of mammals and to Ypt51/Ypt52/Ypt53 of yeast , and is associated with prevacuolar compartments and involved in endocytic and vacuolar trafficking in plant cells [58] , [59] . Co-expression of Ara7 fused to GFP and pCambiaTuMV/6K2:mCherry in N . benthamiana leaves cells showed that there was no colocalization between Ara7 motile dots and 6K2-tagged vesicles ( Fig . 5A ) . Also , 6K2-tagged vesicles were never associated with FM4-64-labeled vesicles ( Fig . 5B ) . Lack of co-localization of FM4-64 and Ara7 with TuMV-induced 6K2-tagged vesicles further indicates that endocytic pathways associated with these markers were not important for TuMV cellular spread . Many viruses and individual virus proteins require the actomyosin system for their intracellular and/or intercellular movement [15] , [16] , [18]–[20] , [22] , [60] . However , recent studies showed that RNA viruses might have evolved differently in their requirements for actin and the associated myosin motors [22] , [61] . We first tested the effect of Latrunculin B ( LatB ) , and Cytochalasin D ( CytD ) , which inhibit maintenance of microfilaments [62] , on the intercellular spread of TuMV . Leaves were infiltrated with 5 µM LatB , 10 µM CytD , or DMSO 4 h before agroinfiltration with pCambiaTuMV/6K2:mCherry//GFP-HDEL . The disruption of actin by LatB or CytD was confirmed by confocal microscopy observation of microfilaments labeled with the actin-binding domain 2 of A . thaliana fimbrin fused to GFP ( GFP-ABD2-GFP ) [63] ( Fig . S1D ) . TuMV intercellular movement was assessed by imaging N . benthamiana leaves at 4 dpinf . Inhibition of TuMV intercellular movement was observed after LatB or CytD treatment ( Fig . 6B–D ) . This experiment was repeated two more times and the statistical data are presented in Fig . S3A . The ratio of red to green fluorescence in dual expressing cells was unchanged between treatments indicating that virus replication was unaffected by these microfilament antagonists ( Fig . 6E ) . These results indicate that an intact microfilament network was important for TuMV intercellular movement , but not for replication . The last assertion is in line with the prior observation that LatB treatment did not affect the production of TuMV-induced 6K2-tagged perinuclear structures and peripheral vesicles [40] . It was previously shown that overexpression of the myosin XI-K tail , a dominant negative mutant of this myosin species , inhibited the intracellular trafficking of TuMV 6K2 vesicles and reduced TuMV infection [41] , indicating the involvement of this myosin in virus movement . We were also interested to see if other myosins could be involved . Tobacco rattle virus-mediated virus-induced silencing ( TRV-VIGS ) was adopted to determine the role of myosins on intercellular movement of TMV , potato virus X ( PVX ) , tomato bushy stunt virus , and turnip vein-clearing virus ( TVCV ) [22] . We thus used TRV-VIGS to silence individual myosin genes prior to TuMV infection . N . benthamiana leaves were first infected with TRV constructs and 15 days later upper leaves were infiltrated with agrobacterium strain containing pCambiaTuMV/6K2:mCherry//GFP-HDEL . TuMV intercellular movement was quantified at 4 dpinf . Quantitative RT-PCR confirmed that the transcriptional level of the target myosin genes was decreased in plants infected by the TRV silencing construct containing the corresponding genes ( Fig . 7A ) . We then monitored TuMV intercellular movement by measuring areas of foci expressing mCherry-only . Quantification indicated that there was no significant difference in TuMV intercellular movement in mock- and TRV-infected plants ( Fig . 7B and 7D ) . Virus movement was not affected in myosin VIII-1 and VIII-2-silenced plants . However , silencing of myosin XI-2 reduced TuMV intercellular movement by a factor of 10 compared to the control ( Fig . 7B and 7D ) . Reduced TuMV movement was observed in myosin XI-F silenced plants , but was not found to be statistically significant . This experiment was repeated two more times and the data are presented in Fig . S3B . To be sure this effect on virus movement was specific to myosin XI-2 silencing , we analyzed the effect of silencing myosin XI-2 on the other myosins ( Fig . 7C ) . Silencing myosin XI-2 had no significant effect on the transcript level of the other tested myosins . Studying the role of different myosins in virus movement has also been carried out with transient expression of dominant negative myosin mutants [6] , [23] . A significant decrease of movement was observed when we expressed N . benthamiana dominant negative myosin mutants for the myosin XI-2 and XI-K , but no significant effect was observed for myosin VIII-1 and XI-F ( Fig . 8A and C ) . This experiment was repeated two more times and the data are presented in Fig . S3C . Intensity ratio of red over green foci was calculated to determine if replication was affected by the expression of the dominant-negative myosin mutants . The replication was not affected by any dominant-negative myosin mutants assessed in this experiment ( Fig . 8B ) . Results presented here indicate that myosin XI-2 and XI-K are required for intercellular movement of TuMV and that the other myosins analyzed did not appear to play a role in cell-to-cell movement of TuMV .
Studies on intercellular movement have shown that plant viruses may use different trafficking pathways to move from one cell to another ( reviewed in [2] , [4] ) . In this study , by discriminating infiltrated and primary-infected cells from cells infected following intercellular virus movement , we were able to evaluate the contribution of the secretory pathway and the cytoskeleton for TuMV intercellular movement . Using our dual gene cassette construct ( Fig . 1 ) , we first observed the green fluorescence at 48 hpinf , and mCherry fluorescence at 60 hpinf . This is the time frame normally observed when virus infection is initiated through agroinfiltration [64]–[66] . TuMV intercellular movement was observed between 60 and 72 hpinf and progressed at a rate of one new infected cell per ∼3 hours and systemic infection of the plant occurred at 4–5 dpinf . The intercellular rate of spread of TuMV was very close to that observed for TMV and TEV [5] , [34] . Infections caused by mechanical inoculation with virus suspensions as opposed to agroinfiltration often result in measurable virus replication at 24 hpinf and systemic infection 2 days later [34] . The delay in observable infection in our agroinfiltration system may be explained by several factors . The first factor is that a T-DNA copy of the viral RNA genome is delivered in the cell . This T-DNA molecule must be transported to the nucleus and transcribed into RNA , which is then transported back in the cytoplasm . In the case of potyviruses , there may be an additional delay because the RNA transcribed from the T-DNA is not linked to VPg . There is consequently a first round of translation that needs to take place before bona fide infection begins . Lastly , monitoring fluorescence as opposed to viral RNA through an amplification system ( e . g . RT-PCR ) is likely less sensitive and requires maturation of the fluorescent marker [67] . Importantly , however , after the initial delay in infection , TuMV intercellular movement and systemic infection proceeded at the same rate as infections using purified viral particles . This indicates that the infiltrated agrobacterium did not cause an additional defense response by the plant that significantly impeded spread of TuMV beyond what is normally observed during virus infections . This latter finding further supports the use of our dual cassette construct as a valid tool to study virus intercellular movement . It was previously shown that intracellular motility of individual potyviral proteins was dependent on the early secretory pathway [17] , [39] , [41] , [57] . In addition to ER , COPI , and COPII coatomers , the Golgi apparatus can be recruited into virus factories [16] , [48] , [68] but the role of late secretory pathway in plant viral movement was not investigated . Although ESCRT ( endosomal sorting complexes required for transport ) proteins , which have a major role in the sorting of cargo proteins , are recruited for tomato bushy stunt virus replication [69] , [70] , it is not known if they have any involvement in virus movement . In the present study , we determined that in addition to the early secretory pathway , post-Golgi transport components were also required for TuMV intercellular movement ( Fig . 2–3 ) . Likely , this late secretory pathway is required for sorting the membrane-associated viral RNA-protein complex to PDs . Intercellular movement of TuMV also depended on microfilaments ( Fig . 6 ) and myosin motor proteins ( Fig . 7 and 8 ) . In plant cells , myosins are classified into class VIII or class XI [71] . Among the myosins tested in this study , myosin XI-2 and XI-K were required for TuMV intercellular movement , but not myosin XI-F , VIII-1 or VIII-2 [39] . Class XI myosins are also required for normal sustained movement of TMV [22] and GFLV [6] . In the case of GFLV , it may be that myosin is required to transport a host factor to the PD that then supports GFLV movement . For TMV , it has been suggested that the influence of myosin XI-2 on its sustained intercellular spread may be through metabolism of virus products after virus movement , since the related tobamovirus TVCV does not require actomyosin for intercellular spread and TMV spreads normally for 24 h post treatment with a microfilament antagonist [22] , [61] , [72] . Previously , myosin XI-K was shown to be involved in the intracellular movement of TuMV 6K2 vesicles [41] . Myosin XI-K and myosin XI-2 , but not other myosins , have been shown to be major facilitators for cellular motility between actin filaments and the ER [73] . It has also been shown that myosin XI-K localizes to the motile endomembrane vesicles associated with F-actin [71] . We suggest that there may be more than one myosin-dependent activity necessary for a single virus and its expressed proteins to spread in plants and that XI-2 , together with XI-K , may be important for the movement of TuMV RNA complexes . Disruption of the secretory pathway had no impact on TuMV accumulation in the initially infected cells . We showed previously that BFA treatment had no effect on the formation of TuMV-induced 6K2-tagged structures , although motile 6K2 vesicles showed a higher incidence of localization with the COPII marker Sec24 [48] . Similarly , disruption of the early secretory trafficking by BFA inhibited intercellular virus movement of melon necrotic spot virus but did not modify its accumulation in infected cells [16] . Coronavirus-induced remodeling of the ER and viral replication equally took place in the presence of BFA [74] . Breakdown of actin filaments also did not affect the formation of TuMV 6K2-tagged vesicles [40] . These results suggest that replication activities , despite their requirement for membranes , are influenced separately from those involved in movement , although aspects of both are likely coordinated [75] . In conclusion , we show in this study that the secretory pathway and the actomyosin system are both important for the intercellular movement of TuMV . These host components are likely required by the virus to aid its movement out of the initially infected cell . Further work is necessary to identify host proteins within the secretory pathway and the actomyosin network that interact with the virus proteins and influence virus movement .
TuMV infectious clone pCambiaTuMV/6K2:mCherry was as described [40] , [76] . Ara7/RabF2b was as described [59] . The binary vectors designed to express N . benthamiana myosin tails VIII-1 , XI-K , XI-F , and XI-2 were as described [23] . The introduction of the 35S-GFP-HDEL gene cassette into pCambiaTuMV/6K2:mCherry was done as follows: pBIN/20-ER-gk [77] was digested with AseI and ligated with similarly digested pCambiaTuMV/6K2:mCherry . Kanamycin-resistant Escherichia coli colonies were screened for pCambiaTuMV/6K2:mCherry//GFP-HDEL . Transient expression studies were performed by agroinfiltration on three-week-old N . benthamiana plants . Plasmids were introduced by electroporation into Agrobacterium tumefaciens AGL1 and selected on LB ampicillin-kanamycin plates . The pellet of an overnight culture was gently suspended in water supplemented with 10 mM MgCl2 and 150 µM acetosyringone and left at room temperature for 3 h . The solution was then diluted to an OD600 of 0 . 6 for pCambiaTuMV/6K2:mCherry//GFP-HDEL; 0 . 1 for pARF1 ( NI ) , pRAB-E1d ( NI ) and pYFP-RAB-F2b; 1 . 5 for pTRV1 and pTRV2; 0 . 3–0 . 5 for myosin dominant negative mutant . For co-expression , 1∶1 mixture of the two AGL1 bacteria containing the plasmids of interest were agroinfiltrated . All dominant negative mutants were agroinfiltrated 24 h before pCambiaTuMV/6K2:mCherry//GFP-HDEL agroinfiltration . Plants were kept for 3–4 dpinf in a growth chamber until observation . Small pieces of N . benthamiana leaves were cut and dipped in 1 µg/µl of FM4-64 ( Molecular Probes ) . Leaves were incubated at room temperature for 40–45 minutes and observed by confocal laser microscopy . Stock solutions of Latrunculin B ( LatB: 2 . 5 mM Calbiochem ) and Cytochalasin D ( CytD; 20 mM Calbiochem ) were prepared in dimethyl sulfoxide ( DMSO ) and diluted to the desired concentration in water prior to their infiltration into 3-week-old N . benthamiana leaves . The final concentration of Brefeldin A ( BFA ) , CMA , Tyrphostin A23 , Tyrphostin A51 and Wortmannin were 10 µg/ml , 0 . 5 µM , 30 µM , 30 µM , and 20 µM , respectively . The maximal surface of N . benthamiana leaves were agroinfiltrated with the inhibitors 4 hours before pCambiaTuMV/6K2:mCherry//GFP-HDEL agroinfiltration . pCambiaTuMV/6K2:mCherry//GFP-HDEL agroinfiltration was restricted to a small region in the leaf , to be sure that this region received the inhibitors treatment , and in order to be able to follow the cell to cell movement . pTRV2 with myosin fragments was as described [22] . Virus-induced gene silencing ( VIGS ) studies were conducted as described previously [78] , [79] . vTRV infections were established in N . benthamiana by co-agroinfiltration of pTRV1 and pTRV2 . To confirm silencing of specific myosin transcripts , RNA was isolated from 20 day-old TRV-infected systemic leaves ( two plants/construct ) using the RNeasy plant mini kit ( Qiagen ) . DNase-treated RNA ( 4 µg ) was used to generate cDNA with iScript cDNA synthesis kit ( Bio-Rad ) . After a 15-fold dilution of the cDNA , 2 µL of solution was used for quantitative RT-PCR through a Rotor Gene 3000 real-time DNA detection system ( Corbett Research ) . The following primers were used to detect N . benthamiana myosins: VIII-1: 5′-GCCCGAGAGAGCAATGGA-3′and 5′-CCTCAGCTAATCGGCTTATAACACT-3′; VIII-2: 5′-ACTCCTATTGAATTTGCCAGCAA-3′ and 5′-CTGCACATAAACTGCCATTATTCC-3′; XI-2: 5′-CAACTCCTACCCGCAAACCA-3′ and 5′-TCCCATTGTCATTCTCCCAAA-3′; XI-F: 5′-GCACAGGGTTTTCGCTCAA-3′ and 5′-CCCTCAATTCCGCTGTATCC-3′ . Transcript levels were adjusted for loading differences after comparison with Actin-Binding Domain 2 ( ABD2 ) transcript internal control values and were calculated using the Delta-Delta CT method . N . benthamiana leaves above the original TRV-inoculated leaf were agroinfiltrated with pCambiaTuMV/6K2mCherry//GFP-HDEL 16 days after TRV infection . The leaves were observed 4 days later by confocal microscopy . Agroinfiltrated leaf sections were mounted on a depression microscope slide , aligning the leaf tissue in the well . Cells were observed using a 10× objective , 20× , 40× and 63× oil immersion objective on a LSM 510 Metaconfocal microscope ( Zeiss ) or on a LSM 780 Metaconfocal microscope ( Zeiss ) . For LSM 510 Metaconfocal microscope experiments , argon and HeNe lasers were used to excite fluorescent proteins and for a LSM 780 Metaconfocal multiline argon and DPSS 561 were used . Data from both green and red channels were collected at the same time . After acquisition , images were processed using Metamorph and/or ImageJ to quantify the average intensity of fluorescence , Carl Zeiss LSM Image Browser , and/or Adobe Photoshop software for post-capture imaging processes . Statistical analysis was performed from a total of 10–21 patches from 21 leaves and 5 different plants . Graphpad Prism One-way analysis of variance ( 1 way ANOVA ) was used to assess the overall statistical differences between the means of different groups . Following 1 way ANOVA , Tukey's Multiple Comparison Test was also used to assess whether the mean of two particular groups were different from each other . P value summary ( P<0 . 05 ) shows statistically significant differences between different treatments .
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Plant viruses move from the initially infected cell to neighboring cells during local movement and then over long distances through vascular tissue to establish a systemic infection in the plant . Virus intercellular transport requires viral and host factors to move viral RNA-protein complexes through plasmodesmata ( PDs ) . Virus intercellular movement is normally assessed by assays that cannot always differentiate between reduced viral RNA replication and intercellular movement . By using a dual cassette of genes encoding fluorescent proteins that can differentiate between primary infected cells and cells infected after intercellular transport , we provide evidence that turnip mosaic virus ( TuMV ) needs a functional secretory pathway where pre- and post-Golgi trafficking and the actomyosin network are important for its movement . Interestingly , disruption of these host transport machineries had no impact on TuMV accumulation in initially infected cells . These results support the idea that virus replication activities can be influenced separately from those involved in other virus activities such as movement , although aspects of both are likely coordinated .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
|
Contribution of Host Intracellular Transport Machineries to Intercellular Movement of Turnip Mosaic Virus
|
Exaggerated traits involved in species interactions have long captivated the imagination of evolutionary biologists and inspired the durable metaphor of the coevolutionary arms race . Despite decades of research , however , we have only a handful of examples where reciprocal coevolutionary change has been rigorously established as the cause of trait exaggeration . Support for a coevolutionary mechanism remains elusive because we lack generally applicable tools for quantifying the intensity of coevolutionary selection . Here we develop an approximate Bayesian computation ( ABC ) approach for estimating the intensity of coevolutionary selection using population mean phenotypes of traits mediating interspecific interactions . Our approach relaxes important assumptions of a previous maximum likelihood approach by allowing gene flow among populations , variable abiotic environments , and strong coevolutionary selection . Using simulated data , we show that our ABC method accurately infers the strength of coevolutionary selection if reliable estimates are available for key background parameters and ten or more populations are sampled . Applying our approach to the putative arms race between the plant Camellia japonica and its seed predatory weevil , Curculio camelliae , provides support for a coevolutionary hypothesis but fails to preclude the possibility of unilateral evolution . Comparing independently estimated selection gradients acting on Camellia pericarp thickness with values simulated by our model reveals a correlation between predicted and observed selection gradients of 0 . 941 . The strong agreement between predicted and observed selection gradients validates our method .
Few metaphors have captured the interest of evolutionary biologists and ecologists more than the coevolutionary arms race [1] . Whether between species , sexes , individuals , or genes , the idea of perpetually and reciprocally escalating defenses and counter-defenses has inspired an enormous amount of research [e . g . , 2 , 3–21] . As a result , we now have convincing evidence that arms races occur both within and between species , at least in some well-studied cases . What we know with much less certainty , however , is just how reciprocal , common , and intense evolutionary arms races tend to be across the diversity of life as a whole . Our overall understanding of evolutionary arms races is limited by existing approaches that are labor intensive and that generally yield qualitative rather than quantitative estimates for the strength of reciprocal selection . For instance , studies exploring arms races at the level of genes often rely on classical population genetic tools that identify signatures of positive selection using ratios of synonymous to non-synonymous substitutions , patterns of linkage disequilibrium , or shifts in the site frequency spectra [22 , 23] . Although the results of such studies can be consistent with a coevolutionary arms race ( e . g . , positive selection acting on putatively interacting host and pathogen genes ) , the degree of reciprocity between the species cannot be easily ascertained and alternative explanations for parallel positive selection are often plausible . Similar issues plague studies investigating arms races at the phenotypic level . Such studies often rely on fossil times series [24–26] , the phylogenetic distribution of traits [13 , 27 , 28] , or relationships between traits over space/time [2 , 19 , 29–32] . As with the genetic approaches , these phenotypic studies can provide evidence consistent with a coevolutionary arms race ( e . g . , parallel patterns of trait escalation in the fossil record , correlated traits among populations , etc . ) , but are generally unable to quantify the extent of reciprocity or rule out alternative explanations for parallel patterns of escalation in interacting species . Thus , we are currently in a situation where we have tools that can be used to identify parallel patterns of genetic or phenotypic escalation in interacting species pairs , but few tools that can robustly estimate the degree of reciprocal or coevolutionary selection underlying these patterns of parallel evolutionary change . Recently , we developed a maximum likelihood approach that begins to fill this gap in the toolkit available for investigating coevolutionary arms races [33] . This approach estimates the strength of coevolutionary selection between a pair of interacting species using the spatial distribution of traits involved in the interaction . In addition to estimating the strength of coevolutionary selection , this approach opens the door to likelihood ratio tests that allow the relative support for coevolutionary and non-coevolutionary hypotheses to be evaluated . Although fast and efficient , this method relies on a handful of important assumptions . Specifically , this approach assumes interactions do not depend too strongly on the traits of the interacting individuals . In addition , the maximum likelihood approach ignores gene flow among populations and assumes random genetic drift is the only force generating phenotypic diversity among populations . Here we develop a complementary Bayesian approach ( ABC Coevolution ) that relaxes key restrictions of the maximum likelihood framework by allowing for strong coevolutionary selection , gene flow among populations , and environmental variation in abiotic optima . Although we restrict our attention to interactions between species , the general methodology developed here could be applied to arms races between the sexes with only very minor modifications . Extending our approach to other forms of ecological interaction ( e . g . , mutualism ) or different functional forms of interaction ( e . g . , trait matching ) is equally straightforward . We will begin by developing a model that simulates coevolution between a pair of interacting species distributed across a landscape; these simulations will power our ABC framework . Next , we will evaluate the performance of our ABC approach using simulated data . Finally , we will apply our ABC method to a well-studied , but putative , example of a coevolutionary arms race between a seed boring weevil and its plant prey [34] .
We simulate coevolution between a pair of species that interact , and potentially coevolve , within the N spatially distributed populations for which phenotypic data has been collected . Specifically , we follow the population mean phenotypes of the two species within each of N populations over the course of a generation consisting of: 1 ) selection , 2 ) random genetic drift , 3 ) gene flow , and finally 4 ) random mating and inheritance . We then repeat this life cycle for one hundred generations , after which the life cycle continues until the summary statistics , μx , μy , σx , σy , and ρxy reach an approximate equilibrium where the means change by less than 1% of their values each generation and the standard deviations and correlation change by less than 5% of their values each generation , on average , over a ten generation window . Although a 5% change in standard deviations or correlation may seem inconsistent with equilibrium , this level of variation is consistent with sampling error given the relatively small number of populations we study here ( i . e . , < 20 ) . In the sections that follow , we describe the details of each step of this life cycle , pointing out key assumptions along the way . All model parameters and their biological interpretations and assumptions are summarized in Table 1 . Natural selection–We assume that individuals of species i inhabiting population j experience stabilizing selection toward some spatially variable phenotypic optimum , θi , j . Because correlations between abiotic optima may lead to patterns similar to those produced by coevolution [45 , 46] , we allow the phenotypic optima of the two species to be modestly correlated across space , with correlations ranging between -0 . 1 and 0 . 1 . For simplicity , we will refer to this background selection as “abiotic” even though it may result from biotic interactions external to the focal interaction , the abiotic environment , or some combination of both . Specifically , we assume that the abiotic fitness of the two species in population j is given by: WA , X , j=exp[−γX ( xj−θX , j ) 2] ( 1 ) WA , Y , j=exp[−γY ( yj−θY , j ) 2] where γi is the strength of stabilizing selection acting on species i . Selection imposed by the interaction between the focal species , X and Y , is assumed to depend on their relative trait values . For simplicity and brevity , we refer to this as “biotic” selection . Specifically , we assume that the fitness of an individual of species X with phenotype x in an encounter with an individual of species Y with phenotype y is given by: WB , X , j=11+exp[−αx ( x−y ) ] ( 2 ) and the fitness of an individual of species Y with phenotype y in an encounter with an individual of species X with phenotype x is given by: WB , Y , j=11+exp[−αy ( y−x ) ] where the parameter αi measures the sensitivity of the biotic component of fitness in species i within population j to the difference between the phenotypes of the individuals . These functions assume a phenotypic differences or arms race model of interaction [e . g . , 47] where individual fitness is increased by having a phenotypic value that is large relative to that of the interacting individual . For selection to be reciprocal and coevolutionary , αi>0 for both species X and Y . Assuming encounters between individuals occur at random , Eq ( 2 ) can be used to determine the expected fitness of individuals within each species by integrating over the phenotype distribution of the interacting species: W¯B , X , j=∫11+exp[−αx ( x−y ) ]ϕy , j ( y ) dy ( 3 ) W¯B , Y , j=∫11+exp[−αy ( y−x ) ]ϕx , j ( x ) dx where ϕx , j ( x ) and ϕy , j ( y ) are the phenotype frequency distributions for traits x and y , respectively , within population j . These phenotype distributions are assumed to be normal , with means x¯ and y¯ and variances VX and VY , respectively . For simplicity , we assume the phenotypic variances , VX and VY , are constant over space and time . The total lifetime fitness of individuals is assumed to be the product of the abiotic and biotic fitness components: WT , X , j=WA , X , jW¯B , X , j ( 4 ) WT , Y , j=WA , Y , jW¯B , Y , j The mean fitness of each species can then be calculated by integrating total lifetime fitness over the phenotype distribution of the focal species: W¯T , X , j=∫WT , X , jϕx , j ( x ) dx ( 5 ) W¯T , Y , j=∫WT , Y , jϕy , j ( y ) dy Total lifetime fitness ( 4 ) and population mean fitness ( 5 ) can then be used together to predict the frequency distribution of phenotypes within each species and population following selection: ϕx , j′ ( x ) =ϕx , j ( x ) WT , X , jW¯T , X , j ( 6 ) ϕy , j′ ( y ) =ϕy , j ( y ) WT , Y , jW¯T , Y , j where the primes indicate the next step in the life-cycle . The post-selection population mean phenotypes can then be calculated for each species and population by integrating the product of the trait value and post-selection phenotype frequency ( 6 ) : x¯j′=∫xϕx , j′ ( x ) dx ( 7 ) y¯j′=∫yϕy , j′ ( y ) dy Random genetic drift–After selection , we assume a sample of individuals from species i equal to the local effective population size , ni , survives . The population mean phenotypes after this sampling process are then given by: x¯j′′=x¯j′+ξx ( 8 ) y¯j′′=y¯j′+ξy where ξi is a random variable drawn from a gaussian distribution with mean zero and variance equal to Vi/ni in species i . Movement–We assume individuals move among populations at random , with the probability of movement between pairs of populations in species i defined by the migration matrix M . With this assumption , the population mean phenotype for species X in population j following movement among populations is: x¯j′′′=∑k=1Nmx , j , kx¯k′′ ( 9 ) and the population mean phenotype for species Y in population j following movement is: y¯j′′′=∑k=1Nmy , j , ky¯k′′ In these expressions , mi , j , k represents the entry in the migration matrix , M , measuring the probability an individual of species i moves from population k to population j . For the special case of the island model we focus on here where gene flow occurs at an equal rate among all populations , ( 9 ) reduces to: x¯j′′′= ( 1−mx ) x¯j′′+mxN−1∑k=1k≠jNx¯k′′ ( 10 ) y¯j′′′= ( 1−my ) y¯j′′+myN−1∑k=1k≠jNy¯k′′ where mi is the proportion of individuals within each population composed of immigrants from other populations and N is the number of populations for which phenotypic data is available . Random mating and inheritance–Following movement among populations , individuals mate at random and reproduce . Assuming the traits mediating the interaction are heritable , the change in the mean phenotype of species X and Y within population j is given by: Δx¯j=hx2 ( x¯j′′′−x¯j ) ( 11 ) Δy¯j=hy2 ( y¯j′′′−y¯j ) where the heritability , hi2 , is assumed to be constant over both time and space . Approximate Bayesian Computation is a conceptually simple rejection algorithm that implements the following steps: 1 ) Draw parameters of the model from prior distributions , 2 ) Simulate data for the selected parameters and calculate summary statistics , 3 ) If the summary statistics calculated from the simulated data are sufficiently close to their values in the real data , include the parameters in the posterior distribution and return to Step 1 . Otherwise , do not include the parameters in the posterior distribution and return to Step 1 . For well-chosen summary statistics and appropriate thresholds for acceptance into the posterior , this algorithm converges on an accurate approximation of the posterior distribution [48] . Approximate Bayesian Computation has now been applied to a wide range of problems in ecology and evolution , and its strengths and weaknesses are well-understood [41 , 42 , 49] . Here , we rely on previous work demonstrating that the bivariate distribution describing population mean phenotypes of coevolving species within a metapopulation can be accurately described using only five statistical moments to select our summary statistics [46] . Specifically , we summarize both simulated and real data using the average population mean phenotype of each species over the metapopulation , μx and μy , the standard deviation of population mean phenotypes for each species over the metapopulation , σx and σy , and the correlation between the population mean phenotypes of the two species over the metapopulation , ρxy . If the values of these five summary statistics are sufficiently similar in simulated and real data , the parameters generating the simulated data are added to the posterior distribution . The result is multivariate posterior distribution for the 17 model parameters described in Tables 2 and 3 . Detailed descriptions of prior distributions and thresholds for acceptance into the posterior are described in subsequent sections .
To evaluate the performance of our approach , we applied it to a large number of simulated data sets . Specifically , we drew the parameters described in Table 1 at random and simulated evolution within metapopulations consisting of 5 , 10 , and 20 populations . Each simulation assumed migration followed an island model and continued until the metapopulation reached an approximate equilibrium where the statistical moments describing the multivariate distribution of population mean phenotypes remained approximately constant over time . If an equilibrium was not reached within 500 generations , the simulation was halted and parameters drawn again at random . At the completion of each simulation , the summary statistics , μx , μy , σx , σy , and ρxy were recorded . Once data had been simulated , we used our ABC method to develop posterior distributions for the parameters in Table 1 , focusing our assessment of accuracy on the coevolutionary sensitivity in each species , αi , and a composite index for the strength of coevolution equal to αxαy . We studied the performance of our method for two different scenarios . In the first , we assumed little independent biological information was available to inform prior distributions of background parameters ( e . g . , rates of gene flow , effective population sizes , etc . ) such that prior distributions for these parameters were broad and restricted only by biological plausibility . In the second , we assumed independent biological information ( e . g . , molecular studies , experiments , etc . ) was available and could be used to refine prior distributions for background parameters . Unrefined priors–We first considered the power and performance of our method when applied to a biological system where only the trait means of the interacting species are known across populations . In such situations , prior distributions for the background parameters required by our method are constrained only by biological intuition and plausibility . Consequently , the modes of prior distributions could be very far from the actual parameters used to generate simulated data . To evaluate performance under this worst-case scenario , prior distributions were assumed to be identical to the distributions from which parameters used to simulate data were drawn with two exceptions . First , prior distributions for parameters defining the strength of stabilizing selection ( γx , γy ) were assumed to follow uniform distributions informed by meta-analyses [50 , 51] . Second , distributions from which the coevolution parameters were drawn for simulation differed from the prior distributions by including a “hurdle” . Specifically , the simulations drew the coevolution parameters from uniform hurdle distributions that enriched the probability of drawing parameters uniquely equal to zero . Using hurdle distributions allowed us to calculate Type I error rates for our method by guaranteeing “control” simulations were performed where biotic selection was absent for one or both of the species . A detailed description of the distributions used to draw parameters for the simulations and the prior distributions can be found in Table 2 . For each simulated data set , the ABC method was run until 200 points were in the posterior distribution . Although 200 points is far too few to achieve a reliable estimate for any individual simulated data set , it allowed us to explore a much greater diversity of simulated data sets in a reasonable amount of time . Because so few points were included in the posterior , it is likely our results represent the worst-case scenario for the performance of our method . Later , when we apply our method to real data , we vastly increase the number of points in the posterior . Acceptance into the posterior distribution required that the spatial averages of population mean phenotypes be within 0 . 1+15% of their observed values , that the spatial standard deviations of population mean phenotypes be within 0 . 1+20% of their observed values , and that the spatial correlation between population mean phenotypes be within 25% of its observed value . These thresholds were chosen to balance the competing demands of acceptance rate and accuracy in a way that allowed us to explore the performance of our method over a large number of simulated data sets . For each simulated data set , we calculated the estimated values for the coevolutionary sensitivities , αi , by identifying the modes of their marginal posterior distributions . We also calculated a composite strength of coevolution , αxαy , that integrates the coevolutionary sensitives of each species into a single numeric score . Ninety five percent credible intervals were calculated for these quantities as the interval of highest posterior density ( HPD ) in the marginal posterior distribution . Although relying on the marginal distributions for these key parameters ( rather than the full multivariate distribution ) could , in principle , be problematic , initial simulations suggested the posterior distributions for these key parameters are approximately independent in most cases . Focusing on only the marginal distributions allowed us to get more reliable estimates with fewer points in the posterior , and thus allowed us to study a larger number of simulated data sets . We applied our ABC method to 155 simulated data sets where 5 populations were sampled , 166 simulated data sets where 10 populations were sampled , and 162 simulated data sets where 20 populations were sampled . Performance was evaluated in two ways . First , we compared the true values of the coevolutionary sensitivities to their values estimated by the modes of their marginal posterior distributions ( Fig 1 ) . This comparison revealed that our method did a reasonable job of estimating the coevolutionary sensitivities for each species , and the composite strength of coevolutionary selection ( Fig 1 ) . Next , we calculated the percentage of cases in which the true values of the coevolutionary sensitivities fell outside their 95% credible intervals ( Fig 2 ) . This demonstrated that the error rates of our estimates were slightly inflated , with between 4%-8% of estimates lying outside the 95% credible interval ( Fig 2 ) . Similarly , analysis of Type I error rates demonstrated positive values of the coevolutionary sensitivities were erroneously inferred in between 4%-30% of cases , although when twenty populations were sampled the Type I error rates fall to more reasonable values between 8%-17% . Refined priors–In some cases , such as the Camellia-Weevil interaction we will apply our method to in the next section , independent estimates for background parameters are available , allowing for increased refinement of prior distributions . We studied such scenarios by centering the prior distributions for the background parameters on the values used to generate the simulated data ( Table 3 ) . This analysis proceeded identically to that described in the previous section except that the method was tested using 136 simulated data sets with 5 populations sampled , 172 simulated data sets with 10 populations sampled , and 177 simulated data sets with 20 populations sampled . Not surprisingly , when background parameters have been estimated independently and accurately , the performance of our method is substantially improved ( Figs 3 and 4 ) . Specifically , sampling from ten or more populations now guarantees the true values of the coevolutionary sensitivities reside within their 95% credible intervals in at least 95% of simulations as desired ( Fig 4 ) . Similarly , as long as ten or more populations are sampled , the Type I error rates for the coevolutionary sensitivities remain below 5% ( Fig 4 ) . The interaction between the Japanese camellia , Camellia japonica , and its obligate seed predator , Curculio camelliae , is a textbook example of a coevolutionary arms race [34] . Japanese camellias defend against weevil attack using a thickened pericarp that defends the seeds inside from the weevil’s attempts to drill through the defensive pericarp using its elongated rostrum . Key features of this interaction include striking exaggeration of the traits mediating the interaction ( rostrum length in the weevil and pericarp thickness in the camellia ) and a strong statistical association between plant and weevil traits over the geographic range of the interaction . Through a lengthy series of field studies , elegant experiments , and genetic work , coevolution has been established as the most likely explanation for these unusual features of the interaction [32 , 52–55] . Despite the extensive research effort devoted to this system , however , we lack quantitative estimates for the strength of coevolution , other than those we have recently derived using maximum likelihood ( Week and Nuismer , 2019 ) . In this section , we apply our ABC method to this system , capitalizing on the extensive body of existing research to define prior distributions for key background parameters . Trait data–The phenotypic data we analyze comes from studies of this interaction that estimated population mean pericarp thickness and population mean rostrum lengths across 17 populations in Japan [34] . Because our method assumes an island model of migration , we restricted our analysis to the subset of these populations that formed a single cluster within population genetic analyses [56] . This resulted in a final data set consisting of estimates for population mean phenotypes in 13 populations ( S1 Table ) . Prior distributions–Previous research in this system provides a solid grounding for prior distributions of most background parameters . For instance , effective population sizes ( ni ) and rates of gene flow ( mi ) have been estimated for both camellia and weevil [54 , 55] . Heritability ( hi2 ) has been estimated for pericarp thickness directly [53] and can be at least crudely guessed and bounded for rostrum length using estimates for related species [54] . The phenotypic optima favored by stabilizing selection ( μθ , i ) can also be estimated independently from previous work . Specifically , the optimum trait value for the weevil can be at least crudely estimated using rostrum lengths of male weevils , because male weevils do not use their rostra in interactions with the plant [53] . Thus , as long as male and female rostrum lengths are not genetically correlated ( or the population is at equilibrium ) , male rostrum length should serve as a reasonable proxy for the optimum rostrum length in the absence of interaction with the Camellia . Unfortunately , the optimum trait value for the camellia must be estimated using populations outside the range of the weevil , and such estimates could easily be confounded by spatial variation [53] . Consequently , we use rather broad parameters for these parameters to capture this uncertainty . Similarly , the spatial variance in these phenotypic optima can be crudely estimated as the variance in rostrum length in male weevils from different populations and the variance in pericarp thickness from different populations outside the range of the weevil [53] . Phenotypic variance ( Vi ) can be estimated for each species by averaging the within population phenotypic variance for each trait over all thirteen populations included in our analysis . Unfortunately , the strengths of stabilizing selection ( γi ) have not been independently estimated , forcing us to rely on broad priors for these parameters informed only by previous meta-analyses of the strength of stabilizing selection across studies and taxonomic groups [50 , 51] . Prior distributions for all model parameters are described in Table 4 . Posterior distributions and coevolutionary inference–The ABC algorithm was run until there were 7 , 513 points in the posterior distribution , using acceptance thresholds somewhat more restrictive than those used in method performance evaluations . Specifically , parameter combinations were passed to the posterior distribution only when the spatial average population mean phenotypes were within 1 . 120mm and 0 . 887mm of their values in the empirical data for weevil and camellia , respectively , the standard deviations among population mean phenotypes were within 0 . 327mm and 0 . 553mm of their values for weevil and camellia , and the correlation between weevil and camellia mean phenotypes was within 0 . 224 of its value in the data . The modal values of the coevolutionary sensitivities were then identified , as were their 95% credible intervals ( HPD ) . Posterior distributions are reported in Fig 5 . The results demonstrate that the mode for weevil coevolutionary sensitivity ( αW ) is equal to 2 . 37 with a 95% credible interval between 0 . 60 and 2 . 94 . Thus , our results support the idea that Camellia pericarp thickness exerts selection on weevil rostrum length . For the Camellia , our results demonstrate the mode for Camellia coevolutionary sensitivity ( αC ) is equal to 0 . 21 with a 95% credible interval between 0 and 2 . 40 . Thus , our results are consistent with the hypothesis that weevil rostrum length exerts selection on Camellia pericarp thickness , but cannot rule out the possibility that pericarp thickness evolves independently of weevil rostrum length . In summary , the results of our ABC analysis point to reciprocal selection and coevolution as the most likely scenario , but do not preclude the possibility that evolution is unilateral , with weevil rostrum length tracking independently evolving Camellia pericarp thickness . Validation against independent estimates of selection– Although estimates of population mean phenotypes are not available from enough populations for standard methods of cross-validation to be useful [e . g . , 57] , previous work estimating selection gradients acting on pericarp thickness within a number of populations [34] allows independent validation of our estimates for the coevolutionary sensitivities . Specifically , using the coevolutionary sensitivities estimated by our ABC method , we can predict the standardized biotic selection gradient for any population where trait means are known . Applying this approach to the five populations for which significant selection gradients acting on pericarp thickness have been previously reported [34] resulted in a correlation of 0 . 97 between predicted and observed values ( Fig 6 ) . Although strongly correlated , our simulated selection gradients consistently overestimated values measured directly from the data . This discrepancy may arise because our mathematical model fails to capture some of the nuanced functional relationship between weevil rostrum length and camellia pericarp thickness . It is also possible , however , that this apparent discrepancy is nothing more than noise stemming from the small sample sizes used in the empirical study and the small number of populations for which significant estimates of selection are available . Resolving this apparent discrepancy will require data from a larger number of populations and exploration of alternative mathematical formulations . As a whole , however , we take the general agreement between our predicted selection gradients and those directly and independently estimated through phenotypic selection analysis as support for the validity of our approach .
We have developed an approximate Bayesian methodology ( ABC Coevolution ) for estimating the strength of coevolutionary selection using the spatial distribution of trait means . Our approach relaxes key assumptions of an existing maximum likelihood technique and performs reliably when population mean trait values are sampled from ten or more populations and independent information is available to refine prior distributions for background parameters . Specifically , when priors are broad and informed only by biological plausibility , the true values of the coevolutionary sensitivities lie outside of their 95% credible intervals in up to 8% of simulated data sets ( Fig 2 ) . In contrast , when independent estimates of background parameters can be used to refine priors , the true values of the coevolutionary sensitivities lie outside their 95% credible interval in fewer than 5% of cases , as long as 10 or more populations are sampled ( Fig 4 ) . Applying our method to the well-studied interaction between the plant , Camellia japonica , and its seed predator , Curculio camelliae , provides support for the hypothesis of a coevolutionary arms race between armament and defense , but fails to unequivocally rule out the possibility of unilateral trait escalation . Comparing the estimates for coevolutionary selection in the C . camellia–C . japonica derived here with those we previously derived using a maximum likelihood approach ( Week and Nuismer , 2019 ) reveals qualitative similarity ( i . e . , coevolution is the best supported hypothesis ) but quantitative discrepancy . Specifically , the estimates of coevolutionary selection we derive here are much larger than those inferred using maximum likelihood , even after transforming the previous estimates to the same scale of measurement . There are at least three reasons the ABC approach infers a greater magnitude of coevolutionary selection than the maximum likelihood approach . First , the two approaches assume different functional forms of interaction between the species . Second , the maximum likelihood approach assumes only random genetic drift generates spatial variation in trait means . Because drift is a weak force in all but the smallest of populations , spatial variation can be maintained only when stabilizing selection and coevolutionary selection are also very weak . If this were not the case , stabilizing and coevolutionary selection would overwhelm drift and erode spatial variation . By allowing the optimal trait values favored by stabilizing selection to vary across space , the ABC approach avoids this trap and can maintain spatial variation even when stabilizing and coevolutionary selection become strong . Third , the maximum likelihood approach assumes the outcome of interactions does not depend too strongly on the traits of the interacting individuals , allowing analytical approximations for evolutionary change to be derived . Although mathematically convenient , this assumption guarantees the maximum likelihood approach will underestimate the true magnitude of coevolutionary selection . In contrast , the ABC approach developed here avoids this assumption by relying on brute force simulation and so can return estimates of coevolutionary selection that are much greater in magnitude . In short , the maximum likelihood approach is faster and more computationally efficient but will underestimate the strength of coevolutionary selection in cases where its true value is strong . Although the Bayesian approach we develop here relaxes several important assumptions of our earlier maximum likelihood approach ( e . g . , weak coevolutionary selection , absence of gene flow , spatially homogenous abiotic optima ) , it still makes important assumptions that may not be satisfied in all systems . For instance , as currently implemented , our approach does not allow the strength of coevolutionary selection to vary over space , and thus ignores the potential for selection mosaics [58–60] . An obvious , and relatively straightforward , extension of the Bayesian methodology developed here would include such selection mosaics . However , initial explorations of this possibility suggested accurate inference will require sampling trait means from many more populations than what is generally available , even in very well-studied interactions like those between C . camellia and C . japonica . In addition , just as with our previous likelihood-based method , the approach developed here assumes the metapopulation has reached an evolutionary equilibrium , at least with respect to the statistical moments we use as summary statistics . In cases where time series information on traits is available , or it is possible to establish times of divergence , developing non-equilibrium approaches may offer promising alternatives . Our approach also relies upon the temporal constancy of key parameters such as heritabilities , phenotypic variances , abiotic optima , and strengths of stabilizing and coevolutionary selection . Although allowing these parameters to vary over time is relatively straightforward from a programming/computational standpoint , doing so seems wildly premature given we lack sufficient data to establish even ballpark priors for how these parameters change over time in natural systems . Finally , we focus here on the special case of an island model where gene flow occurs equally among all populations . Extending our approach to cases that generate isolation by distance , such as stepping stone models , will allow application to a broader range of biological systems . In summary , we have presented a novel Bayesian methodology for estimating the strength of coevolutionary selection driving putative arms races between pairs of interacting species . Although we have restricted our attention to arms races between species , adapting our approach to arms races within species , such as putative cases of runaway sexual selection or conflict between sexes or groups within a species [e . g . , 15 , 61 , 62] , is a straightforward matter . Similarly , adapting our approach to other forms of ecological interactions such as mutualism or competition or to other mechanisms such as phenotype matching , is extremely straightforward and requires only minor modifications to the source code . Implementing these and other options in our inference package ( ABC coevolution ) will be a central goal of future development . Broad application of the approach developed here provides an opportunity to better understand the distribution of coevolutionary selection across interactions , communities , and ecosystems , and to answer long-standing debates such as the importance of reciprocity in the evolutionary process [63 , 64] .
|
Exaggerated traits involved in species interactions , such as extreme running speeds in predator and prey , have long captivated the imagination of evolutionary biologists and inspired the durable metaphor of the coevolutionary arms race . Despite decades of research , however , we have only a handful of examples where coevolution has been rigorously established as the cause of trait exaggeration . The reason support for a coevolutionary mechanism remains elusive is that we lack generally applicable tools for quantifying the intensity of coevolution . Here we develop a computational approach for estimating the intensity of coevolutionary selection ( ABC Coevolution ) and illustrate its use by applying the method to a well-studied interaction between the plant Camellia japonica and its seed predatory weevil , Curculio camelliae . Our results provide support for a coevolutionary hypothesis but fail to preclude the possibility of unilateral evolution .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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2019
|
Approximate Bayesian estimation of coevolutionary arms races
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The fungus Aspergillus fumigatus is a leading infectious killer in immunocompromised patients . Calcineurin , a calmodulin ( CaM ) -dependent protein phosphatase comprised of calcineurin A ( CnaA ) and calcineurin B ( CnaB ) subunits , localizes at the hyphal tips and septa to direct A . fumigatus invasion and virulence . Here we identified a novel serine-proline rich region ( SPRR ) located between two conserved CnaA domains , the CnaB-binding helix and the CaM-binding domain , that is evolutionarily conserved and unique to filamentous fungi and also completely absent in human calcineurin . Phosphopeptide enrichment and tandem mass spectrometry revealed the phosphorylation of A . fumigatus CnaA in vivo at four clustered serine residues ( S406 , S408 , S410 and S413 ) in the SPRR . Mutation of the SPRR serine residues to block phosphorylation led to significant hyphal growth and virulence defects , indicating the requirement of calcineurin phosphorylation at the SPRR for its activity and function . Complementation analyses of the A . fumigatus ΔcnaA strain with cnaA homologs from the pathogenic basidiomycete Cryptococcus neoformans , the pathogenic zygomycete Mucor circinelloides , the closely related filamentous fungi Neurospora crassa , and the plant pathogen Magnaporthe grisea , revealed filamentous fungal-specific phosphorylation of CnaA in the SPRR and SPRR homology-dependent restoration of hyphal growth . Surprisingly , circular dichroism studies revealed that , despite proximity to the CaM-binding domain of CnaA , phosphorylation of the SPRR does not alter protein folding following CaM binding . Furthermore , mutational analyses in the catalytic domain , CnaB-binding helix , and the CaM-binding domains revealed that while the conserved PxIxIT substrate binding motif in CnaA is indispensable for septal localization , CaM is required for its function at the hyphal septum but not for septal localization . We defined an evolutionarily conserved novel mode of calcineurin regulation by phosphorylation in filamentous fungi in a region absent in humans . These findings suggest the possibility of harnessing this unique SPRR for innovative antifungal drug design to combat invasive aspergillosis .
Invasive fungal infections are a leading cause of death in immunocompromised patients [1] . With a 40–60% mortality rate , invasive aspergillosis , caused by the filamentous fungus Aspergillus fumigatus , is the most frequent fungal cause of mortality [2] . Through both genetic and pharmacologic inhibition , we have established that the conserved phosphatase calcineurin is necessary for invasive fungal disease [3] , [4] . Although currently available calcineurin inhibitors FK506 and cyclosporine A are active in vitro against A . fumigatus [5] , they are also immunosuppressive in the host , limiting therapeutic effectiveness . Our goal is to translate fungal biology into tangible clinical benefit by identifying targets that specifically inhibit fungal calcineurin , resulting in fungal killing without suppressing the immune system of the host . Calcineurin is a Ca2+/calmodulin ( CaM ) -dependent protein phosphatase comprised of a catalytic A and regulatory B subunit heterodimeric complex [6] . Calcineurin is activated after Ca2+/CaM binds to calcineurin A at the CaM-binding domain ( CaMBD ) , adjacent to the calcineurin B binding helix ( CnBBH ) in its regulatory domain and displaces the auto-inhibitory domain ( AID ) [6] , [7] . Although calcineurin is conserved from yeasts to human , it exhibits diverse roles in different cell types , evidenced by modulating immune responses , impacting muscle development , neuronal plasticity and cell death in mammalian cells [8]–[11] , and influencing cation homeostasis , morphogenesis , cell wall integrity , mating , and stress responses in yeasts [12]–[15] . In the fission yeast Schizosaccharomyces pombe , calcineurin participates in morphogenesis by affecting septal positioning , spindle body organization , and membrane trafficking [16] , [17] . In the pathogenic yeasts Candida albicans and Cryptococcus neoformans , calcineurin regulates growth at alkaline pH and elevated temperature , membrane stress , and virulence [18]–[20] . In filamentous fungi , calcineurin is important for cell cycle progression , hyphal branching , stress adaptation , sclerotial development and formation of the infectious appressorium in a plant pathogen [21]–[25] . As a protein phosphatase , calcineurin is known to dephosphorylate specific substrates [26] . However , few reports have focused on phosphorylation of calcineurin as a mechanism of its own activation . King and Huang [27] first reported that bovine brain calcineurin contains sub-stoichiometric amounts of covalently bound phosphate , suggesting calcineurin regulation by phosphorylation . While bovine calcineurin phosphorylation by CK1 yielded no change in resultant activity [28] , its phosphorylation by CaM Kinase II and PKC in the CaMBD ( S411 ) inactivated it and decreased its affinity for substrates [29]–[31] . Although this phosphorylation was inhibited upon CaM binding [29] , it did not significantly alter the binding of CaM [32] . Recently , calcineurin from S . pombe was shown to be activated after phosphorylation by the check point kinase Cds1 at the similarly positioned serine residue within the CaMBD ( S459 ) , and at another site at the C-terminus ( S521 ) [33] . We and others have previously determined that calcineurin is required for hyphal growth and virulence of A . fumigatus [3] , [34] . We subsequently showed that the calcineurin complex ( CnaA and CnaB ) localizes at both the hyphal tips and septa to direct proper hyphal growth and regular septum formation , and that the regulatory subunit ( CnaB ) is essential for activation of the catalytic subunit ( CnaA ) in vivo [35] , [36] . Here we performed mutational analyses in the functional domains of A . fumigatus CnaA to investigate those required for hyphal growth , CnaA septal localization , phosphatase function , and virulence . We uncovered six novel findings , including ( i ) the linker between the CnBBH and CaMBD , contains a region unique to filamentous fungi ( completely absent in humans ) , that is rich in serine and proline residues ( 404-PTSVSPSAPSPPLP-417; designated “SPRR” for Serine Proline Rich Region ) and is phosphorylated in vivo at all 4 clustered serine residues ( S406 , S408 , S410 and S413 ) , ( ii ) complementation of the A . fumigatus ΔcnaA mutant strain with calcineurin A homologs from other fungi defined a filamentous fungal-specific phosphorylation of the SPRR in CnaA , suggesting its evolutionarily conserved importance in fungal hyphal growth , ( iii ) GSK-3β , CK1 , CDK1 and MAP kinase as potential kinases that phosphorylate the SPRR , implicating their role in the regulation of A . fumigatus CnaA , ( iv ) mutations in the SPRR did not affect septal localization of CnaA but resulted in significant hyphal growth and virulence defects , implicating the importance of calcineurin phosphorylation for its function in A . fumigatus and its possibility as a new antifungal target , ( v ) CaM is not required for septal localization of CnaA but is required for its function at the hyphal septum , and ( vi ) the PxIxIT substrate binding motif in CnaA is required for its localization at the hyphal septum .
To characterize domains required for CnaA activity and septal localization , we generated A . fumigatus strains expressing a series of truncated cnaA cDNAs ( cnaA-T1 , cnaA-T2 , cnaA-T3 and cnaA-T4 ) under the control of its native promoter in the ΔcnaA mutant strain ( Figure 1A ) . While the expression of cnaA-T1 , containing only the catalytic domain ( 1–347 aa ) , did not complement the hyphal growth defect of the ΔcnaA mutant strain and mislocalized CnaA in the cytoplasm ( Figure 1B and 1C ) , expression of cnaA-T2 that included the CnBBH region ( 1–400 aa ) showed partial growth recovery , indicating that this fragment may bind to CnaB in vivo and partially function by less efficiently localizing at the septum ( Figure 1B and 1C ) . However , expression of cnaA-T3 ( 1–425 aa ) , containing the linker region spanning 23 aa between the CnBBH and CaMBD ( Figure 1A; indicated in red ) , completely restored hyphal growth and efficiently localized CnaA at the septum ( Figure 1B and 1C ) . This indicated that the CaMBD and AID are not required for septal targeting of CnaA . Complete hyphal growth recovery observed in the CnaA-T3 strain also suggested the possibility of a constitutively active calcineurin due to the absence of the AID . Expression of cnaA-T4 , including the CaMBD but not the AID ( 1–458 aa ) , also completely restored hyphal growth and properly localized CnaA at the septa ( Figure 1B and 1C ) . Expression of all the constructs was confirmed by Western analysis ( Figure 1D ) . Our previous studies have shown that the “paradoxical effect” ( attenuation of the antifungal activity of the echinocandin drug caspofungin at elevated concentrations ) is calcineurin-mediated , and that paradoxical growth is abolished in the ΔcnaA mutant strain lacking calcineurin activity [37] . While the CnaA-T1 strain showed no paradoxical growth ( Figure 2 ) , the CnaA-T2 strain exhibited partial recovery of paradoxical growth only at 4 µg/ml of caspofungin . In comparison to the wild-type , the CnaA-T3 and CnaA-T4 strains displayed more sensitivity to 0 . 25 µg/ml caspofungin , indicating less calcineurin activity , but showed almost wild-type equivalent paradoxical growth recovery at 4 µg/ml caspofungin . Concordant with these findings , the CnaA-T1 and CnaA-T2 strains ( Figure 1E ) showed a significant reduction in calcineurin activity ( 86% and 80% , respectively ) , and the CnaA-T3 strain showed only a 28% decrease in activity . Inclusion of the CaMBD in the CnaA-T4 strain restored wild-type level of calcineurin activity ( Figure 1E ) . The growth restoration of the CnaA-T3 and CnaA-T4 strains may also be attributed to constitutively active calcineurin due to the truncation of the C-terminal autoinhibitory domain . Taken together , these results indicated that the major determinants/residues for hyphal growth restoration in the CnaA-T3 and CnaA-T4 strains and CnaA septal targeting may be present in this newly described linker region between the CnBBH and the CaMBD of CnaA . However , it is possible that targeting CnaA to the hyphal septum occurs either independently or by binding of the linker region to other unknown protein ( s ) . Because the truncated forms of CnaA revealed the importance of the linker region between the CnBBH and the CaMBD for hyphal growth and septal localization of CnaA , we performed multiple sequence alignments of A . fumigatus CnaA with homologs from other organisms . This alignment confirmed a high degree of conservation within the catalytic domain , CnBBH , and the CaMBD across different species ( data not shown ) . However , the linker region showed marked variation in different species ( Figure 3A and S1 ) . There are no structural data available on fungal calcineurins , so we modeled the A . fumigatus CnaA-CnaB complex based on the available human calcineurin complex structure [38] . Our calcineurin modeling attempts did not reveal any known structure in this linker region , as it seemed to be highly disordered . Inside the 23-residue A . fumigatus linker region there is a specific 14 residue domain ( 404-PTSVSPSAPSPPLP-417 ) that is relatively conserved in filamentous fungi and completely absent in the human calcineurin α-catalytic subunit ( Figure 3A ) . We designated this as the “Serine-Proline Rich Region” ( SPRR ) , based on the preponderance of serine and proline residues . A phylogenetic tree constructed from an alignment of the CnBBH-linker-CaMBD domains from diverse organisms showed that , although the CnBBH and CaMBD were nearly identical for all species , the linker , and specifically the SPRR , clearly distinguished the filamentous fungi from other species ( Figure 3B ) . This suggested that the SPRR could be evolutionarily important for filamentous hyphal growth . The A . fumigatus SPRR has little homology with the region in the yeast S . cerevisiae , and , importantly , the region is absent in human calcineurin . We transformed the A . fumigatus ΔcnaA mutant strain with CnaA homologs from human ( CnAα ) and S . cerevisiae ( CNA1 ) and found no hyphal growth recovery and cytosolic CnaA localization ( data not shown ) . We then complemented our A . fumigatus ΔcnaA mutant strain with CnaA counterparts from other phylogenetically distinct fungi belonging to the phyla basidiomycota and zygomycota ( Figure 4A and 4B ) . CNA1 from C . neoformans minimally restored the hyphal growth defect ( Figure 4B ) but septal localization was seen , indicating that C . neoformans CNA1 contained the determinants required for septal localization but not hyphal growth . Western analysis confirmed the expression of C . neoformans CNA1 ( Figure 4D ) . Next , we constructed a chimera ( CNAFCNA ) consisting of the N-terminal C . neoformans CNA1 catalytic domain and the C-terminal regulatory domain of A . fumigatus cnaA , including the SPRR ( Figure 4B ) . This chimera completely restored hyphal growth , indicating that the SPRR is important for regulating hyphal growth . We also complemented the A . fumigatus ΔcnaA mutant strain with calcineurins from a phylogenetically unrelated zygomycete fungus , Mucor circinelloides , which grows as extended hyphae but lacks septation ( Figure 4B ) . M . circinelloides has three calcineurin A homologs ( designated as MccnaA , MccnaB and MccnaC; Lee SC et al , communicated ) and we utilized the genes encoding MccnaA and MccnaC that showed variability in the SPRR ( Figure 4A ) . Although both McCnaA and MccnaC localized to the septum , MccnaC expression showed greater recovery of the hyphal growth defect ( Figure 4B ) . Clustal alignment with the A . fumigatus CnaA SPRR revealed that MccnaC contained 3 prolines and a serine residue , but MccnaA had only a single serine residue ( Figure 4A ) , suggesting that the partial growth complementation observed with MccnaC may be due to partial homology to the A . fumigatus SPRR . To confirm this , we transformed the A . fumigatus ΔcnaA mutant strain with cnaA homologs from closely related filamentous fungi Magnaporthe grisea and Neurospora crassa that have greater homology in the SPRR ( Figure 3A and 4C ) . Both M . grisea and N . crassa calcineurins fully complemented the growth defect , restored calcineurin activity ( data not shown ) and also localized to the hyphal septa ( Figure 4C ) . Western analysis confirmed the expression of the respective calcineurins from M . circinelloides , M . grisea , and N . crassa ( Figure 4D ) . While calcineurin activity was decreased by ∼70% in the C . neoformans CNA1 complemented strain , concomitant with the decreased radial growth , expression of the chimera ( CNAFCNA ) , which contained the SPRR , completely restored both calcineurin activity and hyphal growth ( Figure 4E ) . Although the McCnaC partially complemented the hyphal growth defect , that replacement strain possessed less calcineurin activity . Taken together , these results indicated that SPRR is important for regulating proper hyphal growth , calcineurin activity , and CnaA septal localization . The concentration of serine and proline residues in SPRR may create a hydrophobic environment , and the PPLP-motif , predicted to be a WW-domain protein binding motif , may contribute to protein-protein interactions [39] . Phosphorylation at serine or threonine residues that precede a proline , referred to as proline-directed phosphorylation , is known to play an essential role in the regulation of cellular processes such as cell proliferation and differentiation [40] . The two proline residues P409 and P414 , preceded by serine residues at positions 408 and 413 , respectively , prompted us to examine the phosphorylation of A . fumigatus CnaA . We found that the isolated A . fumigatus CnaA-EGFP fusion protein reacted with the anti-phosphoserine antibody , indicating that CnaA was phosphorylated in vivo ( data not shown ) . Further phosphoproteomic analyses by LC-MS/MS identified 6 serine residues phosphorylated in A . fumigatus CnaA ( Figure 5 ) , including all four serine residues clustered in the SPRR at positions 406 , 408 , 410 and 413 ( Figure 5A and 5B ) , and two additional serine residues in the C-terminus at positions 537 and 542 ( Figure 5C ) . Validation of phosphorylation site localization was performed using the AScore algorithm ( Table 1 ) . Furthermore , we also identified that the calcineurin regulatory subunit , CnaB , which was co-purified with CnaA , was also phosphorylated at two serine residues ( Ser21 and Ser33 ) at its N-terminus ( Figure S2 and Table 1 ) . To investigate if phosphorylation of the evolutionarily conserved filamentous fungal SPRR is also a conserved feature , we isolated M . grisea and N . crassa CnaA from the two complemented A . fumigatus strains and analyzed their in vivo phosphorylation status . Two phosphorylations ( positions 432 and 436 ) were detected in the M . grisea CnaA SPRR , and a single phosphorylated serine residue ( position 423 ) was detected in the N . crassa CnaA SPRR ( Figure S3A and S4 ) . Additionally , we also identified the phosphorylation of a serine residue ( Ser577 ) in the C-terminus of M . grisea CnaA ( Figure S3B ) . Because we also noted partial hyphal growth complementation with the M . circinelloides CnaC construct , which contains a single serine residue ( position 404 ) in the region aligning with the A . fumigatus CnaA SPRR , we verified its phosphorylation status in vivo and found that this serine residue was phosphorylated , along with another serine residue at position 499 in the C-terminus ( Figure S5A and S5B ) . These results confirmed that CnaA phosphorylation at the SPRR is a unique and conserved mechanism in filamentous fungi . In order to determine the potential kinase ( s ) that may phosphorylate the CnaA SPRR we scanned this region using Scansite 2 . 0 , NetPhos 2 . 0 , and NetPhosK 1 . 0 programs . These analyses suggested that the amino acids surrounding S406 and S413 of CnaA form a potential consensus sequence for phosphorylation by the proline-directed kinases , such as glycogen synthase kinase ( GSK-3 ) , cyclin dependent kinase 1 ( CDK1 ) , and mitogen activated protein kinase ( MAP Kinase ) . Casein kinase I ( CK1 ) was also predicted to phosphorylate the SPRR . Based on this prediction , we performed in vitro phosphorylation assays using the purified recombinant CnaA regulatory domain ( AfRD; regulatory domain spanning 395–482 aa of CnaA , including the SPRR and the CaMBD ) from A . fumigatus and various combinations of the kinases . The phosphorylation reactions were processed for mass spectrometry after proteolytic digestion to identify the phosphorylated residues . As shown in Table 2 , GSK-3β and CK1 alone phosphorylated the S413 and S406 residues , respectively . Because GSK-3β recognizes two substrate motifs characterized by either primed or non-primed phosphorylation sites at serine/threonine-proline rich motifs , and a majority of GSK-3 substrates are formed via prior phosphorylation by an additional kinase at position P+4 ( pS/TXXXpS/T ) [41] , a combination of the two kinases was also tested . Interestingly , GSK-3β and CK1 together phosphorylated all 4 clustered serine residues ( S406 , S408 , S410 and S413 ) within the SPRR . Next , to determine the role of GSK-3β and CK1 in the phosphorylation of S406 , S408 , S410 and S413 in vivo , we treated the CnaA-EGFP expression strain with GSK-3β and CK1 specific inhibitors , GSK-3β inhibitor VII and D4476 , respectively . Both the GSK-3β inhibitor VII and D4476 showed a growth inhibitory effect in a concentration range of 0 . 5–0 . 75 µM ( data not shown ) . The CnaA-EGFP fusion protein was isolated after treatment with 0 . 75 µM each of GSK-3β inhibitor VII and D4476 and analyzed for its phosphorylation status by mass spectrometry . Surprisingly , treatment with GSK-3β and CK1 inhibitors resulted in the dephosphorylation of only S406 , but S408 , S410 and S413 were phosphorylated , suggesting the possibility of S406 as a target for GSK-3β and CK1 in vivo , while other kinases may be involved in phosphorylating the S408 , S410 and S413 residues in vivo . To investigate this possibility , we performed in vitro phosphorylation reactions in presence of the other potential proline-directed kinases , CDK1 and MAP kinase . As shown in Table 2 , we found that CDK1 alone phosphorylated 2 serine residues at positions 406 and 408 in the SPRR . Although we could not identify any sites phosphorylated in the presence of MAP kinase alone , a mixture of CDK1 and MAP kinase phosphorylated S406 , S408 , S410 and S413 . Based on these results , it is possible that more than one kinase is responsible for regulating CnaA by phosphorylation at the SPRR in vivo and further work on the interaction of these enzymes with CnaA , specifically addressing the timing of phosphorylation of CnaA by these enzymes , will lead to a more definitive understanding of the role of these kinases in the regulation of CnaA . Since the immunosuppressant FK506 inhibits calcineurin activity by binding to the immunophilin FKBP12 , we also examined the phosphorylation of CnaA and CnaB in the presence of FK506 to correlate phosphorylation versus activity . The FK506-treated sample showed a 2-fold decrease in the phosphorylation of S406 in the CnaA SPRR and a 1 . 2- and 1 . 8-fold increase in the phosphorylation of S537 and S542 , respectively , in the C-terminus compared to the untreated control ( Figure 6 and S6 ) . While CnaB was phosphorylated at S21 and S33 residues under basal conditions , FK506 also significantly reduced the phosphorylation at S33 ( Figure 6 and S7 ) . These results suggest a previously unknown link between FK506-FKBP12-mediated inhibition of calcineurin activity and CnaA phosphorylation , including in the novel SPRR . Based on a recent report on the inactivation of GSK-3 by calcineurin inhibitors , cyclosporine A and tacrolimus ( FK506 ) in renal tubular cells [42] , and our result demonstrating the phosphorylation of CnaA by GSK-3β and CK1 , it is possible that FK506 inhibits the activity of GSK-3β , resulting in its inability to phosphorylate CnaA . Ca2+/CaM binds to the CaMBD to displace the AID , resulting in calcineurin activation [7] . In mammalian calcineurin , phosphorylation at S411 near the CaMBD resulted in its inactivation [29] , while phosphorylation at the same residue in S . pombe ( S459 ) activated calcineurin [33] . However , filamentous fungal calcineurins do not show conservation of this phosphorylation site in the CaMBD ( Figure S1 ) . A recent study on the structural basis for the activation of human calcineurin by CaM revealed that the intrinsically disordered CaMBD , along with ∼25 to 30-residues adjacent to the AID , adopts an α-helical structure upon Ca2+/CaM binding [43] . Since the SPRR is located close to the N-terminal end of the CaMBD ( Figure S1 ) , we examined if the phosphorylation of the four serine residues in the SPRR influences Ca2+/CaM binding to CnaA or imparts a change in structural content following Ca2+/CaM binding . To test this , we utilized the purified AfCaM and the CnaA regulatory domain ( AfRD; regulatory domain spanning 395–482 aa of CnaA , including the SPRR and the CaMBD ) from A . fumigatus . We also expressed another recombinant AfRD in which we mutated the four SPRR serine residues to glutamate to mimic phosphorylated status , designating this as AfRD-4SE . The CD spectra of both complexes ( AfRD+AfCaM and AfRD-4SE+AfCaM ) indicated essentially identical secondary structural content revealing that conformational changes that occur upon Ca2+/CaM binding appear to be unaffected by the phosphorylation ( Figure 7 ) . Although glutamate residues may not accurately mimic the phosphorylated state , these results suggest that phosphorylation in the SPRR does not alter the structure of the calcineurin complex , but there is a significant increase in α-helical content . The CD spectrum for AfCaM bound to AfRD has more α-helix than the mathematical sum of the individual AfCaM and AfRD spectra , as evidenced by the stronger negative bands at 222 nm and 208 nm ( Figure 7 ) . This is consistent with earlier observations of the human regulatory domain bound to Ca2+/CaM [43] . This suggests that the AfRD is disordered , and then folds upon binding to AfCaM , and we speculate that the phosphorylation-dependent activation of CnaA is independent of Ca2+/CaM binding . We next mutated the 4 phosphorylated serine residues in the SPRR to alanine ( CnaAmt-4SA; S406A , S408A , S410A and S413A ) to block phosphorylation and also the 4 serine residues to glutamate ( CnaAmt-4SE ) to mimic a fixed phosphorylated state in vivo . In comparison to the wild-type strain expressing CnaA-EGFP , only the CnaAmt-4SA strain exhibited a growth defect ( Figure 8A , 8B , and 8D; GMM panel ) , but CnaA septal localization remained unaltered ( Figure 8C ) . To verify if the slightly increased cytosolic staining seen in the CnaAmt-4SA strain is due to protein instability , we performed Western analysis of the extracts obtained from the strains . As shown in Fig . 8E ( upper panel ) , all the mutated constructs were stably expressed , indicating the possibility that the higher cytosolic distribution seen in the CnaAmt-4SA could be a consequence of this mutation , leading to some amount of CnaA mislocalization . While the control strain and the CnaAmt-4SE strain showed complete hyphal growth , the CnaAmt-4SA strain had very poor growth in liquid media ( Figure 8D ) . This indicated that the phosphorylation of the 4 serine residues is required for calcineurin-mediated regulation of hyphal growth but is not required for CnaA septal localization . Moreover , the CnaAmt-4SA strain showed hyphal growth recovery in the presence of sorbitol , indicative of osmotic stress and cell wall defects ( Figure 8A; SMM panel ) similar to our other calcineurin mutant strains [3] , [36] , [37] , [44] . The CnaAmt-4SA strain was also hypersensitive to caspofungin ( Figure 8A; GMM+Caspofungin panel ) . Supporting these observations , calcineurin activity was also decreased by ∼70% in the CnaAmt-4SA strain compared to the wild-type strain , indicating that phosphorylation plays an important role in the regulation of calcineurin activity ( Figure 8E; lower panel ) . Although the CnaAmt-4SE strain showed complete recovery of hyphal growth , its activity was also decreased by ∼25% , indicating the possibility of active phosphorylation-dephosphorylation events required to fully control calcineurin activation and function . Although the substitution of glutamate residues for phosphorylated serines does not perfectly mimic phosphorylation in vivo , the CnaAmt-4SE strain indeed showed wild-type comparable hyphal growth , but only a slight decrease in calcineurin activity . It is possible that a threshold level of calcineurin activity is sufficient for regular hyphal growth . Because we noted a significant growth defect of the CnaAmt-4SA strain ( Figure 8 ) , we examined its virulence in our persistently neutropenic murine inhalational model of invasive aspergillosis . The mortality associated with CnaAmt-4SA strain infection ( Figure 9A ) was significantly lower ( 10% ) in comparison to the wild-type strain ( 90% ) ( P<0 . 0001 ) indicating that phosphorylation of the four serine residues clustered in this novel SPRR is critical for calcineurin function and virulence . Consistent with the survival data , lung histopathologic studies revealed both decreased inflammation as well as a near absence of hyphal growth in the mice infected with the CnaAmt-4SA strain compared to those infected with the wild-type strain ( Figure 9B ) . Binding studies with the human calcineurin-NFAT complex previously revealed the PxIxIT motif as a common binding site for calcineurin on its substrates [38] . In S . cerevisiae , mutation of the calcineurin residues ( N366 I367 R368 ) in contact with the PxIxIT motif resulted in defective substrate interaction [45] . Recent structural studies of Ca2+/CaM bound to a 25-residue peptide spanning the CaMBD in the human calcineurin catalytic subunit also revealed that R408 , V409 , and F410 play a major role in rigidity and stabilization of the central helix of CaM bound to calcineurin [46] . To investigate the role of these specific domains for CnaA septal localization and calcineurin function in A . fumigatus , we mutated the PxIxIT-binding NIR residues to alanines ( NIR-AAA ) , as well as the critical Ca2+/CaM-binding RVF residues in the CaMBD to alanines ( RVF-AAA; Figure 10A and 10B ) . The NIR-AAA mutation only partially restored hyphal growth and completely mislocalized CnaA , indicating that septal localization of CnaA occurs through binding to other protein ( s ) ( Figure 10C and 10D ) . On the contrary , the RVF-AAA mutation had partial hyphal growth restoration but did not affect CnaA septal localization ( Figure 10C and 10D ) , supporting our CnaA truncation results . Western analysis confirmed that both mutations maintained protein stability ( Figure 10E ) . The observed growth defect with the RVF-AAA mutation may be due to the inability of CaM to bind to CnaA , and as a result the AID remains bound to the regulatory domain , leading to continued inhibition of calcineurin activity . Although CaM localizes at the hyphal tip and septum in A . nidulans [47] , and we confirmed this in A . fumigatus ( data not shown ) , these results , coupled with our truncational analyses , confirmed that CnaA localization at the septum is CaM-independent , yet CaM is required to activate CnaA to completely restore hyphal growth . Critical regions controlling calcineurin function in S . cerevisiae have been identified by substitution of V385 with an aspartic acid that disrupted the interaction between the catalytic and the regulatory subunit , and also by random mutagenesis of three residues ( S373 , H375 , and L379 ) that led to loss of calcineurin activity but did not disrupt calcineurin A binding to Ca2+/CaM or to calcineurin B [48] . To examine if any of these mutations would affect the septal localization or function of A . fumigatus CnaA , we mutated V371 to aspartic acid ( V371D ) and the T359 , H361 , and L365 to proline , leucine and serine ( THL-PLS ) , respectively . Both mutations had a significant effect on hyphal growth , but neither affected CnaA septal localization ( Figure 10C and 10D ) . The V371D mutation confirmed our previous finding [36] that , although CnaB is not required for CnaA septal localization , it is required for CnaA function and growth . The THL-PLS mutation had an effect on the catalytic activity and therefore it is possible that although CnaA is localized at the hyphal septum it is catalytically inactive . We confirmed the stability of each mutation by Western analysis ( Figure 10E ) . The reduction in calcineurin activity due to these mutations ( Figure 10F ) and the lack of paradoxical growth recovery ( Figure S8 ) established that catalytic site residues and CnaB-binding activity of CnaA do not influence its septal localization , yet catalytically active calcineurin is required at the hyphal septum to direct proper hyphal growth .
Calcineurin inhibitors are promising new antifungal candidates due to their unique mode of action from other antifungal classes ( e . g . , polyenes , triazoles , echinocandins ) , efficacy against emerging resistant strains , and synergism with existing antifungals [4] . However , currently-used calcineurin inhibitors complex with immunophilins leading to host immunosuppression [49] . Although calcineurin has been well studied in several organisms and its functional domains described , few studies have focused on mutations in its key domains in vivo , and none have examined phosphorylation as a mechanism of calcineurin function in a human pathogen . By deleting the C-terminal regulatory domains of CnaA which led to progressive defects in hyphal growth ( Figure 1 and 2 ) , we identified a unique fungal-specific 23 residue linker domain between the CnBBH and the CaMBD , containing the novel and evolutionarily conserved SPRR ( Figure 3A ) . Inclusion of the SPRR showed full recovery of hyphal growth , concomitant increase in calcineurin activity , and clear localization of CnaA to the hyphal septum ( Figure 4 ) . To evaluate the conservation of this linker region , we examined it in 22 eukaryotes selected based on divergence and to include model organisms and pathogens affecting both humans and plants ( Figure S1 ) . Phylogenic analysis showed that the linker containing the SPRR clearly distinguished the filamentous fungi ( Figure 3B ) . Based on the CnaA-CnaB molecular model we created ( Figure 10A ) , the SPRR is present outside the core binding region between CnaA and CnaB , and the preponderance of proline and serine residues in this linker creates a hydrophobic environment which could lead to binding to other as of yet unknown proteins . To determine the importance of the SPRR for CnaA function in vivo , we performed complementation tests in our A . fumigatus ΔcnaA mutant strain with cnaA homologs from human , S . cerevisiae , C . neoformans and M . circinelloides , all of which lacked similarity within the SPRR ( Figure 4A and 4B ) . While neither human nor S . cerevisiae CNA , which possess an overall 56% and 50% similarity to A . fumigatus CnaA , respectively , complemented the hyphal growth defect ( data not show ) , C . neoformans CNA1 , which exhibits 67% similarity to A . fumigatus CnaA , localized to the septum but did not restore hyphal growth ( Figure 4B ) . Interestingly , only M . circinelloides CnaC , which had partial similarity to the A . fumigatus SPRR , and exhibits 59% similarity to A . fumigatus CnaA , partially complemented the hyphal growth defect and localized CnaA at the septum ( Figure 4A and 4B ) . Domain swapping of the C . neoformans CNA1 C-terminus with the A . fumigatus cnaA , to include the SPRR in the chimera , completely restored hyphal growth and localized CnaA at the septum ( Figure 4B ) , revealing that the SPRR is required for calcineurin function in regulating proper hyphal growth . To further confirm this , calcineurins from more closely related ascomycete filamentous fungi , such as N . crassa and M . grisea , each of which exhibit overall similarity of 80% and greater conservation in the SPRR , were used for complementation of the A . fumigatus ΔcnaA strain . The respective complemented strains showed proper septal localization and complete hyphal growth recovery ( Figure 4C ) . Taken together , although these results clearly indicated the importance of the SPRR for calcineurin function , we cannot exclude the possibility that some minor variations in other regions of calcineurin may also limit the ability of calcineurins from other species to fully complement the A . fumigatus ΔcnaA strain . Our phosphoproteomic analyses provided unequivocal evidence of A . fumigatus CnaA phosphorylation in vivo at the unique SPRR that is specific to filamentous ascomycetes ( Figure 5 and Table 1 ) . Phosphorylation at the SPRR serine residues in proximity to the proline residues may induce secondary structure conformation in the molecule facilitating binding to other proteins . Moreover , as this SPRR is non-conserved in the yeasts and is completely absent in the human calcineurin α-subunit , it may have been acquired during evolution by diverging from the phylum basidiomycota . As mentioned earlier to confirm if phosphorylation at the SPRR is also important for other filamentous fungi , we performed similar complementation analyses with the closely related filamentous fungi N . crassa and M . grisea , which contain 4 and 5 serine residues in their SPRR , respectively , and both complemented hyphal growth ( Figure 4C ) . Phosphoproteomic analyses confirmed the phosphorylation of serine residues from those fungi within the SPRR ( Figure S3 and S4 ) , demonstrating a unique feature of calcineurin function via a conserved phosphorylation in this novel domain found only in filamentous fungi . Mutation of the 4 serine residues in the A . fumigatus SPRR to block phosphorylation of CnaA caused increased branching and reduction in hyphal growth , confirming the significance of this phosphorylation ( Figure 8 ) . Most importantly , we also found that the CnaAmt-4SA strain was defective in virulence ( Figure 9 ) , strengthening our results of the requirement for phosphorylation at the SPRR for calcineurin activation and function in vivo . Phosphorylation was reduced in both the catalytic and the regulatory calcineurin subunits following treatment with FK506 ( Figure 6 , S6 and S7 ) . It is possible that the FK506-FKBP12 complex may indirectly cause an inhibitory effect on the kinase that phosphorylates calcineurin at the SPRR and also at the N-terminus of CnaB . Based on the phosphorylation of residues in both the SPRR and the C-terminus , we speculated that more than one kinase is responsible . Although mammalian calcineurin was shown to be phosphorylated at S411 in the CaMBD by PKC and CaM kinase II 29–31 , and the same residue at position S459 in S . pombe was phosphorylated by Cds1 check point kinase [33] , this serine residue is not conserved among filamentous fungal calcineurins . Based on our identified phosphorylation sites in A . fumigatus CnaA and the SPRR , we were able to predict the phosphorylation of this region by potential proline-directed kinases such as GSK-3 , CDK1 and MAP kinase . By in vitro phosphorylation assays , using the enzymes GSK-3β and CK1 , we identified that all 4 serine residue in the SPRR were phosphorylated . Additionally , S413 and S408 are both flanked by downstream proline residues , which represent typical GSK-3 phosphorylation sites . While GSK-3β alone phosphorylated S413 in the SPRR , CK1 alone phosphorylated S406 , and a combination with GSK-3β led to phosphorylation of other serine residues ( S408 and S410 ) , revealing that the prephosphorylation of S406 residue by CK1 may trigger the subsequent phosphorylation of S408 and S410 . Interestingly , a previous study showed that the yeast Mck1 protein kinase belonging to the GSK-3 kinase family stimulated calcineurin activity by phosphorylating Rcn1 , and in the absence of GSK-3 kinase , calcineurin activity was fully inhibited revealing a allosteric mechanism of calcineurin regulation by Rcn1 [50] . We further validated our in vitro phosphorylation data by examining the phosphorylation status of CnaA in vivo after treating with inhibitors for GSK-3β and CK1 , which confirmed the absence of phosphorylation at S406 only but not the other serine residues , leading to the notion that other kinases may also phosphorylate the CnaA SPRR in vivo . The observed phosphorylation of CnaA SPRR by CDK1 and MAP kinase in vitro strengthens this possibility . However , further analyses are required to specifically understand how these enzymes actually regulate CnaA . Since the CaMBD is in close proximity to the SPRR , we hypothesized that phosphorylation in the SPRR caused conformational changes or altered binding between Ca2+/CaM and the calcineurin complex . Circular dichroism studies revealed that conformational changes that occur after Ca2+/CaM binding remained unaffected between unphosphorylated and phosphomimetic SPRR constructs ( Figure 7 ) , indicating that CaM-mediated activation of calcineurin is independent of SPRR phosphorylation . This is intriguing when considering that calcineurin phosphorylation at S411 in Rat slightly decreased the affinity of calcineurin for Ca2+/CaM , causing its inactivation [29] , and phosphorylation at the same residue ( S459 ) in S . pombe activated calcineurin [33] . Surprisingly , even though Ca2+/CaM is well known to bind to calcineurin and localize at the hyphal tips and septum [47] , the septal localization of CnaA was not impaired by the deletion of the C-terminus ( Figure 1 ) , indicating that CaM is not involved in CnaA septal localization . We confirmed this by mutating key residues ( RVF-AAA ) within the CaMBD ( Figures 10 and 11A ) , which did not cause CnaA mislocalization from the septum but affected hyphal growth , indicating the requirement of CaM for calcineurin activity and growth but not for septal localization . Mutation of V371 ( V371D ) , located at the beginning of the long helix that forms the bulk of the CnaB binding interface of CnaA , would place a charged residue in an unfavorable hydrophobic environment and result in destabilization of the calcineurin heterodimer ( Figure 11B ) . Mutation of N352 , I353 , and R354 ( NIR-AAA ) residues that lie in the substrate recognition β strand of CnaA ( Figures 12A and 12B ) [38] , [45] , revealed that CnaA localizes at the septum ( Figure 10 ) by likely binding to other proteins , and its septal localization is important for hyphal growth . Neither the V371D mutation , nor the triple mutation of the residues T359 , H361 , and L365 ( THL-PLS ) that lie in a stretch of amino acids connecting the last β strand of the catalytic core domain of CnaA with the helical CnaB binding domain ( Figures 10 , and 12C , 12D and 12E ) , altered the CnaA septal localization . This reconfirmed that CnaA localizes to the septum independent of CnaB , validating our previous findings [36] . The septal localization aspect of calcineurin complex is specific to filamentous fungi . Although the exact mechanism of how these mutations affect calcineurin function at the hyphal septum is unknown , we expect that the availability of the crystal structure for A . fumigatus calcineurin in the future would help us in better understanding the consequence of these mutations . Based on our previous report [36] , we presume that the localization and activity of the calcineurin complex at the septum is necessary to maintain proper septum formation through proper cell wall assembly at the septum by regulating the enzymes involved in β-glucan and chitin synthesis , apart from also regulating the cell wall repair mechanisms at the hyphal septa under stress . In contrast to the yeast cells , filamentous fungi proliferate by hyphal tip extension and form septa that divide the hyphal compartments at regular intervals; during these two very important processes , active cell wall biosynthesis is required . Based on our previous findings and also our present results , we think the calcineurin phosphorylation is important for not only its activation but also for its interaction with other substrates that are necessary for cell wall biosynthesis . Our findings on A . fumigatus CnaA are broadly significant because of the conservation of this unique SPRR among filamentous fungi , and phosphorylation in this region is a newly described mode of calcineurin regulation for growth and virulence . Future studies directed towards understanding the phosphorylation status of CnaA at different stages of growth , stress conditions , and also identifying phosphorylation-dependent interactions of the calcineurin complex with other substrates in vivo will help reveal the exact mechanism of calcineurin-mediated regulation of hyphal growth in filamentous fungi . In addition , given the importance of CnaA phosphorylation at the SPRR for its activity , function , and virulence in this pathogen , and the absence of the SPRR in human calcineurin , future antifungal drug targeting to combat invasive aspergillosis could exploit the SPRR .
Animal studies at Duke University Medical Center were in full compliance with all of the guidelines of the Duke University Medical Center Institutional Animal Care and Use Committee ( IACUS ) and in full compliance with the United States Animal Welfare Act ( Public Law 98-198 ) . Duke University Medical Center IACUC approved all of the vertebrate studies under the protocol number A-038-11-02 . The studies were conducted in the Division of Laboratory Animal Resources ( DLAR ) facilities that are accredited by the Association for Assessment and Accreditation of Laboratory Animal Care ( AAALAC ) . Strains are listed in Table S1 . ΔcnaA mutant strain ( ΔcnaA::A . parasiticus pyrG ) [3] , was used for transformations . Isogenic A . fumigatus wild-type strain ( AF293 ) or the strain expressing cnaA-egfp under its native promoter [36] were used as control strains for radial growth assays and paradoxical growth testing [36] , [37] . Axioskop 2 plus microscope equipped with AxioVision 4 . 6 imaging software was used for fluorescence microscopy [36] . For ΔcnaA mutant complementation analyses , the respective truncated or mutated cDNAs encoding cnaA amplified by using pUCGH-cnaA as a template with primers listed in Table S2 were cloned in the plasmid pUCGH-cnaApromo and transformants selected in the presence of hygromycin B [36] were verified by Southern analysis . The calcineurin A encoding cDNAs from C . neoformans , M . grisea , N . crassa and M . circinelloides were amplified from respective cDNA libraries . All genes were amplified using the respective templates and primers listed in Table S2 and cloned into the pUCGH vector as previously described [36] . For cloning human CNA-α subunit the plasmid , pET15b CnA CnB ( obtained from Addgene ) , was used as a template . S . cerevisiae genomic DNA was used as a template to amplify S . cerevisiae CNA1 . The plasmids were sequenced to verify for accuracy prior to their transformation into the A . fumigatus ΔcnaA mutant strain . Transformants were selected in the presence of hygromycin B ( 150 µg/ml ) . Preparation of cell extracts and Western detection were performed as described earlier [36] . Cell extracts from 24 h cultures were assayed for calcineurin phosphatase activity [36] using p-nitrophenyl phosphate as substrate at 405 nm . The difference of absorbance values between the amounts of p-nitrophenol released in the strains versus the ΔcnaA ΔcnaB double mutant control strain represented the phosphatase activity mediated by calcineurin . Each experiment consisted of two biologic replicates , with each assay consisting of 6 technical replicates; data are presented as mean ± SD of nanomoles of pNPP released/min/mg protein . Total cell lysates were extracted and normalized to contain ∼10 mg protein in each sample before GFP-Trap® affinity purification ( Chromotek ) and processed for TiO2 phosphopeptide enrichment and mass spectrometry as previously described [51] . The dried phospho-peptide enriched samples were resuspended in 10 µl of 2% acetonitrile , 0 . 1% formic acid , 10 mM citric acid and subjected to chromatographic separation on a Waters NanoAquity UPLC equipped with a 1 . 7 µm BEH130 C18 75 µm I . D . ×250 mm reversed-phase column . The mobile phase consisted of ( A ) 0 . 1% formic acid in water and ( B ) 0 . 1% formic acid in acetonitrile . Following a 5 µl injection , peptides were trapped for 5 min on a 5 µm Symmetry C18 180 µm I . D . ×20 mm column at 20 µl/min in 99 . 9% A . The analytical column was held at 5% B for 5 min then switched in-line and a linear elution gradient of 5% B to 40% B was performed over 90 min at 300 nl/min . The analytical column was connected to a fused silica PicoTip emitter ( New Objective , Cambridge , MA ) with a 10 µm tip orifice and coupled to an LTQ-Orbitrap XL mass spectrometer . In some experiments the analytical column was connected to a fused silica PicoTip emitter ( New Objective , Cambridge , MA ) with a 10 µm tip orifice and was coupled to a Waters Synapt G2 QToF mass spectrometer through an electrospray interface operating in a data-dependent mode of acquisition . The instrument was set to acquire a precursor MS scan in the Orbitrap from m/z 400–2000 with r = 60 , 000 at m/z 400 and a target AGC setting of 1e6 ions . In a data-dependent mode of acquisition , MS/MS spectra of the three most abundant precursor ions were acquired in the Orbitrap with r = 7500 at m/z with a target AGC setting of 2e5 ions . Max fill times were set to 1000 ms for full MS scans and 500 ms for MS/MS scans with minimum MS/MS triggering thresholds of 5000 counts . For all experiments , fragmentation occurred in the LTQ linear ion trap with a CID energy setting of 35% and a dynamic exclusion of 60 s was employed for previously fragmented precursor ions . When using the Waters Synapt G2 QToF mass spectrometer through an electrospray interface operating in a data-dependent mode of acquisition the instrument was set to acquire a precursor MS scan from m/z 50–2000 with MS/MS spectra acquired for the three most abundant precursor ions . For all experiments , charge dependent CID energy settings were employed and a 120 s dynamic exclusion was employed for previously fragmented precursor ions . Raw LC-MS/MS data files were processed in Mascot distiller ( Matrix Science ) and then submitted to independent Mascot database searches ( Matrix Science ) against a SwissProt ( fungus taxonomy ) containing both forward and reverse entries of each protein . Search tolerances for LTQ-Orbitrap XL data were 10 ppm for precursor ions and 0 . 02 Da for product ions , and for Synapt G2 data were 10 ppm for precursor and 0 . 04 Da for product ions using trypsin specificity with up to two missed cleavages . Carbamidomethylation ( +57 . 0214 Da on C ) was set as a fixed modification , whereas oxidation ( +15 . 9949 Da on M ) and phosphorylation ( +79 . 9663 Da on S , T , and Y ) were considered a variable modification . All searched spectra were imported into Scaffold ( Proteome Software ) and protein confidence thresholds were set using a Bayesian statistical algorithm based on the PeptideProphet and ProteinProphet algorithms which yielded a peptide and protein false discovery rate of 1% . Phosphopeptide intensities were obtained by generating selected ion chromatograms ( 20 ppm window around most abundant charge state of precursor ion with seven point Boxcar smoothing ) from raw LC-MS data . MS response at peak apex was used for quantitating abundance . E . coli optimized genes for A . fumigatus calcineurin ( AfRD; regulatory domain from 395–482 aa of CnaA including the SPRR and the CaMBD ) and calmodulin ( AfCaM ) were synthesized and ligated into a pUC57 . The genes were digested and ligated into the pET303/CT-His vector using XhoI and XbaI . The mutant AfRD4Ser-Glu ( AfRD-4SE ) , containing mutations S14E , S16E , S18E , and S21E , was made using a QuikChange Site-Directed Mutagenesis Kit . AfRD and AfRD-4SE were transformed into E . coli BL-21 ( DE3 ) cells for expression , and purified on a Ni2+/NTA column followed by a calmodulin-sepharose column . AfCaM was purified on a 2-trifluoromethyl-10-aminopropylphenothiazine ( TAPP ) -sepharose column . Protein concentrations were determined by bicinchoninic acid assay . Circular dichroism ( CD ) experiments were performed using a Jasco J-810 spectropolarimeter . Sample buffers were composed of 20 mM Tris , 200 mM NaCl , 4 mM EGTA , pH 7 . 5 , and 20 mM CaCl2 . Samples contained AfRD , AfCaM , or equimolar concentrations of AfRD and AfCaM . The CD experiments shown were all performed in the presence of an excess of calcium in order to determine the effects of the phospho-mimics on the AfRD conformation when bound by AfCaM . The concentrations of either protein alone per sample ranged from 10–20 µM . In the sample that contained both of AfRD and AfCaM , the total protein concentration per sample ranged from 10–25 µM . Spectra were collected in quartz 1 mm pathlength cuvettes . Samples were scanned from 200–260 nm in 0 . 5 nm increments at a scanning speed of 50 nm/sec and each spectrum is the average of 4 scans . The raw CD data ( in millidegrees ) was converted to molar ellipticity . The amount of secondary structure was determined using the CONTIN/LL deconvolution program . In vitro phosphorylation reactions with GSK-3β , CK1 , CDK1/cyclinB and MAP kinase ( New England Biolabs ) contained 10 µg of recombinant CnaA-AfRD protein , reaction buffer supplied by the manufacturer , and 500 µM ATP in total volume of 50 µl . GSK-3β ( 2500 U ) , CK1 ( 5000 U ) , CDK1 ( 100 U ) and MAP kinase ( 500 U ) were used for each reaction . The reactions were performed either with single enzymes or combinations of the different enzymes . The reactions were incubated at 30°C for 4 h and processed for mass spectral analysis following digestion with Glu-C and TiO2 phosphopeptide enrichment . To determine the effect of GSK-3β and CK1 inhibitors on the phosphorylation status of CnaA in vivo , the CnaA-EGFP expression strain was grown in presence of 0 . 75 µM each of the GSK-3β inhibitor VII ( calbiochem ) and D4476 ( abcam ) for a period of 24 h and the isolated CnaA-EGFP fusion protein was subjected to phospho enrichment and mass spectrometry as described earlier [51] . Homology models for the A . fumigatus calcineurin A and B subunits were prepared using the Phyre server . A model of the fungal heterodimer was constructed by superimposing the individually created homology models onto the X-ray structure of the human calcineurin heterodimer bound to a substrate peptide ( PDB ID 2P6B ) [38] . Superpositions and analysis of mutations were performed using PyMol , Coot and Molprobity . Due to the high level of sequence conservation with the mammalian calcineurin structure already determined , the models are largely superimposable at the Cα backbone level and exhibit good packing overall . We employed this model of A . fumigatus calcineurin to propose a structural basis for the disruption of calcineurin activity observed for the mutants described in the current study . Six-week-old CD1 male mice ( mean weight 22 . 5 g ) were immunosuppressed with both cyclophosphamide and triamcinolone acetonide as previously described [52] . Two groups of 20 immunosuppressed , unanesthetized mice each inhaled an aerosolized suspension of either AF293 wild-type or CnaAmt-4SA strain [52] . Survival was plotted on a Kaplan-Meier curve and log rank was used for pair-wise comparison of survival with statistical significance defined as a two-tailed p<0 . 05 . Histopathological examination of the lungs was performed in two mice in each group that were euthanized on day +7 of infection . Lungs were embedded in 10% neutral buffered formalin and subsequently sectioned and stained with Gomori methenamine silver and hematoxylin-eosin for assessment of histological signs of infection . The animal model and experiments were conducted in accordance with the Animal Care and Use Program of the Duke University Medical Center .
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Invasive fungal infections are a leading cause of death in immunocompromised patients . Translating molecular understanding into tangible clinical benefit has been difficult due to the fact that fungal pathogens and their hosts have similar physiology . The calcineurin pathway is an important signaling cascade in all eukaryotes , and calcineurin inhibitors are powerful immunosuppressants that have revolutionized medicine . Through both genetic and pharmacologic inhibition , we have established that calcineurin is vital for invasive fungal disease . Although the currently available calcineurin inhibitors are active in vitro against the major invasive fungal pathogens , they are also immunosuppressive in the host , limiting therapeutic effectiveness . Here we defined an evolutionarily conserved novel mode of calcineurin regulation by phosphorylation in filamentous fungi that is responsible for virulence in the opportunistic human pathogen , Aspergillus fumigatus . This phosphorylation occurs on a cluster of four serine residues located in a unique serine-proline rich domain of calcineurin that is absent in humans . This finding of a new fungal-specific mechanism controlling hyphal growth and virulence represents a new potential target for antifungal drug therapy .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"molecular",
"cell",
"biology",
"biology",
"microbiology"
] |
2013
|
Phosphorylation of Calcineurin at a Novel Serine-Proline Rich Region Orchestrates Hyphal Growth and Virulence in Aspergillus fumigatus
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The Aryl hydrocarbon Receptor ( AhR ) is a transcription factor that mediates the biochemical response to xenobiotics and the toxic effects of a number of environmental contaminants , including dioxins . Recently , endogenous regulatory roles for the AhR in normal physiology and development have also been reported , thus extending the interest in understanding its molecular mechanisms of activation . Since dimerization with the AhR Nuclear Translocator ( ARNT ) protein , occurring through the Helix-Loop-Helix ( HLH ) and PER-ARNT-SIM ( PAS ) domains , is needed to convert the AhR into its transcriptionally active form , deciphering the AhR:ARNT dimerization mode would provide insights into the mechanisms of AhR transformation . Here we present homology models of the murine AhR:ARNT PAS domain dimer developed using recently available X-ray structures of other bHLH-PAS protein dimers . Due to the different reciprocal orientation and interaction surfaces in the different template dimers , two alternative models were developed for both the PAS-A and PAS-B dimers and they were characterized by combining a number of computational evaluations . Both well-established hot spot prediction methods and new approaches to analyze individual residue and residue-pairwise contributions to the MM-GBSA binding free energies were adopted to predict residues critical for dimer stabilization . On this basis , a mutagenesis strategy for both the murine AhR and ARNT proteins was designed and ligand-dependent DNA binding ability of the AhR:ARNT heterodimer mutants was evaluated . While functional analysis disfavored the HIF2α:ARNT heterodimer-based PAS-B model , most mutants derived from the CLOCK:BMAL1-based AhR:ARNT dimer models of both the PAS-A and the PAS-B dramatically decreased the levels of DNA binding , suggesting this latter model as the most suitable for describing AhR:ARNT dimerization . These novel results open new research directions focused at elucidating basic molecular mechanisms underlying the functional activity of the AhR .
The Aryl hydrocarbon Receptor ( AhR ) is a basic Helix-Loop-Helix PER-ARNT-SIM ( bHLH-PAS ) -containing transcription factor that responds to a variety of structurally diverse exogenous and endogenous chemicals with the modulation of gene expression and production of diverse biological and toxic effects in a wide range of species and tissues [1] . Early studies were mainly focused on the role of AhR in mediating the biochemical response to xenobiotics and the toxic effects of environmental contaminants such as halogenated aromatic hydrocarbons ( including dioxins and dioxin-like compounds ) . Recent studies have revealed endogenous regulatory roles for the AhR in normal physiology and development , including the pivotal role that this ligand-dependent transcription factor plays in the differentiation and/or affinity maturation of several key immune cells important in both innate and acquired immune response [1] . These findings have generated a renewed interest in understanding the molecular mechanisms of activation and their contribution to different complex physiological roles of the receptor [1–3] . In the classical mechanism of action [1] , the AhR is maintained in its inactive form in the cytosol as part of a larger protein complex containing hsp90 , XAP2 , and p23 . Following binding of ligand ( agonist ) to the AhR , the AhR protein complex translocates into the nucleus and its dimerization with the AhR Nuclear Translocator ( ARNT ) protein results in the release of the AhR from its cytosolic protein partners and conversion into its high-affinity DNA binding form . Binding of the ligand:AhR:ARNT complex to its specific DNA recognition site , the Dioxin Responsive Element ( DRE ) , stimulates transcription of adjacent genes and the production of the spectrum of biological and toxic effects of AhR ligands [1 , 3] . Extensive mutagenesis and deletion analysis has been carried out to define the functional domains of the AhR and ARNT proteins and these experimental studies have identified a number of domains including those for DNA binding ( bHLH ) , AhR:ARNT dimerization ( HLH , PAS-A and PAS-B ) , ligand binding ( PAS-B ) , hsp90 binding ( bHLH and PAS-B ) and transcriptional activation/coactivator binding ( glutamine rich region ) [4–6] . From a functional point of view , ligand binding to the AhR not only leads to a conformation change that exposes its nuclear localization sequence , facilitating nuclear translocation , but also exposes the PAS-A dimerization domain [7] . Once in the nucleus , the binding of ARNT to the exposed PAS-A domain progressively displaces hsp90 and its associated proteins and leads to subsequent interactions of ARNT with the second dimerization site present in the AhR bHLH domain [5 , 7] . Dimerization of the AhR and ARNT occurs primarily through the HLH and PAS-A domains , and while these domains appear sufficient on their own to allow transformation of the AhR:ARNT complex into its high affinity DNA binding form , deletion mutagenesis and DNA binding analysis have revealed that the PAS-B domain appears to be critically involved in initiation of AhR:ARNT dimerization [4 , 6] . AhR dimerization is a key step in the AhR signaling pathway and increased understanding of the PAS domain dimerization modes would provide further insights into mechanisms of ligand-dependent transformation of the AhR into its transcriptionally active DNA binding form . Moreover , it may provide avenues in which to finally gain insights into the ligand-dependent differences in AhR functionality . Despite elucidation of the role of the bHLH and PAS domains in the AhR:ARNT dimerization , the absence of experimentally determined structures for these proteins has hampered detailed analysis and understanding of the dimer geometry as well as of the molecular determinants of its stability . Recently however , several experimental structures of dimeric forms of PAS domains in other bHLH-PAS proteins have been become available , providing insights into the interactions between these domains [8–13] . bHLH-PAS proteins show conserved N-terminal domains as well as similarities in the mechanisms of action , despite the broad range of functions exerted by these proteins in different systems [2 , 14] . Their N-terminal domains include a common motif for DNA-binding ( i . e . basic amino acids ) attached to a HLH protein dimerization domain , followed by the PAS-A and PAS-B domains . Both PAS domains contribute to dimerization between bHLH-PAS partners , but show distinct functions . PAS-A is primarily a dimerization domain , whereas PAS-B is mainly important for sensing environmental or physiological signals , in addition to providing a second dimerization interface . Class I bHLH-PAS proteins ( including AhR , the hypoxia-inducible factor α ( HIF2α ) , the circadian locomotor output cycles kaput ( CLOCK ) , and some neuronal factors ) have the functional role of binding small molecules ( AhR ) or sensing environmental stimuli ( i . e . , hypoxic conditions for HIF2α ) . These proteins do not dimerize with other Class I proteins . Class II bHLH-PAS proteins ( ARNT , ARNT2 , and brain muscle ARNT-like ( BMAL ) ) have regulatory roles . They are capable of forming dimers with one or more class I proteins and the resulting heterodimers gain the ability to bind to specific DNA sites to regulate specific target genes [2 , 14] . The structural and functional conservation of PAS proteins suggests that structural information derived from bHLH-PAS protein dimers can be exploited for modeling the AhR:ARNT PAS domain dimerization mode . On the other hand , comparison of the recently available crystal structures of these dimers [11–13] ( Fig 1 ) shows that , although the PAS fold is well conserved , different orientations and interfaces appear to be involved in different PAS protein dimers . The human HIF2α:ARNT PAS-B heterodimer , which was made amenable to crystallization by creating a highly stable HIF2α-R247E and ARNT-E362R double mutant [11] , shows an antiparallel orientation of the two domains , with the β-sheet interfaces mediating protein-protein interaction ( Fig 1B ) . In addition to the apo form , some structures of the same dimer containing artificial ligands demonstrated that the HIF2α internal cavity can bind ligands and that such ligands can modulate the HIF2α:ARNT interaction [8–11] . The murine CLOCK:BMAL1 heterodimer was the first crystal structure solved for a dimer consisting of intact bHLH-PASA-PASB domains [13] . It shows a roughly parallel orientation of the two proteins ( Fig 1A ) . The PAS-B domains twist to align the interaction surfaces , consisting of the helical face of CLOCK and the β-sheet face of BMAL1 . Each PAS-A mediates the heterodimeric interaction by a N-terminal extra-domain α-helix packed between the β-sheets surfaces . Finally , the murine AhR PAS-A homodimer [12] , represents the first AhR derived protein structure , shows a dimerization interface similar to that of the CLOCK:BMAL1 PAS-A domains , including the extra-domain α-helices and a portion of the β-sheets surfaces , but with a slightly different reciprocal orientation of the two domains ( Fig 1C ) . An additional PAS dimer structure , the murine Period ( PER ) homodimer including both the PAS-A and the PAS-B domains has been reported [15 , 16] . However , PER lacks the bHLH motif , and the N-terminal portion of the PAS-A domains shows a disordered region instead of the α-helix present in the bHLH-PAS protein structures . The presence of this helix was shown to be critical for both the dimerization and the transcriptional activity of the AhR:ARNT complex [12] . Moreover , the unusual fold of the PER PAS-A domains may also influence the reciprocal orientation of the PAS-B domains , due to the short linker connecting the PAS domains and the presence of a secondary interaction interface of PAS-A:PAS-B type . For these reasons the X-ray structure of the PER homodimer appeared unsuitable for modeling the AhR:ARNT PAS complex . In this contribution we aim to unveil the structural mode of dimerization of AhR:ARNT and identify the essential interacting interfaces . We describe the development and validation of a structural model of the murine AhR:ARNT PAS domain dimer using three X-ray structures of bHLH-PAS domain complexes described above [11–13] as structural templates for the homology modeling stages . The initial hypothesis is that AhR presents a PAS-B dimerization mode similar to that of HIF2α , given that the two proteins share the same dimerization partner , ARNT , and that several chemicals were observed to bind to the PAS-B of both proteins [17] . However , different reciprocal orientation of both the PAS-A and the PAS-B domains , described by the structural templates available for bHLH-PAS proteins , were examined and two alternative dimerization modes were considered for each domain . With the aim of predicting the most reliable dimerization mode , a number of geometrical and energetic computational evaluations were combined to analyze in depth the physico-chemical characteristics of each model at the dimerization interface and to predict the residues that mostly contribute to the dimer stabilization . Following the computational prediction , a set of mutagenesis experiments of both the murine AhR and ARNT proteins was designed and ligand-dependent DNA binding ability of the AhR:ARNT heterodimer mutants was utilized as a measure of normal functional AhR:ARNT dimerization in order to select the most reliable structural model for the AhR:ARNT PAS-A and PAS-B dimers .
Homology modeling of the murine AhR:ARNT PAS domain dimers was performed adopting the following X-ray structures of mammalian PAS domain dimers: the human HIF2α:ARNT PAS-B heterodimer ( PDB 3F1P ) [11]; the murine CLOCK:BMAL1 heterodimer , inclusive of the bHLH , PAS-A and PAS-B domains ( PDB 4F3L ) [13]; and the murine AhR PAS-A homodimer ( PDB 4M4X ) [12] ( Fig 1 ) . In these structures , each individual domain contains both the secondary structure ( SS ) elements and the topological arrangement typical of PAS domains: a five-stranded anti-parallel β-sheet ( with strands in the order 2-1-5-4-3 ) , a bundle of three short helices , and a long α-helix , known as the helical connector ( see S1 Fig ) . The PAS-A and PAS-B domain folds mainly differ in the length of connecting loops , while the PAS-A domains contain an additional N-terminal α-helix ( A’ ) . Given that the template X-ray structures show slightly different reciprocal orientation of the PAS-A domains ( S2A Fig ) and completely different PAS-B dimerization interfaces ( S2B Fig ) , to analyze all the putative dimerization modes of these domains in the AhR:ARNT complex , a two step modeling strategy was devised . In the first step , modeling of the individual PAS-A and PAS-B structures of the mouse AhR and ARNT ( mAhR and mARNT ) was performed starting from the template structure with the highest sequence identity available for each domain . In the second step , different models of the PAS-A and PAS-B dimers were assembled from the protomer models , according to the different dimerization modes described by the template structures . This approach allows detailed examination of the protomer structural rearrangements in the dimerization process and facilitates direct comparison of the different dimer models . The mAhR PAS-A domain was extracted from the crystallographic structure of the mAhR PAS-A homodimer ( PDB 4M4X ) , while for the mARNT PAS-A domain a homology model was developed , using as template the structure of the mBMAL1 PAS-A domain in the CLOCK:BMAL1 dimer ( PDB 4F3L; similarity: 75 . 2% , identity: 55 . 0% ) . Both experimental PAS-A structures lack several residues that map to extended loops connecting the SS elements of the domain core ( S1 Fig ) , significantly distant from the PAS dimerization interface . Since ab initio loop refinement methods applied to such long disordered regions may be error prone [18] , the missing regions were filled by grafting the atomic coordinates of the topologically equivalent PAS-B loops onto the PAS-A structures ( as detailed in the Methods Section ) . The DOPE profiles confirm the quality of both models and the regions surrounding the gaps appear to be not perturbed by the insertion of the grafted loops ( S3 Fig ) . Among the structural models previously proposed and validated by our group for the mAhR PAS-B domain [17 , 19] , the one obtained from multiple HIF2α PAS-B holo structures [17] ( similarity: 52 . 3% , identity: 30 . 8% ) was used as the mAhR PAS-B protomer model . Since ligand binding to the PAS-B stimulates the AhR nuclear translocation and its subsequent dimerization with ARNT , the holo form of the domain obtained by docking the high affinity ligand 2 , 3 , 7 , 8-tetrachlorodibenzo-p-dioxin ( TCDD ) [17] was adopted . The ARNT PAS-B domain was derived from the X-ray structure of the human isoform ( HIF2α:ARNT PAS-B heterodimer , PDB 3F1P; similarity: 98 . 2% , identity: 96 . 4% ) by applying a PyMOL tool for in silico mutagenesis [20] at the following positions: V357I , R362E , N433T and Q434R . In the second modeling step , to obtain the complete structure of the AhR:ARNT PAS-A dimer two alternative models , each derived from one of the templates ( CLOCK:BMAL1 and AhR:AhR ) , were developed by introducing a further refinement of the spatial atomic coordinates in the protomer models , mainly involving the A' α-helices . For the mAhR:ARNT PAS-B dimer two alternative models were built , starting from the protomer models and by using the two distinctly different template structures of the HIF2α:ARNT and CLOCK:BMAL1 complexes . Since the helical bundle region of the CLOCK PAS-B domain is a structurally disordered region rich in prolines , no further refinement based on the CLOCK structure was conducted in this region ( residues: 295–314 in mAhR ) . The two alternative models developed for the AhR:ARNT PAS-A dimer and the two for the PAS-B dimer ( Fig 2 ) were termed PASA . 4F3L , PASA . 4M4X , PASB . 4F3L and PASB . 3F1P , respectively , consistent with the PDB ID of the structural template . Sequence alignments used to obtain the dimer models are presented in S4 Fig . Sequence identities with the templates ( S1 Table ) are above the “twilight zone” threshold ( 30–40% identity ) that was proposed to infer similarity in the interactions of protein-protein complexes [21] and identity and similarity further increase at the dimerization interfaces ( S1 Table ) . The SS elements predicted for the targets are highly consistent with those of the templates ( see the RMSD values in S2 Table ) . The stereochemical quality , assessed by PROCHECK [22] , is good , with 87–95% of residues found in the most favored areas of the Ramachandran plot , and no residues in the disallowed regions . The overall G-factors range from -0 . 18 to 0 . 15 ( S2 Table ) . The overall Z-scores calculated with the ProSA [23 , 24] validation method range from -4 . 03 to -8 . 03 , i . e . within experimentally determined values for protein chains in the current PDB ( S2 Table and S5 Fig ) . The geometrical and energetic features of the AhR:ARNT PPI interfaces for either the PAS-A and PAS-B dimers were compared between the two alternative models in each case , in order to identify the model with the most reliable dimerization mode . The number of residues involved in the dimerization interface of each protomer model obtained by PISA analysis [25] , the characteristics of their intermolecular interactions derived by using LigPlot+ [26] , and the changes in the total solvent accessible surface area upon complexation ( ΔSASA , calculated using NACCESS [27] ) are reported in Table 1 . The same analyses were performed for the dimerization interfaces of the templates , to allow a direct comparison with the models ( data in brackets in Table 1 ) . The results in Table 1 reveal that the PAS-A dimer models show similar interfaces , in terms of area and total number of residues involved , suggesting that the AhR and the ARNT domains contribute equally to the interaction . The PASA . 4M4X model is characterized by more van der Waals interactions , while that of PASA . 4F3L shows more electrostatic interactions ( H-bonds and salt bridges ) . In the PAS-B dimer models , the contributions provided by the AhR and ARNT domains are clearly different . In PASB . 4F3L , where the helical bundle of AhR interacts with the β-sheet of ARNT , ARNT provides a more extended interface area , while in PASB . 3F1P , characterized by the interaction between the β-sheets of the two domains , AhR contributes a larger interface . Moreover , the PASB . 3F1P model exhibits more hydrophobic ( van der Waals ) interactions . No substantial differences emerge between the surface extension and the number of interface residues of each model and those of the related template ( this is also highlighted in the sequence alignment , S4 Fig ) . However , as detailed below , several differences emerge in the residue types and consequently in the intermolecular interactions with respect to the templates . The characteristics of the inter-domain interactions were analyzed in depth using multiple approaches . A preliminary comparison between the per-residue contributions to the global binding free energy in the two alternative dimer models was performed by the Rank Products method [28 , 29] , and applied to MM-GBSA multiple evaluations ( for more details see Methods section ) . Next , the pattern of residues showing significantly different contributions to ∆Gbinding in a given model was termed its ∆G signature ( vide infra ) . The MM-GBSA ∆Gbinding value of each model is related to overall dimer stability , and the residue pairwise contributions to this property , calculated using Energy Decomposition analysis [29 , 30] ( see Methods section ) , were used to localize the most significant energetic couplings in the dimer structure . The Electrostatic Potential Surface , obtained by DelPhi [31] , was used to estimate the complementarity of charged/neutral regions at the dimerization interface . Finally , the hot spot prediction performed by HotPoint [32] , KFC2 [33] and Robetta [34] methods allowed selection of a group of residues that significantly contributed to the binding mode and to the dimer stability . To validate the proposed PAS-A and PAS-B dimer models in order to establish the most reliable AhR:ARNT dimerization mode , a panel of point mutations was defined . The list of the proposed mutants is depicted in Table 4 and involves 11 residues in the AhR protein ( 6 in PAS-A and 5 in PAS-B ) and 16 residues in the ARNT protein ( 7 in PAS-A and 9 in PAS-B ) . The specific mutagenesis positions were chosen based on the energetic/structural information provided from the EPS surfaces , the interaction energy matrices , and the hot spot predictions . An accurate visual inspection of the 3D structures helped us to hypothesize which positions are of most importance for each dimer , and which show unique features between the proposed alternative dimer models . The latter characteristic was verified by using the rank products profiles: most of the proposed mutated positions in a specific dimer model belong to the ΔG signature of that model ( Fig 3 ) . Since the two PAS-A dimer models share a somewhat similar dimerization interface , the positions selected from both models map mainly onto the A’ α-helices and the H and I strands of both the AhR and ARNT proteins ( Table 4 ) . Both PAS-B dimer models suggest the ARNT A , H and I strands as mutagenesis targets , but in this case the two AhR interfaces are dramatically different and involve different structural elements of AhR: the helical region in the PASB . 4F3L model and the A and I strands in the PASB . 3F1P model ( Table 4 ) . All but one of the targeted mutations were designed to disrupt AhR:ARNT PAS domain dimerization . In the PASA . 4F3L model , the mutations A119R in AhR , and L167E and I340R in ARNT would result in an insertion of a charged residue into the hydrophobic core of the PPI interface; conversely , the R236A mutant of AhR and E163A of ARNT may impair the formation of putative intermolecular salt bridges ( Fig 6A ) ; R342A of ARNT was designed to perturb the correct placement of the A’ helix . A unique mutation was planned to reinforce the interaction between the AhR H strand and the ARNT A’ helix: the Q234R mutant of AhR would extend the electrostatic network involving R236 in AhR and E170 in ARNT . In the PASA . 4M4X model: F115S of AhR and Y311Q of ARNT is proposed to disrupt the stacking interaction between the A’ helix of the former protomer and the H strand of the latter; K238A of AhR and D161A of ARNT may impair the formation of a salt bridge between these two residues; F260S of AhR and L169R of ARNT would cause the insertion of a polar or charged residue into the hydrophobic environment ( Fig 6B ) . In the PASB . 4F3L model: mutations within the bundle of aromatic residues that characterize the PPI interface ( F289L and Y316R in AhR , F444E , F446S and Y450Q in ARNT ) may disrupt the stacking interactions and/or insert polar or charged side-chains into this hydrophobic environment; I324R in AhR would place a charged residue directly in the middle of the same region ( Fig 6D ) ; in contrast , the ARNT mutants R366A , N448A and E455A may perturb the network of electrostatic interactions in the solvent exposed portion of the dimerization interface ( Fig 6C ) . In the PASB . 3F1P model: mutations K284D in AhR and E362K in ARNT may eliminate the putative salt bridge between these residues; both I374W of AhR and I458W of ARNT would augment the steric hindrance of the side-chain to perturb the region of hydrophobic interactions; F444R of ARNT would insert a charge residue in the same region ( Fig 6E ) . The proposed mutations ( Table 4 ) were introduced into AhR and ARNT sequences using site-directed mutagenesis and the resulting mutant AhR and ARNT constructs were sequence verified . The introduced mutations did not affect protein expression of AhR or ARNT constructs in the reticulocyte in vitro transcription/translation ( TNT ) system ( S8 Fig ) . The TNT experimental system has been extensively validated and utilized for more than 20 years to analyze the mechanisms of AhR activation and a major application has been for the analysis of ligand-induced AhR:ARNT dimerization and subsequent DRE-dependent DNA binding [4 , 7 , 19 , 38–40] . The ligand-dependent binding of in vitro synthesized AhR:ARNT complexes to DRE-containing DNA can be easily visualized by gel retardation analysis ( S9 Fig ) . This sample gel demonstrates lack of DRE-bound complex with unprogrammed lysate , as well as the supershifting of the AhR:ARNT:[32P]DRE complex in the presence of anti-AhR and anti-ARNT antibodies , confirming the presence of these specific proteins in the induced protein-DNA complex . The TNT expression system is derived from mammalian cells ( rabbit reticulocytes ) and has been shown to produce fully functional AhR capable of ligand-binding , hsp90 binding , ARNT dimerization and ligand-dependent DNA binding ( as a dimer with ARNT ) and allows for easy manipulation of primary protein sequence [4 , 7 , 19 , 38–41] . However , the TNT system does have some limitations , including that only a fraction of the expressed AhR protein appears to be functional ( in contrast , ARNT appears to be fully functional ) . This may contribute to the elevated background activity observed in methods which rely on detection of labeled AhR protein , such as that of co-immunoprecipitation ( CoIP ) . Indeed , CoIP experiments with in vitro synthesized radiolabeled AhR have revealed high background signal and a small signal/background ratio [4] , making CoIP approaches problematic for clear and accurate qualitative and quantitative assessment of the functional activity of mutant and wild-type AhR/ARNT dimers . In contrast , both ligand binding ( hydroxyapatite binding ) and DNA binding ( gel retardation analysis ) methods rely on detection of [3H]TCDD- and [32P]DRE-binding to functional AhR , respectively . Accordingly , these latter experimental approaches have been shown to be valid and appropriate for quantitative assessment of AhR functional activities . Given the well-established overall correlation between AhR:ARNT dimerization and DNA binding by wild-type and mutant AhRs ( except those mutations specifically targeting the DNA binding domain [41] ) , we analyzed the ligand-dependent AhR/ARNT activation using gel retardation analysis as an indirect measure of normal functional ligand-dependent AhR:ARNT dimerization . For those AhR mutations that result in significant reductions in ligand-dependent DNA binding , we also conducted [3H]TCDD ligand binding analysis in order to determine whether the reduction in DNA binding was actually due to a significant reduction in ligand binding ability or whether it was due to an alteration in DNA binding . Given that it has been well established that dimerization of ARNT with agonist-bound AhR is the critical event in AhR transformation ( i . e . conversion of the AhR into its DNA binding form [4–7] ) , those AhR mutations that result in decreased ligand-dependent AhR:ARNT DNA binding but show little decrease in [3H]TCDD ligand binding must be adversely affecting transformation of the AhR into its normal functional DNA binding form . The most likely explanation for this alteration in AhR transformation is that the targeted mutation leads to an alteration in the normal protein-protein interactions important in normal functional dimerization between AhR with ARNT , leading to decreased AhR:ARNT DNA binding DNA binding analysis of in vitro synthesized AhR and ARNT , incubated in the presence of 20 nM TCDD for 2 h prior to DNA binding ( sample gel shown in Fig 7A ) , revealed several AhR and ARNT mutants with significantly decreased DNA binding activity compared to the response obtained with wild-type ( wt ) AhR and ARNT ( Fig 7B and 7C ) . Mutant sets characteristics of either the PAS-A or PAS-B heterodimer models derived using the CLOCK:BMAL1 ( 4F3L ) template ( see Table 4 ) demonstrated both the higher number of affected mutants ( 7 out of 16 proposed 4F3L mutants affected or 50% rate; while with non-4F3L mutants 4 out of 11 affected or 36% ) , and more significant decreases in the level of AhR:ARNT DNA binding . In particular , the L167E PAS-A domain mutant of ARNT , the Y316R PAS-B domain mutant of AhR and F446S PAS-B domain mutant of ARNT , resulted in dramatically decreased levels of ligand ( TCDD ) -stimulated DNA binding , with respect to wtAhR and wtARNT ( Fig 7B and 7C ) . Given similar levels of in vitro expression of all AhR and ARNT mutant proteins ( S8 Fig ) , the observed decreases in functional activity of a specific mutant ARNT protein would likely be due to an effect of the mutation on normal ligand-dependent AhR:ARNT dimerization as ARNT has no adverse effect on AhR ligand binding . All ARNT mutants with significantly decreased DNA binding levels were derived from the PASA . 4F3L or the PASB . 4F3L models ( L167E in PAS-A , and F446S , N448A , and E455A in the PAS-B: all with an 80% or greater decrease in activity relative to wtARNT ) , and only one moderately decreased mutant ARNT ( I458W , ~50% decrease in activity ) was derived from the 3F1P model . In contrast , mutations of AhR may also impair its ligand-binding properties , especially in residues contained within the TCDD-binding fingerprint in the AhR PAS-B domain [19] . Accordingly , all AhR mutants that exhibited reduced ligand-dependent DNA binding activity ( A119R and F260S in PAS-A and K284D , I374W , Y316R and I324R in PAS-B , Fig 7B and 7C ) were further examined by [3H]TCDD ligand-binding analysis in order to determine whether the introduced mutations adversely affected AhR ligand binding and thus the ability of AhR to transform into a form that can functionally dimerize with ARNT and subsequently bind to DNA . Ligand binding analysis demonstrated that several AhR mutants including F260S , K284D , I374W and Y316R exhibited reduced [3H]TCDD binding ability relative to that of the wtAhR ( Fig 8A ) . Three of these mutants were derived from the PASA . 4M4X and PASB . 3F1P models , and therefore the observed decrease in DNA binding ability of these mutant AhRs is likely due to their impaired ligand-binding ability . This point can be better illustrated by comparing specific DNA binding versus specific ligand binding for mutant AhRs ( Fig 8B ) . In this graph , data points for AhR mutants F260S , K284D , I374W and Y316 are located near the line indicating that these AhRs exhibit the same relative ratio of ligand binding to DNA binding , but the ligand-binding and DNA-binding activity of these mutant AhRs were proportionally decreased relative to that of the wtAhR . These results suggest that these mutations simply affect the AhR in a manner that reduced its ligand binding activity and thus its relative ability to be converted into a form that can dimerize with ARNT and bind to DNA , without differentially affecting AhR:ARNT dimerization interactions . Previous mutagenesis studies demonstrating that mutation of Y316 decreased both AhR ligand and DNA binding [19] are consistent with this conclusion . In contrast , while the 4F3L-derived mutants ( A119R and I324R ) demonstrated no reduction in [3H]TCDD specific binding , they exhibited significantly reduced ligand-dependent DNA binding , consistent with impaired dimerization as a likely underlying mechanism . When the effect of decreased functional activity in AhR mutants F260S , K284D and I374W is factored into the overall analysis , only 1 out of 11 ( or 9% ) non-4F3L model-derived mutants demonstrated a moderate decrease in DNA binding ( I458W ) , while 7 out of 16 of the 4F3L-derived mutants ( 45% ) demonstrated decreased DNA binding . Taken together , these data are more consistent with the 4F3L-derived heterodimerization models as being the more reliable ones for the AhR:ARNT dimerization mode of both PAS-A and PAS-B domains .
Aim of this paper was to unveil the structural mode of dimerization of AhR:ARNT and identify the essential interacting interfaces in both the PAS-A and PAS-B domains . Our studies suggest for the first time that the AhR and ARNT PAS domains have dimerization modes similar to those observed in CLOCK:BMAL1 . In addition to a number of computational analyses on the alternative dimer models developed , the reliability of the proposed structures as well as their functional relevance are strongly supported by our site-directed mutagenesis and functional analysis experiments . The very recently published structures of the HIF2α:ARNT complex including the entire bHLH-PASA-PASB domains [42] , further confirm our findings . In fact , both the PAS-A:PAS-A and the PAS-B:PAS-B interdomain interfaces in these new structures are very similar to those observed in the CLOCK:BMAL1 heterodimer [13] . It is conceivable that the dimerization mode previously observed for the isolated PAS-B domains of HIF2α and ARNT [8–11] is less functionally relevant than the modes observed upon crystallization of the entire bHLH-PASA-PASB region in the same complex as well as in CLOCK:BMAL1 . Our results suggest that , not only structures , but also the dimerization modes of the PAS domains are well conserved in bHLH-PAS proteins . However , the functional roles and the biochemical mechanisms of action of proteins belonging to this family are distinctly different . In particular , since AhR is the only bHLH-PAS protein characterized by a ligand-activated mechanism , our models could be the starting point for unraveling the key aspects of a mechanism that could not be investigated at a molecular level until now . In particular , the results reported here combined with the availability of the experimentally resolved scaffold of the entire N-terminal portion of two bHLH-PAS dimers [13 , 42] , offer an unprecedented opportunity to build a full-length model of the N-terminal region of the AhR:ARNT complex . Given that different quaternary architectures are suggested by the two templates for this region [42] , only an accurate modeling of the structure and dynamics of the regions connecting the AhR and ARNT domains combined with experimental validations would allow to propose the more appropriate structure for AhR:ARNT and to characterize the critical PPI hallmarks in the intra- and inter-domain interfaces . These studies would provide important new insight into the role of inter-domain interactions in the signal transmission , from ligand activation to the conversion of the AhR into its high-affinity DNA binding form . Moreover , structural insights here derived for the PAS domain interactions of AhR and ARNT could lead to an increased understanding of the interactions of these domains with other partners , such as the chaperone hsp90 , the AhR repressor , as well as that of co-activator proteins . Detailed characterization of protein-protein interactions , both inter-domain ( within the AhR protein ) and with AhR interacting proteins , would open new avenues to analyze physiological roles of the AhR including both the classical and non-classical mechanisms of its activation .
PAS-A and PAS-B domain structural models were built for the murine isoforms of the AhR and ARNT proteins ( mAhR: UniProt Q3U5D9 , GI 123784256; mARNT: UniProt P53762 , GI 341940591 ) . To obtain reliable alignments between the target sequences and the structural templates , both the sequences and the secondary structure ( SS ) profiles were taken into account . For the target sequences the SS profile was predicted by using the PSIPRED webserver [43 , 44] while for the templates it was attributed by means of the DSSPcont algorithm [45] . The models were built using the MODELLER 9v8 software [18] . From each alignment 100 putative models were produced and ranked according to the DOPE distance-dependent statistical potential [46] . The overall quality of the best-ranked models was assessed with PROCHECK [22] , which provides information about the stereo-chemical quality , and ProSA [23 , 24] validation method , which evaluates model accuracy and statistical significance with a knowledge-based potential . Several residues were not solved in the PAS-A experimental structures: in the AhR ( 4M4X ) , residues 174–209 and 245–252 ( in the FG and HI loops respectively ) ; in ARNT ( 4F3L ) , residues: 228–259 , 272–301 and 315–334 ( in the FG , GH and HI loops , respectively ) . Based on the highly conserved structural folds of the PAS-A and PAS-B domains ( RMSD values for the Cα atoms in the core region: 1 . 31 Å between hARNT . PAS-B ( 3F1P ) and mBMAL1 . PAS-A ( 4F3L ) ; 1 . 44 Å between the mAhR PAS-B homology model [17] and PAS-A ( 4M4X ) ) , these missing regions were filled by grafting the atomic coordinates of the topologically equivalent PAS-B loops onto the PAS-A structures . The overall quality of the PAS-A models and the influence of the inserted loops were evaluated through the inspection of the related DOPE profiles . To remove residual steric clashes and relax the overall structure of the dimer models , energy minimization protocol was performed using the AMBER software [47] . The protein systems were described by the AMBER force field 99SB [48] , whereas the topology of the TCDD ligand , that was included in the mAhR PAS-B model , was treated with the general AMBER force field ( GAFF ) [49] , with atomic partial charges evaluated with the semiempirical method AM1-BCC [50] . The models were placed in a periodic truncated octahedron box with a minimum distance of 10 Å from the protein surface . The systems were solvated using the TIP3P water model [51] and the net charges were neutralized by adding counterions . The minimization was performed by using 500 steps of steepest descent , followed by 1500 steps of conjugate gradient . Van der Waals and short-range electrostatic interactions were estimated within a 8 Å cutoff , whereas long-range electrostatic interactions were calculated using the Particle Mesh Ewald method [52] . The ligand and the backbone atoms of the protein were restrained with a restraint of 120 kcal mol-1 Å-2 . The binding free energy ( ΔGbinding ) of each modeled dimer was evaluated through the MM-GBSA method . In this context the “ligand” is the ARNT and the “receptor” is the AhR protomer . The MMPBSA . py module of the AMBER software was adopted , with the single protocol strategy [53] ( i . e . calculations were performed starting from an optimized structure of each model instead of an ensemble of snapshots sampled from Molecular Dynamics simulations ) . The input structures were treated in implicit solvent [54] . The polar solvation term was approximated with the Generalized Born ( GB ) model [55] by using the OBC re-scaling of the effective Born radii [56] . A physiological salt concentration ( 0 . 154 M ) was chosen to take into account the electrostatic screening effect of salt [57] . The non-polar solvation term was calculated as the product of the surface tension parameter , surften ( set to 0 . 0072 kcal mol-1 Å-2 ) , and the solvent accessible surface area ( SA ) evaluated using the Linear Combination of Pairwise Overlap ( LCPO ) algorithm [58] . The ΔGbinding calculated by the MM-GBSA method was used for the Energy Decomposition Analysis [29 , 30] of each dimer , as detailed in the S1 Appendix . To this end , for each system ( i . e . the receptor , the ligand and the complex ) the free energy was decomposed into the residue pairwise contributions and for each pair it was further decomposed into the different terms ( covalent , electrostatic , van der Waals and solvation ) . Every energetic contribution was subsequently processed as: ΔGij=Gijcomplex− ( Gijreceptor+Gijligand ) where G is the specific term taken into account , i and j are the specific residues involved in the pairwise interaction . Given that in the MM-GBSA approach here employed both the receptor and the ligand conformations are extracted from the optimized geometry of the complex , all the intramolecular residue pairs give ΔGij = 0 . The sum of the non-bonded and the solvation terms for each intermolecular residue pair defines its individual contribution to the binding free energy . All of such contributions are represented as a matrix , herein defined as interaction energy matrix . The relative per-residue contributions to the overall binding energy of the dimer models were compared by means of the Rank Products algorithm [28 , 29] , as detailed in S1 Appendix . A necessary assumption is that two dimer models subjected to comparison are assembled from the same protomer models that can be oriented differently , resulting in alternative dimerization interfaces . Considering two hypothetical dimers A and B , each residue g of the protomer is ranked on the basis of its relative contribution to the ΔGbinding , thus generating two distinct scores RgA and RgB . The difference between the scores indicates if residue g mostly contributes to dimer A ( positive values ) or B ( negative values ) . Results are presented in logaritmic form , LOG ( RP ) . The subset of LOG ( RP ) values with the same sign indentifies the minimal lists of residues that differentially contribute to ΔGbinding of each dimer model , and it is herein termed ΔG signature . The dimerization interface of each dimer model was analyzed using PISA ( Proteins , Interfaces , Structures and Assemblies ) web server [25] . The resulting list of residues included in the interface was investigated in terms of possible intermolecular non-bonded interactions ( namely , van der Waals and H-bonds ) using the DIMPLOT module , a specific extension of LigPlot+ software for plotting protein-protein or domain-domain interactions [26] . The burial degree of the individual domains upon complexation was evaluated through the calculation of the variations of the Solvent Accessible Surface Area ( ΔSASA ) using NACCESS [27] . The Electrostatic Potential Surface ( EPS ) of each domain in the modeled dimers was calculated using the DelPhi v4 software [31] , with a grid spacing of 0 . 5 Å . Hot spots are defined as those residues belonging to a PPI interface whose sidechains are predicted to significantly stabilize the binding mode [59 , 60] and three different methods for predicting the hot spots , based on alternative methodological assumptions , were selected following previously reported comparative studies [61] . The predictions provided with the HotPoint method [32] are based both on evaluation of ΔSASA and on a scoring function termed potential contact , based on the number of interacting residues in a shell of 7 Å . With this method , a hot spot is found if the SASA is reduced at least by 20% and the potential contact score is at least 18 . The predictions of the KFC2 method [33] are based on a machine learning method trained on 47 different features derived from solvent accessibility and biochemical properties of the residues ( e . g . , hydrophobic profiles , non-bonded interactions and π-stacking interactions ) . The predictions provided by the Robetta method [34] are obtained by performing in silico alanine scanning and calculating the ΔΔG upon mutation with an internal energy function based on rotamers evaluation . Hot spots are defined here as those residues showing a ΔΔG equal or larger than 1 kcal mol-1 . Plasmids mβAhR/pcDNA3 and mβARNT/pcDNA3 have been previously described [7] . The accession number for the specific AhR cDNA is NM_001314027 . 1 and the accession number for the specific ARNT isoform a cDNA is NM_001037737 . 2 . The point mutagenesis was performed using Quikchange Lightning Kit ( Agilent ) and the resulting plasmid constructs were verified by sequencing . To determine protein expression , constructs were synthesized in vitro using TNT Reticulocyte Lysate Kit ( Promega ) in the presence of [35S]methionine ( Perkin-Elmer ) , separated on 4%-10% SDS-PAGE and visualized by autoradiography as previously described [62] . Gel retardation analysis of ligand-activated TNT-synthesized AhR and ARNT has been extensively documented in the literature [4 , 7 , 19 , 38–40] and is a well-established and validated method for analysis of AhR functionalities , including ligand binding , dimerization and DNA binding . For gel retardation analysis , AhR and ARNT proteins were synthesized in vitro using TNT Reticulocyte Lysate Kit ( Promega ) , diluted ( 1:1:8 , AhR:ARNT:buffer ) in MEDGK ( 25 mM MOPS-NaOH , pH 7 . 5 , 1 mM EDTA , 1 mM DTT , 10% [v/v] glycerol , 0 . 15 M KCl ) and incubated in the presence of 20 nM TCDD or 1% ( v/v ) solvent control DMSO for 2 h prior to gel retardation analysis [62] . Briefly , AhR transformation reactions ( 10 ml ) were incubated with 15 ml oligo buffer ( 41 . 7 mM MOPS-NaOH , pH 7 . 5 , 16 . 7% [v/v] glycerol , 253 mM KCl , 16 . 7 mM DTT , 8 . 3 mM EDTA , 0 . 375 ng/ml poly [d ( I•C ) ] [Roche] ) for 15 min , followed by incubation with 100 , 000 cpm [32P]DRE for 15 min and gel separation on a 4% native polyacrylamide gel [40] . Gels were visualized by autoradiography using Fujifilm FLA-9000 and MultiGauge analysis . For ligand-binding analysis , in vitro synthesized AhR protein was diluted with MEDGK ( 1:9 ) and incubated in the presence of 10 nM [3H]TCDD for 1 h at room temperature followed by hydroxyapatite analysis as previously described [62] . Unprogrammed TNT lysate was used as non-specific control in binding reactions . [3H]TCDD was a kind gift from Dr . Stephen Safe ( Texas A&M University ) .
|
Computational modeling combined with experimental validation may give insight into structural and functional properties of protein systems . The basic Helix-Loop-Helix PER-ARNT-SIM ( bHLH-PAS ) proteins show conserved functional domains despite the broad range of functions exerted by the different systems . Within this protein family , the Aryl hydrocarbon Receptor ( AhR ) is known to mediate the toxic effects of a number of environmental contaminants , including dioxins and dioxin-like chemicals , and it also exerts other biochemical and physiological effects . Despite the absence of experimentally determined structures , theoretical models of the AhR PAS domains developed on the basis of homologous systems have allowed understanding of some aspects of the molecular mechanisms underlying its function . In this work we present alternative structural models of the transcriptionally active complex of AhR with the AhR Nuclear Translocator ( ARNT ) protein . Computational characterization of the modeled protein-protein interaction interfaces guided the design of mutagenesis experiments , and evaluation of the DNA binding ability of the resulting AhR:ARNT dimer mutants allowed validation of the models and selection of the most reliable one . These findings open new research directions for understanding the molecular mechanisms underlying the functional activity of the AhR .
|
[
"Abstract",
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"Results",
"Discussion",
"Methods"
] |
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] |
2016
|
Deciphering Dimerization Modes of PAS Domains: Computational and Experimental Analyses of the AhR:ARNT Complex Reveal New Insights Into the Mechanisms of AhR Transformation
|
Replication of plus-strand RNA viruses depends on host factors that are recruited into viral replicase complexes . Previous studies showed that eukaryotic translation elongation factor ( eEF1A ) is one of the resident host proteins in the highly purified tombusvirus replicase complex . Using a random library of eEF1A mutants , we identified one mutant that decreased and three mutants that increased Tomato bushy stunt virus ( TBSV ) replication in a yeast model host . Additional in vitro assays with whole cell extracts prepared from yeast strains expressing the eEF1A mutants demonstrated several functions for eEF1A in TBSV replication: facilitating the recruitment of the viral RNA template into the replicase complex; the assembly of the viral replicase complex; and enhancement of the minus-strand synthesis by promoting the initiation step . These roles for eEF1A are separate from its canonical role in host and viral protein translation , emphasizing critical functions for this abundant cellular protein during TBSV replication .
Genome-wide screens for host factors affecting RNA virus infections have led to the identification of several hundreds host proteins in recent years [1] , [2] , [3] , [4] , [5] , [6] , [7] . These works demonstrated complex interactions between the host and plus-stranded ( + ) RNA viruses , the largest group among viruses . ( + ) RNA viruses contain relatively small genomes and greatly depend on the resources of the infected hosts in many steps during the infection process . These viruses recruit numerous host proteins to facilitate their replication and spread [8] , [9] , [10] . Many host RNA-binding proteins have been implicated in replication of ( + ) RNA viruses , including ribosomal proteins , translation factors and RNA-modifying enzymes [8] , [9] , [10] , [11] , [12] , [13] , [14] . In spite of the extensive effort , the actual function of host factors in ( + ) RNA virus replication is known only for a small number of host factors [8] , [10] , [15] , [16] , [17] . Tomato bushy stunt virus ( TBSV ) and other tombusviruses are model plant RNA viruses with 4 . 8 kb genomic ( g ) RNA coding for two replication proteins , termed p33 and p92pol , and three proteins involved in cell-to-cell movement , encapsidation , and suppression of gene silencing [18] , [19] . Yeast ( Saccharomyces cerevisiae ) expressing p33 and p92pol replication proteins can efficiently replicate a short TBSV-derived replicon ( rep ) RNA [20] , [21] . The tombusviral repRNA plays several functions , including serving as a template for replication and as a platform for the assembly of the viral replicase complex [19] , [22] , [23] . The viral RNA also participates in RNA recombination [6] , [18] , [24] , which likely plays a major role in virus evolution . One of the major advantages of studying TBSV replication is the availability of genomic and proteomic datasets on virus-host interactions [4] , [5] , [6] , [7] , [10] , [15] , [25] , [26] , [27] . For example , systematic genome-wide screens of yeast genes have revealed that TBSV repRNA replication is affected by over 100 different host genes [5] , [7] . Additional genome-wide screens with TBSV also identified ∼30 host genes affecting TBSV RNA recombination [4] , [6] , [28] . The identified host genes code for proteins involved in various cellular processes , such as translation , RNA metabolism , protein modifications and intracellular transport or membrane modifications [3] , [5] , [7] . Additional global approaches based on the yeast proteome microarray ( protein array ) have led to the identification of over 100 host proteins that interact with viral RNA or the viral replication proteins [25] , [26] . Also , proteomics approaches with the highly purified tombusvirus replicase has determined at least seven proteins in the complex , including the viral p33 and p92pol , the heat shock protein 70 chaperones ( Hsp70 , Ssa1/2p in yeast ) , glyceraldehyde-3-phosphate dehydrogenase ( GAPDH , encoded by TDH2 and TDH3 in yeast ) , pyruvate decarboxylase ( Pdc1p ) , Cdc34p ubiquitin conjugating enzyme [14] , [26] , [27] and eukaryotic translation elongation factor 1A ( eEF1A ) [25] . The functions of GAPDH and Hsp70 have been studied in some detail [14] , [29] , [30] , [31] , but the roles of the other host proteins , such as eEF1A , in the replicase complex are currently undefined . eEF1A is a highly abundant cellular protein with a role in delivering aminoacyl-tRNA to the elongating ribosome in a GTP-dependent manner . Many additional functions have been ascribed to eEF1A including quality control of newly produced proteins , ubiquitin-dependent protein degradation , and organization of the actin cytoskeleton [32] , [33] . Although eEF1A has been shown to be part of replicase complexes of several RNA viruses [16] , [34] , [35] , [36] , studies on determining its functions in virus replication are hindered by several major difficulties . These include ( i ) genetic redundancy: yeast has two eEF1A genes ( TEF1 and TEF2 ) , whereas animals and plants have 2–7 genes and several isoforms of eEF1A . ( ii ) eEF1A provides essential functions for cell viability and mutations could have pleiotropic effects on protein translation , actin bundling and apoptosis . ( iii ) eEF1A is a very abundant protein that constitutes 1–5% of total cellular proteins , making it difficult to completely remove eEF1A from biochemical assays using cell extracts . ( iv ) eEF1A is also required for the translation of viral proteins in infected cells , making it difficult to separate its effect on translation versus replication , processes that are interdependent . The first evidence that translation elongation factors , such as EF-Tu and EF-Ts , play a role in ( + ) RNA virus replication was obtained with bacteriophage Qbeta [34] . The eukaryotic homolog of EF-Tu , eEF1A was found to bind to many viral RNAs , including the 3′-UTR of Turnip yellow mosaic virus ( TYMV ) [37] , West Nile virus ( WNV ) , Dengue virus , Tobacco mosaic virus ( TMV ) and Turnip mosaic virus ( + ) RNA [35] , [38] , [39] , [40] . In addition , eEF1A has also been shown to interact with various viral replication proteins or the replicases , such as the NS5A replication protein of Bovine viral diarrhea virus ( BVDV ) [41] , NS4A of hepatitis C virus ( HCV ) [42] , the TMV replicase [43] , and the Gag polyprotein of HIV-1 [44] . It is also part of the replicase complex of vesicular stomatitis virus , a negative-stranded RNA virus [45] . The actual biochemical functions provided by eEF1A for ( + ) RNA virus replication are currently poorly understood . In case of WNV , eEF1A is co-localized with the WNV replicase in the infected cells and mutations in the WNV ( + ) RNA within the mapped eEF1A binding site have led to decreased minus-strand synthesis [46] . On the contrary , eEF1A was shown to enhance translation but repressed minus-strand synthesis of TYMV in vitro [37] , [47] , [48] . Overall , eEF1A likely plays a role in the replication of many RNA viruses . The interactions of eEF1A with viral RNAs and viral replication proteins and its high abundance in cells might facilitate recruitment of eEF1A into virus replication . eEF1A has been shown to interact with the components of the tombusvirus replicase , including the 3′-UTR of the repRNA , as well as the p33 and p92pol replication proteins [25] . eEF1A is also known to interact with the yeast Tdh2p ( GAPDH ) [49] , which is also a component of the tombusvirus replicase . Overall , the multiple interactions of eEF1A with various components of the tombusvirus replicase could be important for eEF1A to regulate yet unknown functions of the viral replicase complex . In this paper , we characterized the functions of eEF1A in TBSV replication based on identification of functional eEF1A mutants in yeast as well as using in vitro approaches . The obtained data support the model that eEF1A plays several roles during TBSV replication , including facilitating the assembly of the viral replicase complex . Moreover , using in vitro replication assays , we demonstrate that eEF1A enhances minus-strand synthesis via stimulating the initiation step of the viral RNA-dependent RNA polymerase . Since eEF1A is also associated with several other viral replication proteins or binds to viral RNAs , it is possible that the uncovered functions of eEF1A might be utilized by other RNA viruses during their replication as well .
To determine the functions of eEF1A during tombusvirus replication , we generated ∼6 , 000 yeast strains expressing eEF1A with random mutations ( see Fig . S1A ) and tested the level of TBSV repRNA accumulation in a high-throughput assay [50] . In this assay , we used yeast strains , in which the two wt eEF1A genes ( TEF1/TEF2 ) were deleted from the chromosome , while the wt or mutated eEF1A was expressed from plasmids . Importantly , a given eEF1A mutant is the only source of eEF1A in the yeast cells used . Using the high-throughput assay , we identified one yeast strain ( N21 ) expressing an eEF1A mutant that supported reduced TBSV repRNA replication , while the other three strains with eEF1A mutants ( named C42 , C53 and C62 ) showed increased level of repRNA accumulation ( Fig . 1A and S1B–G ) . Interestingly , the eEF1A mutants supporting increased steady-state level of repRNA accumulation did not increase the relative level of p33 and p92pol replication proteins ( Fig . 1A , bottom panel; S1D–E ) . Thus , these eEF1A mutants likely affect TBSV replication directly . Accordingly , affinity purification of the solubilized tombusvirus replicase complex from yeast cells , followed by in vitro replicase activity assay revealed that the replicase from C42 , C53 and C62 mutant eEF1A-expressing yeast strains had ∼2-fold increased activities when compared with wt eEF1A-expressing yeast strain ( Fig . 1B , lanes 1–6 versus 7–8 ) . The amounts of replication protein p33 and the co-purified eEF1A were comparable in the purified replicase samples ( Fig . 1B , bottom panel ) , indicating that the differences in replicase activities in the mutants are likely due to enhanced replicase functions , and not due to altered proteins levels in the replicase complexes . Testing the ability of C42 , C53 and C62 mutant eEF1As to bind to the viral RNA or to the p33 and p92pol replication proteins in vitro ( Fig . S2B–C ) did not reveal significant differences between the mutants and the WT . This further supports that these eEF1A mutations likely increase the function of the viral replicase without altering the protein and RNA components in the replicase . Placing the identified mutations in the three novel gain-of-function mutants of eEF1A ( V301D , L374V/N377K , and F413L; Fig . 1C , indicated with yellow balls ) , which exhibited increased tombusvirus replication , over the known structure of eEF1A [51] revealed a cluster on one face of eEF1A ( namely , the actin bundling domain III ) , away from the domains known to bind to tRNA and translation factor eEF1Bα . On the other hand , the new reduced function mutant ( A76V , Fig . 1C , indicated with green balls ) and the previously identified T22S [25] , which exhibited decreased tombusvirus replication , showed a distinct and separate localization . Since eEF1A is part of the tombusvirus replicase complex [25] , it is possible that C42 , C53 and C62 eEF1A mutants might affect the assembly/activity of the tombusvirus replicase . To test this idea , we prepared cell-free extracts ( CFE ) from yeast strains expressing selected eEF1A mutants in the absence of the wt copy of eEF1A . These yeast extracts contained comparable amount of total proteins as well as the amounts of eEF1A , ALP , PGK and Hsp70 ( Ssa ) yeast proteins were comparable ( Fig . 2A ) . The advantage of the CFE extracts is that they can then be programmed with the TBSV ( + ) repRNA in the presence of purified recombinant p33 and p92pol obtained from E . coli that leads to the in vitro assembly of the viral replicase , followed by a single cycle of complete TBSV replication , resulting in both ( − ) -stranded repRNA and ( + ) -stranded progeny [31] , [52] . Therefore , this assay can uncouple the translation of the viral proteins from viral replication , which are interdependent during ( + ) RNA virus infections . Using CFEs from yeast expressing one of the three mutant eEF1As resulted in ∼3-fold increased TBSV repRNA accumulation when compared with the extract obtained from yeast expressing the wt copy of eEF1A ( Fig . 2A , lanes 2–4 versus 5 ) . These data suggest that the viral replicase complex containing the mutant eEF1A can support in vitro TBSV repRNA replication more efficiently than the replicase with the wt eEF1A . In contrast , CFE from N21 yeast supported TBSV repRNA replication to similar extent as the CFE containing wt eEF1A ( Fig . 2A , lanes 1 versus 5 ) , indicating that N21 eEF1A mutant can perform the same functions as the wt eEF1A in vitro , when the same amount of p33 and p92pol was provided . To test if the increased TBSV repRNA replication in vitro was due to enhanced ( + ) or ( − ) -strand synthesis , we analyzed the replication products under non-denaturing versus denaturing conditions ( Fig . 2B ) . These experiments showed that the amount of dsRNA [representing the 32P-labeled ( − ) RNA product hybridized with the ( + ) RNA] increased ∼3-fold in case of C42 , C53 and C62 mutants ( lanes 3–8 , Fig . 2B ) in comparison with the wt ( lanes 9–10 ) . The dsRNA nature of these products was confirmed by the ssRNA-specific S1 nuclease digestion assay ( Fig . 2C ) . On the other hand , the ratio of dsRNA and ssRNA did not change in the various CFEs containing the eEF1A mutants or the wt ( Fig . 2B ) . These results are consistent with the model that the replicase complex carrying the eEF1A mutants increased mostly the level of ( − ) RNA production , which then led to proportionately higher level of ( + ) RNA progeny . Cell-fractionation assay , followed by the cell-free TBSV replication assay demonstrated that the soluble fraction from the C42 , C53 and C62 mutant yeasts stimulated the in vitro replication of TBSV repRNA by ∼3-fold , while the membrane fraction when derived from C42 , C53 and C62 mutant yeasts had a lesser effect ( Fig . 2D , lanes 12–14 versus 7–9 ) . These data are in agreement with the expected mostly cytosolic distribution of eEF1A , albeit eEF1A is also present in the membrane fraction in a smaller amount ( Fig . 2D , bottom panel ) . To test directly if eEF1A could stimulate RNA synthesis by the viral RdRp , we chose the E . coli-expressed recombinant p88pol RdRp protein of Turnip crinkle virus ( TCV ) , which is unlike the E . coli-expressed TBSV or CNV p92pol RdRp , does not need the yeast cell-free extract to be functional in vitro [53] , [54] . The template specificity of the recombinant TCV RdRp with TBSV RNAs is similar to the closely-related tombusvirus replicase obtained from yeast or infected plants [21] , [54] , [55] , [56] . However , the recombinant TCV RdRp preparation lacks co-purified eEF1A , unlike the yeast or plant-derived tombusvirus replicase preparations , facilitating studies on the role of eEF1A on the template activity of a viral RdRp . When we added the highly purified wt eEF1A to the RdRp assay containing TCV RdRp protein and a TBSV derived ( + ) RNA template , which is used by the TCV RdRp in vitro to produce the complementary ( − ) RNA product ( Fig . 3A , lanes 3–4 ) [55] , we observed a ∼6-fold increase in ( − ) RNA synthesis by the TCV RdRp ( lanes 11–12 ) , while , as expected , we did not detect new ( + ) RNA progeny ( not shown ) . This suggests that eEF1A can greatly stimulate TCV RdRp activity in vitro , confirming a direct role for eEF1A in ( − ) RNA synthesis by a viral RdRp . Since it is known that eEF1A can bind to the 3′-UTR of TBSV ( + ) RNA as well as to the tombusvirus replication proteins [25] and to the TCV RdRp ( Fig . S2A ) , we wanted to test if the above stimulating activity of eEF1A in the in vitro RdRp assay was due to binding of eEF1A to the ( + ) RNA template and/or to the TCV RdRp protein . Pre-incubation of the purified wt eEF1A with the TCV RdRp prior to the RdRp assay led to a ∼5-fold increase in in vitro ( − ) RNA synthesis ( Fig . 3A , lanes 9–10 ) , while pre-incubation of the purified eEF1A with the TBSV ( + ) RNA template prior to the RdRp assay led only to a ∼2-fold increase in ( − ) RNA products ( lanes 7–8 ) . Also , pre-incubation of the TCV RdRp with the ( + ) RNA template prior to the RdRp assay containing purified eEF1A led only to a ∼2-fold increase in ( − ) RNA synthesis ( lanes 5–6 ) , suggesting that eEF1A can stimulate ( − ) RNA synthesis less efficiently after the formation of the ( + ) RNA-RdRp complex . Overall , data shown in Fig . 3 imply that eEF1A stimulates ( − ) RNA synthesis most efficiently when it forms a complex with the viral RdRp prior to binding of the template RNA to the eEF1A-RdRp complex . To test if eEF1A stimulates the rate of initiation of ( − ) RNA synthesis , we analyzed the amount of abortive RNA products , which are generated during de novo initiation of RNA synthesis by the TCV RdRp [57] . We found that the amount of the 5–11 nt long abortive RNA products increased by 3 . 5-fold in the presence of purified eEF1A in the TCV RdRp assay ( Fig . 3B , lanes 3–4 versus 1–2 ) . We also tested the RdRp activity in the presence of eEF1A using a ( + ) RNA template with a mutation opening the closed structure in the promoter region that leads to increased template activity [58] . The mutated template also showed 2-fold increased abortive RNA products in the RdRp assay with eEF1A ( Fig . 3B , lanes 5 versus 6 ) . These data strongly support the model that eEF1A stimulates the de novo initiation step in the RdRp assay . To test if eEF1A stimulates the rate of RNA synthesis in the absence of de novo initiation , we analyzed the amount of 3′-terminal extension ( 3′-TEX ) RNA products , which are generated from an internal primer by the TCV RdRp ( Fig . 3D ) [55] . Addition of purified eEF1A did not increase the amount of 3′TEX products ( lanes 2 , 4 , 6 , Fig . 3D ) , suggesting that the elongation step during complementary RNA synthesis is not affected by eEF1A . Altogether , the obtained in vitro TCV RdRp data suggest that eEF1A can mostly stimulate the initiation step during de novo viral ( − ) RNA synthesis . To further test the function of eEF1A in TBSV replication , we used chemical inhibitors of eEF1A , including Didemnin B ( DB ) and Gamendazole ( GM ) . DB inhibits the activity of GTP-bound eEF1A during translation by binding to a pocket in eEF1A involved in the interaction with the aminoacylated tRNA and the nucleotide exchange factor eEF1Balpha [59] , [60] . GM has been shown to inhibit the actin bundling function , while it does not inhibit protein translation or GTP binding functions of eEF1A [61] . We found that both DB and GM efficiently inhibited TBSV repRNA replication in the in vitro assay with CFE , which contains the endogenous eEF1A ( Fig . 4A ) . Time-course experiments revealed that the inhibition by DB was the most effective when the inhibitor was added at the beginning or during the first 10–15 min of the assay ( Fig . 4B , lanes 2–5 ) , while GM inhibited the cell-free replication of TBSV repRNA when added not only at the beginning , but up to 40 min after the start of the assay ( lanes 12–17 ) . It is known that the recruitment of the viral RNA and replication proteins as well as the assembly of the viral replicase complex take place during the first 40–60 min in the cell-free assay [31] . Since DB could inhibit translation , we also tested the effect of another translation inhibitor , namely cycloheximide , which did not affect TBSV repRNA replication in our assay ( Fig . S3 ) . These data suggest that the inhibition by DB and GM is unlikely through decreased translation in the replication assay . Therefore , the above data are consistent with the model that DB and GM interfere with the assembly of the viral replicase complex in the CFE . Also , GM seems to be a more potent inhibitor of TBSV replication than DB . To further test if DB and GM can interfere with the assembly of the tombusvirus replicase complex , we performed a two-step in vitro assembly/replication assay , also based on CFE containing endogenous eEF1A [31] . In this assay , first , we only provide ATP and GTP in addition to the replication proteins , the ( + ) repRNA and CFE , which can support the assembly of the replicase , but cannot perform RNA synthesis due to the lack of CTP and UTP [31] . After 1 hr incubation , once the replicase assembly had taken place , we collected the membrane fraction of the CFE by centrifugation and removed the supernatant containing the unbound p33 , p92 , repRNA as well as the cytosolic fraction of the CFE . Then we added all four rNTPs ( including 32P-labeled UTP ) to the membrane fraction of the CFE to allow for RNA synthesis by the pre-assembled replicase complex ( second step , Fig . 4C ) [31] . Interestingly , adding either DB or GM during the first step resulted in robust inhibition of TBSV repRNA synthesis during the second step of the assay ( Fig . 4C , lanes 2–3 versus 1 ) , whereas providing the same amount of DB and GM at the beginning of the second step did not result in inhibition of repRNA replication ( lanes 4–6 ) . These data support a model that DB and GM could inhibit the assembly of the tombusvirus replicase complex , but not the RNA synthesis by the already assembled replicase . Similarly , DB and GM failed to inhibit TBSV RNA synthesis in an in vitro assay with a highly purified RdRp from yeast ( Fig . S4A ) . Since the assembly of the tombusvirus replicase also depends on events prior to the replicase assembly step , such as template RNA binding by the viral replication proteins/host proteins ( such as eEF1A ) , and template recruitment to intracellular membranes [21] , [52] , we also tested the effect of DB and GM on these processes as well based on purified recombinant eEF1A . We found that GM strongly interfered with the binding of eEF1A to the viral RNA in an EMSA assay ( Fig . 4D , lanes 3–6 versus 2 ) , whereas DB did not affect the binding under the assay conditions ( lanes 9–12 ) . Since DB binds only weakly to eEF1A in solution , but it binds much more effectively to eEF1A in the presence of GTP and the ribosome [62] , we also performed in vitro co-purification experiments . First , 35S-labeled eEF1A was produced in an in vitro translation system ( containing ribosome and GTP ) and , second , biotin-labeled viral ( + ) repRNA was added . After short incubation in the absence or presence of various amount of DB , we performed affinity-purification of the viral RNA . Phosphoimaging revealed that eEF1A was co-purified with the viral RNA and the amount of protein co-purified with the viral ( + ) repRNA was inhibited by increasing amount of DB in the assay ( Fig . 4E , compare lane 1 with 2–5 ) . This demonstrated that DB inhibits the binding of eEF1A to the viral repRNA . Moreover , both DB and GM interfered with the recruitment of the viral template RNA to the membrane of the CFE containing endogenous eEF1A ( Fig . 4F , lanes 5–8 versus 3–4 ) . On the other hand , DB and GM do not seem to affect the interaction between eEF1A and p33 or p92 replication proteins in vitro ( Fig . S4B ) . Altogether , these data suggest that inhibition of eEF1A function by DB and GM could block several steps during the assembly of the tombusvirus replicase complex , including template binding by eEF1A and viral RNA recruitment into replication .
The identified eEF1A mutants were also useful to dissect the functions of eEF1A in TBSV replication . Based on a cell-free TBSV replication assay in CFE prepared from yeast expressing the C42 , C53 or C62 mutants , we found that the minus-strand synthesis was enhanced by ∼3-fold , while the rate of plus-strand synthesis was proportionate with ( − ) RNA synthesis , resulting in ∼10-fold more ( + ) than ( − ) RNA products for wt and each mutant . We confirmed a direct role for eEF1A in RNA synthesis in vitro by using a highly purified eEF1A and the recombinant TCV RdRp , which is closely homologous with the TBSV p92pol . Interestingly , it seems that eEF1A stimulates the RdRp activity directly , since pre-incubation of eEF1A and the RdRp prior to the RdRp assay led to the highest level of stimulation of ( − ) RNA synthesis ( Fig . 3A ) . On the other hand , pre-incubation of eEF1A with the TBSV-derived template RNA led only to ∼2-fold increase in RNA synthesis in vitro ( Fig . 3A ) . Analyzing the amount of short abortive RdRp products , which are produced through initiation followed quickly by abortive termination [57] , in the in vitro assays revealed that eEF1A strongly enhanced the initiation of minus-strand synthesis ( Fig . 3B ) . Although the actual mechanism of stimulation of RdRp activity by eEF1A is currently unknown , we propose that eEF1A might facilitate the proper and efficient binding of the RdRp to the 3′ terminal sequence of the viral RNA prior to initiation of ( − ) -strand synthesis ( Fig . 5 ) . Accordingly , eEF1A was shown to bind to the so-called replication silencer sequence ( RSE ) in the 3′-UTR , which is required for the assembly of the viral replicase complex [22] , [58] . The binding of eEF1A-RdRp complex to the RSE might assist in placing the RdRp over the 3′-terminal promoter sequence , thus facilitating the initiation of ( − ) RNA synthesis starting from the 3′-terminal cytosine . Similar function of eEF1A in stimulation of ( − ) RNA synthesis has been proposed for WNV , based on mutations in the viral RNA within the eEF1A binding sequence that reduced the binding affinity of RNA to eEF1A and inhibited ( − ) RNA synthesis in infected cells [46] . Recent intensive work revealed that the assembly of the viral replicase complex is a regulated process involving viral- and host factors , cellular membranes and the viral ( + ) RNA [8] , [10] , [19] , [63] , [64] , [65] . The assembly of the viral replicase also depends on steps occurring prior to the actual assembly process , such as selection of the viral template RNA and the recruitment of ( + ) RNA/protein factors to the sites of assembly . Although our current understanding is rather poor about the factors involved and their functions during replicase assembly , rapid progress is being made in this area due to the development of a new cell-free assay based on yeast CFE [30] , [31] . The yeast CFE is capable of assembling the tombusvirus replicase complex in vitro in 40–60 min in the presence of recombinant p33/p92pol and the viral ( + ) repRNA [31] , allowing for studies on direct roles of various factors . We find that inhibition of eEF1A activity by either DB or GM also inhibited the assembly of the tombusviral replicase complex based on time-course experiments ( Fig . 4B ) as well as a direct replicase assembly assay ( Fig . 4C ) . On the contrary , the replicase activity was not inhibited by these compounds after the assembly took place ( Fig . 4B–C ) . It is possible that after the formation of the eEF1A-RdRp-repRNA complex DB or GM are not effective in inhibiting the stimulatory effect of eEF1A on the RNA synthesis by the viral RdRp . Additional in vitro experiments with purified tombusvirus replicase preparations confirmed the lack of inhibition of RNA synthesis by DB or GM ( Fig . S4A ) on pre-assembled virus replicases . The inhibition of the tombusvirus replicase complex by DB or GM might come from the ability of these compounds to inhibit the template RNA recruitment step ( Fig . 4F ) . If the recruitment of the viral ( + ) RNA is inhibited , then the assembly of the viral replicase cannot take place in yeast or in vitro [21] , [22] , [31] . A target for GM and DB could be the inhibition of binding between eEF1A and the viral ( + ) RNA ( Fig . 4D , E ) . Since the actual steps during the replicase assembly process are not yet known , it is possible that eEF1A might play additional roles in the assembly of the viral replicase complex . The presented data are also in agreement with the function of eEF1A as a chaperone of the viral RdRp . Binding between the eEF1A and RdRp might alter the structure of the RdRp that favors de novo initiation for RNA synthesis . Indeed , the chaperone activity of eEF1A and its bacterial homolog EF-Tu has been shown before [66] , [67] . Moreover , the EF-Tu-EF-Ts complex is thought to function in the Qbeta replicase complex as a chaperone for maintaining the active conformation of the RdRp protein [68] . Overall , the current work demonstrates two major functions for eEF1A in TBSV replication ( Fig . 5 ) : ( i ) stimulation of the assembly of the viral replicase complex , likely by facilitating the recruitment of the viral RNA template into the replicase; and ( ii ) enhancement of the minus-strand synthesis by promoting the initiation step . These roles for eEF1A are separate from its canonical role in host and viral protein translation .
Saccharomyces cerevisiae strain BY4741 ( MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 ) was obtained from Open Biosystems ( Huntsville , AL , USA ) . Plasmid-borne TEF1/2 TKY strains ( MATα ura3-52 leu2-3 , 112 trp1-Δ1 lys2-20 met2-1 his4-713 tef1::LEU2 tef2Δ pTEF2 URA3 ) were published before [69] , [70] , [71] , [72] . The plasmid pESCHIS4-ADH-His33/CUP1-DI-72 expressing Cucumber necrosis virus ( CNV ) p33 and the TBSV replicon RNA , called DI-72 , was described earlier [25] . The LYS2-based plasmid pRS317-Tet-His92 , expressing CNV p92 under the control of Tetracycline-regulatable ( Tet ) promoter was constructed as follows: the Tet promoter sequence was obtained from pCM189-His92/Tet [73] by digestion with EcoRI and BamHI , and CNV p92 coding sequences from pGAD-His92 [7] digested with BamHI and PstI , followed by ligation into pRS317 vector treated with EcoRI and PstI . To generate mutations within TEF1 coding sequence by random mutagenesis , we constructed the TRP1-based plasmid pRS314-pTEF1-TEF1 , which expressed TEF1 under the control of its native promoter . The TEF1 promoter sequence , the TEF1 coding region and the Cyc1 terminator sequences were amplified by PCR with the following primer pairs , #2764 ( CCGCGAGCTCATAGCTTCAAAATGTTTCTAC ) /#2765 ( CCGCGGATCCGTAATTAAAACTTAGATTAGATTGC ) , #2768 ( CCGCGGATCCAAAATGGGTAAAGAGAAGTCTC ) /#1877 ( CCGCCTCGAGTTATTTCTTAGCAGCCTTTTGAGCAGC ) , and #2769 CCGCCTCGAGGAGGGCCGCATCATGTAA/#2770 ( CCGCGGTACCAGCTTGCAAATTAAAGCCTTC ) , respectively . This was followed by cloning the PCR products into pRS314 digested with SacI and KpnI . The mutagenic PCR conditions were as follows: 50 mM KCl , 10 mM Tris ( pH 8 . 3 at 25°C ) , 7 mM MgCl2 , 0 . 3 mM MnCl2 , 1 mM dCTP and dTTP each , 0 . 2 mM dGTP and dATP each , 0 . 2 µM of each primer , 20 pM of template DNA and 10 units of Taq polymerase in a 10 µl reaction volume in 10 aliquots . The PCR was performed for 30 cycles at 94°C for 1 min , 50°C for 1 min , and 72°C for 1 min in a conventional thermal cycler . Three overlapping ∼300–500 bp N- , central- and C-terminal segments of the TEF1 gene were amplified separately by PCR using primer pairs: #2767 ( GTTTCAGTTTCATTTTTCTTGTTC ) /#2788 ( GAGTCCATCTTGTTGACAG ) , #2787 ( CATCAAGAACATGATTACTGGTAC ) /#2790 ( GACGTTACCTCTTCTGATTTC ) and #2789 ( CGGTGTCATCAAGCCAGGT/#2771 , ( TTCGGTTAGAGCGGATGTGG ) , respectively . Yeast strain TKY102 was co-transformed with constructs pESCHIS4-ADH-His33/CUP1-DI-72 and pRS317-Tet-His92 to induce TBSV repRNA replication according to standard Lithium acetate-PEG protocol [74] . The transformed yeast cultures were grown in a Synthetic Complete ( SC ) media with 2% glucose lacking leucine , histidine , lysine and uracil ( SC-ULHK− ) by shaking at 29°C overnight . To completely suppress TBSV replication before induction , 1 mg/ml Doxycycline was added to the media to inhibit the expression of p92 . The plasmid pool carrying the randomly mutated TEF1 gene was introduced into the yeast cells already transformed with the two virus expression plasmids by in vivo gap repair mechanism via homologous recombination ( Fig . S1A ) [75] . Briefly , pRS314-pTEF1-TEF1 was digested with enzymes to truncate the TEF1 coding sequence , and then the digested plasmid was recovered . The gapped plasmid ( 5–10 µg ) was transformed together with overlapping PCR ( 20 µg ) products carrying the TEF1 mutations created by random mutagenic PCR ( see above ) . The transformed yeast cells were selected on SC media lacking uracil , tryptophan , leucine , histidine and lysine . The colonies were further streaked onto SC media plate lacking tryptophan , leucine , histidine and lysine ( SC-TLHK− ) with 0 . 1% ( w/v final ) 5-Fluoroorotic Acid ( 5-FOA ) media to select against the URA3-based wild-type TEF1 plasmid ( Fig . S1A ) . This selection was repeated once and the loss of URA3 plasmid was confirmed by the inability of the yeast strains to grow on uracil-minus media . The yeast cells carrying the randomly mutated TEF1 were grown at 29°C for 24 h in SC-TLHK− media with 50 µM CuSO4 to induce virus replication . Total RNA extraction from yeast cells and Northern blotting and Western blotting were done as previously described [7] , [25] . Whole cell yeast extract capable of supporting TBSV replication in vitro was prepared as described [31] . The in vitro TBSV replication assays were performed in 20-µl total volume containing 2 µl of whole cell extract , 0 . 5 µg DI-72 ( + ) repRNA transcript , 400 ng purified MBP-p33 , 100 ng purified MBP-p92pol ( both recombinant proteins were purified from E . coli ) , 30 mM HEPES-KOH , pH 7 . 4 , 150 mM potassium acetate , 5 mM magnesium acetate , 0 . 13 M sorbitol , 0 . 4 µl actinomycin D ( 5 mg/ml ) , 2 µl of 150 mM creatine phosphate , 0 . 2 µl of 10 mg/ml creatine kinase , 0 . 2 µl of RNase inhibitor , 0 . 2 µl of 1 M dithiothreitol ( DTT ) , 2 µl of 10 mM ATP , CTP , and GTP and 0 . 25 mM UTP and 0 . 1 µl of [32P]UTP [31] . The reaction mixture was incubated at 25°C for 3 h . The reaction was terminated by adding 100 µl stop buffer ( 1% sodium dodecyl sulfate [SDS] and 0 . 05 M EDTA , pH 8 . 0 ) , followed by phenol-chloroform extraction , isopropanol-ammonium acetate precipitation , and a washing step with 70% ethanol as described [52] . The newly synthesized 32P-labeled RNA products were separated by electrophoresis in a 5% polyacrylamide gel ( PAGE ) containing 0 . 5× Tris-borate-EDTA ( TBE ) buffer with 8 M urea . To detect the double-stranded RNA ( dsRNA ) in the cell-free replication assay , the 32P-labeled RNA samples were divided into two aliquotes: one half was loaded onto the gel without heat treatment in the presence of 25% formamide , while the other half was heat denatured at 85°C for 5 min in the presence of 50% formamide [31] . S1 nuclease digestion to remove single-stranded 32P-labeled RNA was performed at 37°C for 30 min in a buffer containing 5 mM sodium acetate ( pH 4 . 5 at 25°C ) , 0 . 28 M NaCl , 4 . 5mM ZnSO4 and 40 U S1 nuclease ( Boehringer ) . Fractionation of the whole cell extract was done according to [52] . The total extract was centrifuged at 21 , 000× g at 4°C for 10 min to separate the “soluble” ( supernatant ) and “membrane” ( pellet ) fraction . The pellet was re-suspended and washed with buffer A ( 30 mM HEPES-KOH pH 7 . 4 , 150 mM potassium acetate , and 5 mM magnesium acetate ) followed by centrifugation at 21 , 000× g at 4°C for 10 min and re-suspension of the pellet in buffer A . In vitro TBSV replication in the fractions was performed as described [31] . Expression and purification of the recombinant TBSV p33 and p92 and TCV p88C replication proteins from E . coli were carried out as described earlier with modifications [54] . Briefly , the expression plasmids were transformed separately into E . coli strain BL21 Rosetta ( DE3 ) . Protein expression was induced using isopropyl β-D-thiogalactopyranoside ( IPTG ) for 8 h at 16°C , then the cells were collected by centrifugation ( 5 , 000 rpm for 5 min ) . The recombinant TCV p88C protein was purified on an amylose resin column ( NEB ) , as described [54] . The cells were suspended and sonicated in MBP column buffer containing 20 mM Tris-Cl pH 8 . 0 , 150 mM NaCl , 1 mM EDTA , 10 mM β-mercaptoethanol and 1 mM phenylmethylsulfonyl fluoride ( PMSF ) . The sonicated extract was then centrifuged at 27 , 000 g for 10 min , followed by incubation with amylose resin ( NEB ) for 1 h at 4°C . After washing the resin 3 times with the column buffer and once with a low salt column buffer ( 25 mM NaCl ) , the proteins were eluted with a low salt column buffer containing 0 . 18% ( V/W ) maltose and 6% ( V/V ) glycerol and stored at −80°C . MBP-p33 and MBP-p92pol were purified as above , except 30 mM HEPES-KOH pH 7 . 4 was used instead of 20 mM Tris-Cl pH 8 . 0 . eEF1A was purified from yeast as described [76] and stored in aliquots at the vapor temperature of liquid nitrogen . Protein fractions used for the replication assays were 95% pure , as determined by SDS-PAGE . Yeast strains ( WT , C42 , C53 , C62 ) were transformed with plasmids pESCHIS4-ADH-HF33/CUP1-DI-72 expressing 6XHis- and Flag-tagged CNV p33 and the TBSV DI-72 repRNA , and pRS317-Tet-His92 , expressing CNV p92 under the control of Tet promoter [25] . Co-purification was done according to a previously described procedure with the following modification [25] . Briefly , 200 mg of yeast cells were resuspended and homogenized in TG buffer [50 mM Tris–HCl [pH 7 . 5] , 10% glycerol , 15 mM MgCl2 , 10 mM KCl , 0 . 5 M NaCl , 0 . 1% Nonidet P-40 ( NP-40 ) , and 1% [V/V] yeast protease inhibitor cocktail ( Ypic ) ] by glass beads using FastPrep Homogenizer ( MP Biomedicals ) . The yeast cell lysate was cleared by centrifugation at 500× g for 5 min at 4°C to remove unbroken cells and debris . The membrane fraction containing the viral replicase complex was collected by centrifugation at 21 , 000× g for 15 min at 4°C and then solubilized in 1 ml TG buffer with a buffer containing 1% NP-40 , 5% SB3–10 [caprylyl sulfobetaine] ( Sigma ) , 1% [V/V] Ypic via gentle rotation for 1 h min at 4°C . The solubilized membrane fraction was centrifuged at 21 , 000× g for 15 min at 4°C and the supernatant was incubated with 20 µl anti-FLAG M2-agarose affinity resin ( Sigma ) pre-equilibrated with 0 . 7 ml TG buffer . After 2 h of gentle rotation at 4°C , we washed the resin 5 times with TG buffer containing 1% NP-40 , the resin-bound replicase complex was eluted in 100 µl elution buffer [50 mM Tris–HCl [pH 7 . 5] , 10% glycerol , 15 mM MgCl2 , 10 mM KCl , 0 . 05 M NaCl , 0 . 5% Nonidet P-40 ( NP-40 ) , 1% Ypic and 0 . 15 mg/ml Flag peptide ( sigma ) ] . In vitro RdRp activity assay was performed by using DI-72 RI ( − ) RNA template transcribed in vitro by T7 transcription [25] . EMSA was performed in a 10 µl-reaction containing 20 mM HEPES [pH 7 . 6] , 50 mM KCl , 2 mM MgCl2 , 1 mM DTT , 0 . 1 mM EDTA , 10% [vol/vol] glycerol , 10 U of RNase inhibitor , 10 nM 32P-labeled DI-72 ( + ) RNA probe and 0 . 5 µg purified eEF1A protein [76] . Reactions were incubated at room temperature for 20 min and then resolved by 4% nondenaturing polyacrylamide gel as described previously [25] . For in vitro eEF1A-repRNA co-purification , DI-72 ( + ) repRNA was biotin-labeled in standard T7 transcription reaction in the presence of 20 µM Biotin-16-UTP ( Roche ) . After the T7 transcription , the unincorporated biotin-UTP was removed on a Bio-Rad mini gel filtration column . The biotinylated RNA was immobilized on a column containing Streptavidin MagneSphere Paramagnetic Particles ( SA-PMPs ) . Briefly , a 30-µl suspension of SA-PMPs ( Promega ) was washed three times with 1 ml of water and re-suspended in 1× Phosphate Buffered Saline ( PBS ) . Biotinylated DI-72 ( + ) RNA ( 5 µg ) was then added to the suspension of SA-PMPs , followed by 30 min incubation at 4°C with gentle rotation . The SA-PMPs were collected on the side of the tube in a magnetic stand and washed 3 times with 1× PBS buffer . eEF1A was translated in vitro and labeled with 35S methionine using Rabbit Reticulocyte Lysate ( Promega ) according to manufacturer's manual . The in vitro eEF1A translation product ( 10 µl ) was pre-incubated in a 200 µl binding buffer ( 20 mM HEPES [pH 7 . 6] , 50 mM KCl , 2 mM MgCl2 , 1 mM DTT , 1 mM GTP , 0 . 1 mM EDTA , 10% [V/V] glycerol , 1% BSA , 10 U of RNase inhibitor and 0 . 2% NP-40 ) with 150 µM Didemnin B ( final concentration ) or DMSO for 30 min at 30°C and then incubated with biotinylated DI-72 ( + ) RNA-bound SA-PMPs for 1 h at 4°C . The SA-PMPs were collected in a magnetic stand and washed 5 times with the binding buffer , followed by elution with 30 µl SDS-PAGE sample buffer . The eluted protein samples were resolved by SDS-PAGE and then exposed to phosphorimager . The TCV RdRp reactions were carried out as previously described for 2 h at 25°C [54] . Briefly , the RdRp reactions were performed in a 20 µl reaction containing 50 mM Tris-HCl ( pH 8 . 2 ) , 10 mM MgCl2 , 10 mM DTT , 1 . 0 mM each ATP , CTP , and GTP , 0 . 01 mM UTP plus 0 . 1 µl of [32P]UTP , 7 pmol template RNA , 2 pmol affinity-purified MBP-p88C . 20 pmol eEF1A was added to the reaction at the beginning or as indicated in the text and Fig . 3 legend . The 32P-labeled RNA products were analyzed by electrophoresis in a 5% or 15% PAGE/8 M urea gel [57] . The 86-nt 3′ noncoding region of TBSV genomic RNA was used as the template in the RdRp assay [25] , [54] . Purified Didemnin B ( NSC 325319 ) was kindly provided by the Natural Products Branch , NCI ( Bethesda , MD , USA ) , while Gamendazole was a generous gift from Dr . Tash ( University of Kansas Medical Center ) . Both chemicals were dissolved in DMSO ( the final concentration was 20 mM ) . The concentrations of chemical and time point of the addition of the chemicals to the in vitro reaction are indicated in the text . The cell-free TBSV replicase assay and the in vitro TBSV replicase assembly assay were performed according to [31] . Briefly , the purified recombinant TBSV p33 , p92pol and ( + ) repRNA were added to the cell-free reaction in the presence of 1 . 0 mM ATP and GTP in step 1 . After incubation at 25°C for 1 h , the in vitro reactions were centrifuged 21 , 000× g at 4°C for 10 min . The supernatant containing extra p33 , p92pol and repRNA , which were not bound to the membranes in the cell-free extract , was discarded , while the membrane pellet was re-suspended in a standard in vitro replicase assay buffer containing [32P]-UTP and ATP , CTP , and GTP , and incubated at 25°C for 3 h [31] . The TBSV viral RNA gets recruited to the membrane from the soluble fraction with the help of TBSV replication proteins and host factors present in the yeast CFE . The in vitro RNA recruitment reaction was performed according to [31] , except that 32P-labeled DI-72 ( + ) repRNA were used and rCTP , rUTP , 32P-labeled UTP , and Actinomycin D were omitted from the reaction . As a negative control , a recruitment-deficient repRNA , termed C99-G mutant , was used ( Fig . 4F , lane 2 ) [23] . This mutant RNA is not recognized by p33/p92 replication proteins and it does not replicate in plants , in yeast or in the CFE in vitro [23] , [31] , [52] , [77] . The RNA recruitment assay results in the assembly of the functional viral replicase , when wt repRNA is used , and nonfunctional replicase when the C99-G mutant is used in the assay ( J . Pogany and P . D . Nagy , not shown ) [31] . Inhibitors DB and GM were added at final concentration of 150 and 100 µM , respectively . After two hours of incubation at room temperature , 1 ml of reaction buffer was added to the in vitro assay , followed by incubation on ice for 10 min . Samples were centrifuged at 35 , 000× g for 1h , and the pellet was washed with 1 ml reaction buffer , followed by centrifugation at 35 , 000× g for 10 min . The membrane-bound repRNA was extracted from the pellet by adding 0 . 1 ml stop buffer and 0 . 1 ml phenol/chloroform and vortexing , followed by isopropanol/ammonium acetate precipitation [52] . The RNA samples were analyzed by denaturing PAGE and phophoimaging as described [52] .
|
Plus-stranded RNA viruses are important pathogens of plants , animals and humans . They replicate in the infected cells by assembling viral replicase complexes consisting of viral- and host-coded proteins . In this paper , we show that the eukaryotic translation elongation factor ( eEF1A ) , which is one of the resident host proteins in the highly purified tombusvirus replicase complex , is important for Tomato bushy stunt virus ( TBSV ) replication in a yeast model host . Based on a random library of eEF1A mutants , we identified eEF1A mutants that either decreased or increased TBSV replication . In vitro studies revealed that eEF1A facilitated the recruitment of the viral RNA template for replication and the assembly of the viral replicase complex , as well as eEF1A enhanced viral RNA synthesis in vitro . Altogether , this study demonstrates that eEF1A has several functions during TBSV replication .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"virology/mechanisms",
"of",
"resistance",
"and",
"susceptibility,",
"including",
"host",
"genetics",
"virology/viral",
"replication",
"and",
"gene",
"regulation"
] |
2010
|
Translation Elongation Factor 1A Facilitates the Assembly of the Tombusvirus Replicase and Stimulates Minus-Strand Synthesis
|
Mechanisms behind how the immune system signals to the brain in response to systemic inflammation are not fully understood . Transgenic mice expressing Cre recombinase specifically in the hematopoietic lineage in a Cre reporter background display recombination and marker gene expression in Purkinje neurons . Here we show that reportergene expression in neurons is caused by intercellular transfer of functional Cre recombinase messenger RNA from immune cells into neurons in the absence of cell fusion . In vitro purified secreted extracellular vesicles ( EVs ) from blood cells contain Cre mRNA , which induces recombination in neurons when injected into the brain . Although Cre-mediated recombination events in the brain occur very rarely in healthy animals , their number increases considerably in different injury models , particularly under inflammatory conditions , and extend beyond Purkinje neurons to other neuronal populations in cortex , hippocampus , and substantia nigra . Recombined Purkinje neurons differ in their miRNA profile from their nonrecombined counterparts , indicating physiological significance . These observations reveal the existence of a previously unrecognized mechanism to communicate RNA-based signals between the hematopoietic system and various organs , including the brain , in response to inflammation .
The influence of the immune system on the brain in the context of inflammation is highly relevant for a number of diseases , yet mechanisms for this interaction are not fully understood . The stereotypical response is the secretion of pro-inflammatory cytokines by immune cells . These peripheral cytokines in turn can have a direct effect on neural cells or activate brain inflammatory cytokine signaling , usually via microglia , the principle innate immune cells of the brain [1] . Recently , heterotypic cell fusion of hematopoietic cells with Purkinje neurons in the brain has been suggested as a conceptually different mechanism of response to inflammation . When transplanted into lethally irradiated mice , genetically labeled hematopoietic donor cells have been found to contribute to a number of host tissues including skeletal and cardiac myofibers , hepatocytes in the liver , intestinal crypt cells , and Purkinje neurons in the brain . Initially seen as evidence for an unexpected differentiation potential of hematopoietic stem cells , it was eventually demonstrated that the experimentally observed plasticity was largely attributable to cell fusion , rather than transdifferentiation [2]–[4] . For the brain , fusion of hematopoietic cells has so far mainly been reported with Purkinje neurons . Although the number of fusion events is very low in the healthy animal , peripheral inflammation induces cell fusion events to increase by a factor of 10–100 , giving the first indication that heterotypic fusion is regulated by a pathologic stimulus and may therefore be of biological significance [5] . We were interested in studying the contribution of hematopoietic cells to neural tissue without the accompanying confounding factors such as lethal irradiation , chemoablation , or parabiosis normally associated with replacing the host bone marrow . In contrast , to irreversibly label hematopoietic cells to follow their fate in vivo , we used transgenic mice expressing Cre recombinase under the hematopoietic-specific promoter vav [6] in a Cre reporter background . Although in our mouse model we could observe recombination events in the same tissues and at a frequency comparable to those observed in transplantation studies , we did not find any evidence for cell fusion in Purkinje neurons , marked by the absence of a second nucleus [7] . We now show that recombination in neural cells is caused by the intercellular transfer of Cre recombinase messenger RNA . More specifically , biochemical analysis demonstrates that Cre mRNA is contained in extracellular vesicles ( EVs ) , including exosomes . These EVs are sufficient to induce recombination in neural cells after direct intracerebellar injection . Recombination events occur rarely and are restricted to very few Purkinje neurons in the healthy animal . However , when inducing a peripheral inflammation or an entorhinal cortex lesion ( ECL ) , the number of recombined cells increases dramatically and extends to other neuronal cell populations . Importantly , recombined versus nonrecombined Purkinje neurons display differences in their miRNA profile several days after inflammation , indicating biologically significant changes . These observations reveal the existence of a previously unrecognized mechanism to communicate RNA-based signals between the hematopoietic system and various organs , including the brain , in response to inflammation .
To monitor the contribution of hematopoietic cells to other tissues without any of the confounding factors associated with host bone marrow replacement , we previously utilized transgenic mice expressing Cre recombinase specifically in the hematopoietic lineage under the Vav1 promoter [7] . For this study , we additionally included a mouse model expressing Cre under the hematopoietic- and endothelial-specific promoter Tie2 [8] to minimize the possibility of false positive results due to a leaky expression of Cre . Thus , in hematopoietic cells , marker gene expression is irreversibly induced ( Figure 1A ) , allowing the tracing of hematopoietic contribution to any other tissue . Confocal analysis of different tissue sections of both Vav-iCre- and Tie2-Cre-GFP/LacZ mice showed GFP- or LacZ-positive cells in organs such as the liver , lung , and small intestine ( Figure 1B–D ) similar to observations made in animals transplanted with bone marrow from constitutively marker-gene-expressing cells . In the cerebellum , only Purkinje neurons could be observed expressing the marker gene ( Figure 1E ) . However , all marker-gene–positive Purkinje neurons from both mouse lines contained only a single nucleus in line with our previous findings [7] , suggesting a contribution of hematopoietic-to-neural cells independent of cell fusion ( Figure 1F ) . Thus , in both our transgenic models we could make observations similar to what had been reported for chimeric animals but with no evidence of cell fusion in neurons . The observation of Cre-dependent reporter gene expression without evidence of cell fusion events led us to consider alternative explanations for a transfer of Cre recombinase from hematopoietic cells to neurons . A growing body of evidence suggests a role for EVs in cell-to-cell communication [9] . Moreover , EVs can transfer functional mRNA between cells in vitro [10] . We therefore asked if reporter gene expression in nonhematopoietic cells was the result of an RNA-based transfer of Cre recombinase via EVs , leading to the translation of Cre RNA in target cells and nuclear recombination followed by irreversible reporter gene expression . Because low levels of recombination events could be detected in uninjured animals , we hypothesized that Cre messenger RNA should be detectable in the blood of mice with hematopoietic Cre recombinase expression . Peripheral blood was drawn from six Vav-iCre mice and processed according to a protocol for the isolation of secreted membrane vesicles [11] . In all vesicle isolation experiments , the pellet was treated with RNaseA to remove RNA that was not contained in vesicles . RNA was extracted from vesicle preparations and reverse transcribed , followed by nested primer RT-PCR . Cre recombinase cDNA could be detected in four out of six analyzed blood samples ( Figure 2A ) , showing that Cre mRNA is present at low levels in blood plasma . To obtain larger amounts of secreted membrane vesicles for a more detailed analysis , we prepared in vitro cultures from pooled peripheral blood and bone marrow of Vav-iCre mice ( n = 6 separate preparations from one to two mice each ) and added lipopolysaccharide ( LPS ) at 200 ng/ml to stimulate an inflammatory reaction . Cell culture supernatants were collected after 3 d of stimulation , and membrane vesicle fractions enriched for exosomes were prepared via differential ultracentrifugation . The resulting supernatants and pellets were used for RNA extraction , reverse transcription , and subsequent detection of cDNA using PCR . Consistently , Cre recombinase cDNA could be detected in the vesicle pellet but not in the supernatant ( Figure 2B ) in all experiments , suggesting that Cre mRNA was not part of the soluble cell culture medium but was contained in physical structures amenable to separation by centrifugation . To directly show that Cre mRNA is contained in vesicles and does not sediment because of an association with protein complexes , we took vesicle pellets prepared as above and treated them with RNaseA alone or in combination with detergent . In this way , vesicles would be lysed and the RNA exposed to RNase digestion . Indeed , in the samples treated with detergent and RNAse , we could no longer detect Cre mRNA in contrast to vesicle pellets that were treated with RNAse alone ( n = 2 independent sample preparations ) ( Figure 2C ) . Electron microscopic images from our vesicle preparations showed predominantly structures with sizes between 50 and 100 nm and a round or cup-shaped morphology typically associated with exosomes ( Figure 2D ) [12] . EVs are a heterogeneous population and their typology is not established with regard to their possible biological functions . However , the most prominent subclass are exosomes as defined by their size , protein marker load , and enrichment profile by differential density ultracentrifugation [9] . To specifically determine whether exosomes contain Cre mRNA , we fractionated the vesicle preparations using sucrose density gradient ultracentrifugation [13] . Exosomal identity was confirmed by Western blot analysis for the specific surface markers ADAM10 and CD9 ( Figure 2E , F ) . Using antibodies against Cre recombinase , we could not detect Cre recombinase protein in any of the fractions ( Figure 2G ) . We could also not detect any Cre protein by ELISA with a sensitivity of 0 . 1 ng/ml in total EV preparations of bone marrow and peripheral blood of a single mouse ( unpublished data ) . Thus , our EV preparations do not contain Cre protein or only at exceedingly low concentrations . Cre recombinase RNA could always be detected by RT-PCR in all exosomal fractions ( Figure 2H ) . Nonexosomal fractions varied with regard to the presence of Cre mRNA , ranging from its complete absence ( Figure S1A ) to individual subfractions being positive for Cre-derived cDNA ( n = 5 preparations ) ( Figure 2H ) . The latter observation indicated either trace amounts of exosomes in fractions negative for exosomal markers or Cre mRNA containing nonexosomal vesicles . Quantitative analysis of vesicle number and size revealed an increase in the share of larger membrane blebs or apoptotic vesicles in the in vitro preparations compared to blood plasma ( Figure S1B ) . Our results demonstrate that Cre mRNA but not Cre recombinase protein is contained predominantly in exosomes and suggests that functional Cre recombinase protein was generated from Cre RNA contained in vesicles rather than recombination activity being a result of the direct transfer of Cre protein . We wanted to test whether secreted Cre recombinase RNA-containing EVs are sufficient to induce recombination in Purkinje neurons in vivo . To this end , EVs enriched for the exosomal fraction were prepared from peripheral blood and bone marrow cultures from Vav-iCre mice as described above . These preparations were brought into the circulation of ROSA26-lacZ reporter mice intravenously ( Figure 3A ) . Four days after injection , animals were transcardially perfused and analyzed for recombination events by X-Gal staining and immunohistochemistry in serial sections of the cerebellum . We could not observe recombination events in any of the brains of the animals analyzed ( n = 4 , unpublished data ) . These results suggested either that the amount of exosomes in our preparations was too low , leading to the quantitative absorption of vesicles en route to the brain , or that EVs could not cross the blood–brain barrier ( BBB ) . To circumvent these problems and to test whether induction of reporter gene expression in Purkinje neurons by Cre mRNA-containing EVs is formally possible , we directly injected 1 µl vesicle preparations into the cerebella of ROSA-LacZ reporter mice ( n = 3 , two injections in a single hemisphere of each mouse; Figure 3A ) . Serial sections of the complete cerebellar hemisphere were analyzed for reporter gene expression 4 d after injection . This analysis identified Purkinje neurons expressing the reporter gene in all three injected animals with one to seven recombined Purkinje neurons in a single hemisphere ( Figure 3B ) . Interestingly , we also observed other reporter gene-positive cell types with glial ( Figure 3C ) or microglial ( Figure 3D ) morphology in addition to Purkinje neurons in all injected animals . Hence , Cre mRNA-containing EV preparations are sufficient to induce recombination events in the brain , and the transfer of functional Cre mRNA by EVs appears not to be necessarily restricted to Purkinje neurons . Control brains from ROSA-LacZ mice that were injected with purified recombinant Cre recombinase protein ( 1 µl of 1 U/µl ) ( n = 4 ) or lysate prepared from Vav-iCre bone marrow cells ( BMCs ) ( n = 2 ) did not display reporter gene expression in Purkinje neurons or any other cell types ( Figure 3E ) . To formally rule out the possibility that the recombination events we observe were resulting from an unspecific expression of Cre , we performed additional control experiments . First , we established cerebellar slice cultures from Vav-iCre- or Tie2-Cre-ROSA-LacZ/GFP mice ( n = 4 ) . After 2 d in culture , we induced an injury by transversally cutting into the tissue with a blade , and 3 d later slices were analyzed . We could only detect occasional recombination events in the Purkinje cell layer and no recombination in the injured areas , indicating a lack of Cre activity in the absence of a hematopoietic system . Next , we generated blood chimeras by transplanting Vav-iCre-GFP bone marrow into lethally irradiated ROSA-LacZ reporter mice ( n = 6 ) . In this way , we could identify cells that received Cre recombinase from the donor population while controlling for the possibility of cell fusion through the constitutive expression of GFP in the donor cells . After 5 wk or 2 mo , respectively , we induced peritonitis and 4 d later the chimeric animals were killed ( for an experimental scheme , see Figure 4A ) . We could observe LacZ-positive , GFP-negative cells in the livers of all animals ( n = 6; Figure 4B ) . In the cerebellum , recombined cells became apparent only in the longer surviving animals ( 2 mo , n = 3 ) . We could identify β-galactosidase–positive , GFP-negative cells in the Purkinje layer ( 3 . 3±1 . 2 SD in one hemisphere ) but also in the granular layer ( 5±1 . 7 SD in one hemisphere ) as well as in cells associated with blood vessels ( Figure 4C–E ) . In the latter two locations , we never observed recombined cells in the Vav-iCre/Tie2-Cre-GFP/LacZ mice , indicating physiological changes caused by the irradiation . To test whether we could still observe recombination events in chimeras without the confounding influence of lethal irradiation , we performed adoptive transfer experiments whereby 20 million spleen/lymph node cells from Vav-iCre-GFP mice were i . v . injected into ROSA-LacZ reporter mice ( n = 8 ) . Ten days after the transfer , animals were killed and their livers and brains analyzed . Although we could detect recombined , GFP-negative cells in the livers of four mice , we did not observe any recombination in the brain , probably due to the low and short-lived cell number present in the recipients ( Figure 4A ) . Control animals for both types of experiments included ROSA-LacZ reporter mice as recipients injected with wild-type donor cells ( n = 4 ) . In sum , these experiments present formal evidence of a lateral transfer of Cre mRNA originating from blood cells , excluding fusion or endogenous misexpression . Hematopoietic cells of the myeloid and lymphoid lineage have been shown to contribute to nonhematopoietic tissues by heterotypic cell fusion [14] . These fusion events are very rare , but their number increases substantially after peripheral inflammation [5] , leading to speculation that heterotypic fusion and subsequent reprogramming of the hematopoietic nucleus represent a sort of rescue mechanism for damaged tissues [15] . Likewise , inflammatory stimuli may affect EVs signaling [16] , [17] . We induced a chronic inflammation by injecting Lewis lung carcinoma cells ( LLC2 ) into the flanks of double transgenic mice , leading to the formation of a peripheral tumor after 12 d . For an acute inflammation , we induced peritonitis by intraperitoneal injection of thioglycolate broth and killed the animals 4 d after injection . In both models , we observed a significant increase in the number of recombined Purkinje neurons of up to three orders of magnitude ( Figure 5A–C ) . To complement these models of a systemic inflammation with a pathology that directly affects neural tissue , we induced an ECL [18] in Vav-iCre-LacZ mice ( n = 3 ) , and 4 d postlesion , we observed a significant increase in the number of recombined Purkinje neurons ( Figure 5C ) . Altogether , on the basis of a total population of approximately 150 , 000 Purkinje neurons per cerebellum , the relative quantity of recombined Purkinje neurons after inflammation thus averages 5 . 8% but goes as high as 26% in one individual case . Importantly , none of the recombined cells that we had analyzed in more detail were binucleated ( Figure 5D , E ) , indicating that in our model systems injury is not sufficient to induce heterotypic cell fusion . The numbers of recombined Purkinje neurons for both Cre mouse lines were comparable to increases observed in heterologous transplantation models [5] , [14] . Of note , when screening a large number of sections of postmortem human cerebellar tissue from patients ( n = 12 ) that suffered from severe inflammatory injuries , we could very rarely detect Purkinje neurons containing two nuclei ( Figure S2 , Table S1 ) , consistent with observations made in our transgenic mouse models . Microglia are the main immune cells of the brain and are able to secrete EVs [19] . Therefore , we examined whether this cell type could be a possible source of Cre RNA-containing EVs in our model . We first analyzed brains from early postnatal ( P4–P8 ) double transgenic Vav-iCre/Tie2-Cre-LacZ mice—the peak time of microglia invasion to the brain . In line with recent findings that the origin of microglia precedes definitive hematopoiesis [20] , none of the microglia were positive for the reporter gene ( unpublished data ) . Equally , we did not observe microglia expressing the reporter gene either in the healthy adult brain or after inflammatory injuries ( Figure 5F , G ) . Thus , in our model , Cre-mediated recombination activity in neurons is not induced by EVs of microglial origin . To gain insight into whether blood-derived EVs reach Purkinje neurons directly via the circulation or by entry of leukocytes into the brain and subsequent local release of EVs , we screened serial cerebellar sections from mice with peritonitis for CD45-LacZ double-positive cells ( n = 6 ) . We did not detect a single double-positive cell in the brain parenchyma , arguing against a transfer of EVs from leukocytes to neurons over a short distance . Next , we wanted to test whether EVs themselves may influence BBB properties in order to facilitate their passage into the brain . To this end , we measured changes in the electric resistance and capacitance over a monolayer of bEnd5 brain endothelium cells , a system that has been previously described as an adequate approximation of BBB properties in vitro [21] . EVs from three separate preparations from the supernatant of BMCs were added to the transwells ( three separate measurements in quadruplicate ) . The supernatants of these EV preparations after the ultracentrifugation step served as negative controls . Addition of EVs , but not the supernatant , led to a decrease in the resistance and a concomitant increase in the capacitance at 24 h and 48 h but not any more at 72 h ( Figure 5H , I ) . This suggests that EVs from the bone marrow are sufficient to make the BBB more permeable without lasting toxic effects . Heterotypic cell fusion in the brain induced by peripheral inflammatory injury has only been reported in Purkinje neurons [5] , [22] , [23] . However , because inflammation contributes to various neural pathologies such as epilepsy [24] , neurodegenerative diseases [25] , [26] , and sickness behavior [27] , we tested whether other neuronal populations apart from Purkinje neurons could display Cre-mediated marker gene expression . Analyzing mice with an inflammation revealed different brain areas with recombined neurons . These were tyrosine hydroxylase ( TH ) -immunoreactive dopaminergic neurons in the substantia nigra/ventral tegmental area ( SN/VTA ) ( Figure 6A , B ) . For this area , we could also observe recombined cells with a neuronal morphology that were negative for TH ( Figure 6C ) , indicating loss of TH expression due to inflammation . Additionally we detected recombination in cortical neurons ( Figure 6D–F ) and in the granular cell layer of the hippocampus ( Figure 6G–I ) . In the animals with an ECL , neurons in the hippocampal areas CA1 and CA3 were recombined as well as cells at the lesion site that were neither of a neuronal nor astrocytic lineage ( Figure 6J–L ) . All neurons displaying marker gene expression contained only one nucleus , consistent with our observations in the cerebellum that cell fusion between hematopoietic cells and neurons is not the reason for the observed recombination events . Next we wanted to test if an increase in recombination levels in Purkinje neurons is restricted to our injury model or constitutes a general response to any type of injury . Thus , we analyzed if a lesion that is specific for a selected group of neurons and that does not induce a systemic inflammation is capable of increasing recombination events in the Purkinje neuron population . To this end , we injected 1-methyl-4-phenyl-1 , 2 , 3 , 6-tetrahydropyridine ( MPTP ) into Vav-iCre-LacZ mice ( n = 3 ) . MPTP is converted to the toxin MPP+ ( 1-methyl-4-phenylpyridinium ) in glial cells that is then released and taken up specifically via the dopamine transporter of dopaminergic neurons , leading to cell death by inhibiting mitochondrial complex I . Animals were intraperitoneally injected with MPTP ( 30 mg/Kg body weight per day ) on 5 consecutive days . Two weeks after the last injection , animals were killed and their brains analyzed . MPTP treatment leads to a massive reduction in the number of dopaminergic neurons in the SN , more than in the VTA ( Figure S3 A , B ) . As a result , we did not observe an increase in the number of recombined Purkinje neurons in the cerebellum comparable to levels seen after peripheral inflammation ( 53 . 3±14 . 7 SD labeled Purkinje neurons per cerebellar hemisphere , n = 3 ) . Therefore , an increase in recombined Purkinje neurons seems to be more injury-specific , particularly for a peripheral inflammation and not to limited mechanical damage , similar to findings in the context of cell fusion where mechanical trauma did not lead to an increase in heterokaryon formation [5] . Interestingly , we could detect numerous TH-negative LacZ-positive cells with neuronal morphology in the SN/VTA ( Figure S3C–E ) . This could indicate an effect of a cell-type-specific lesion on a heightened signaling of blood-derived EVs , but we did not formally test this possibility . Finally , we wanted to test whether the EV transfer of Cre mRNA to Purkinje neurons is accompanied by other cellular changes that may indicate a more than transient physiological response . EVs and exosomes contain short noncoding RNAs including microRNAs ( miRNAs ) , and it has been shown in vitro that functional miRNAs can be transferred by exosomes to target cells where they exert their effects on gene regulation [10] , [28] . Furthermore , miRNAs are abundant in the nervous system , and there is compelling evidence for a role of miRNAs in all aspects of neuronal function from development to plasticity in the adult brain [29] . Hence we asked whether we could observe differences in the miRNA profile in recombined Purkinje neurons compared to their nonrecombined counterparts . To this end , we induced peritonitis in two Vav-iCre-LacZ mice . Four days later , these animals were killed and their cerebella snap-frozen and serially cut . After brief fixation , sections were stained for X-Gal and screened for marker-positive Purkinje neurons on a microscope fitted for laser capture microdissection ( LCM ) . Cells that were X-Gal–positive and clearly identifiable as Purkinje neurons based on morphology and location were cut out and collected . To obtain a reference population that was as similar as possible , for each recombined cell a corresponding neighboring X-Gal–negative Purkinje neuron ( <5 neuron distance ) was cut out and collected in a separate tube ( Figure 7A ) . We collected 100 and 50 pairs of recombined/nonrecombined neurons . Material obtained in this way showed little RNA fragmentation as well as high specificity for Purkinje neurons ( Table S2 ) . Next , miRNAs were isolated from all four samples and analyzed by miRNA qPCR arrays . Pairwise comparison of the miRNAs from recombined neurons with their nonrecombined counterparts revealed significant differences in the presence or absence of various miRNAs ( Figure 7B , Table S3 ) as well as changes in expression levels of common miRNAs ( Figure S4 ) . Comparing the miRNAs that were present in both sets of recombined neurons but not in any of the nonrecombined neurons yielded three miRNAs ( Box in Figure 7C ) . Interestingly , of these miRNAs , miR-574-3p is highly expressed in myeloid cells and can be isolated from blood plasma [30] but is not present in previously published miRNA libraries specifically prepared from Purkinje neurons [31] . Additionally , we wanted to analyze whether an inflammation alters the composition of miRNAs contained in circulating serum exosomes . Therefore , we isolated serum exosomes from a group ( n = 20 ) of mice 24 h after induction of peritonitis as well as from an untreated control group ( n = 19 ) . The vesicle preparations for each group were separated by sucrose density gradient ultracentrifugation as described above , and only the exosomal fractions were used for further analysis . miRNAs were isolated from both preparations and analyzed on a miRNA qPCR array . The miRNA profiles of the two samples indicated significant changes in miRNA composition in response to an inflammatory stimulus ( Figure 7D , Table S4 ) . All of the three miRNAs identified specifically in the recombined Purkinje neurons above were contained in the exosomal miRNA population .
Our current study reveals a previously unrecognized mechanism behind how the hematopoietic system may influence cellular processes in the brain in response to inflammation . Our results show that EVs , including exosomes , are released from hematopoietic cells and transfer functional RNA to cells of multiple tissues , including Purkinje neurons in the brain . This EV transfer is a relatively rare event under nonpathological conditions but can rapidly increase by orders of magnitude after peripheral inflammation . It is not restricted to Purkinje neurons in the cerebellum but includes multiple other neuronal types , notably dopaminergic neurons in the SN . Importantly , Purkinje neurons that receive EV RNA , measured by Cre-mediated induction of reporter gene expression , display a different miRNA profile compared to their nonrecombined counterparts , indicating physiologically relevant changes . Exosomes are increasingly recognized as an agent that can transmit genetic information between cells , but their analysis has largely been restricted to in vitro studies . Therefore , the finding that functional Cre mRNA can be transferred by exosomes leading to loxP excision in target cells will serve as a valuable tool to understanding the physiological role of exosomes in vivo . However , our results also caution against a lateral transfer of Cre mRNA as a possible confounding factor when using the Cre-lox system for lineage analysis in vivo . In this study , we used two transgenic mouse lines expressing Cre recombinase in hematopoietic cells and one additionally in the endothelial lineage . Although in the Vav-iCre mouse Cre is constitutively expressed in virtually all hematopoietic cells [6] , [32] , Tie-2 expression in the adult animal is generally associated with endothelial cells and tumor-infiltrating proangiogenic monocytes [33] . However , its expression has also been described or suggested in other hematopoietic cell types [34] . Additionally , cells may up-regulate Tie-2 in response to inflammatory injury in yet other subpopulations . Based on the similarity of marker gene expression in both mouse models , we rule out endothelial cells as a source for EVs . In contrast , mouse lines expressing Cre under the nonhematopoietic promoters for nestin , myelin oligodendrocyte glycoprotein , and glycine transporter 2 did not show any marker-gene–positive Purkinje neurons , further arguing against unspecific Cre expression ( unpublished data ) . Importantly , the fact that we do not observe microglia expressing the marker gene in both animal models demonstrates that a vesicular transfer to neurons does not involve the participation of this cell type . Neither are microglia the target of peripheral blood-derived secreted membrane vesicles , although our results from the intracerebellar injection of Cre RNA-containing exosomes as well as published work [35] suggest that microglia can in principle take up EVs . Irrespective of the cell of origin , horizontal transfer of genetic material by EVs may occur in two fundamentally different , but not necessarily mutually exclusive , ways: EV release by hematopoietic cells into the blood stream increases in response to a peripheral inflammation . They then reach the different organs via the blood supply where they are taken up by local cells ( Figure 8A ) . The specificity of this uptake is unknown , but work by others generally shows that EV uptake can be membrane-receptor-mediated [36] or occur by phagocytosis [37] . The essential question in this model though would be how EVs are able to pass through the BBB . As a second possibility , leukocytes enter the brain and secrete exosomes that are taken up by cells in close apposition to the secreting cells ( Figure 8B ) . The fact that we do not find any marker-gene–positive leukocytes in the cerebellar parenchyma together with the presence of Cre mRNA-containing vesicles in the peripheral blood of Vav-iCre mice suggests to us that the first route , via EVs in the circulation , is the more likely scenario . Together with the results from direct injection of Cre-containing EVs into the cerebellum , this also argues against other conceivable mechanisms enabling a local Cre transfer—for example , via tunneling nanotubes or gap junctions . However , our observations do not rule out instances of such a local transfer by leukocytes after entering the brain . Particularly for more severe injuries and pathologies such as lethal irradiation and induced autoimmune encephalitis that were used in studies of cell fusion , a leukocyte entry into the CNS parenchyma is known [38] . Our results from the ECL indeed indicate that local transfer of EV Cre mRNA also could occur . The similarities in recombination pattern , frequency , and induction by inflammation compared to the phenomenon of heterotypic cell fusion are intriguing . We reason that in both cases lipid bilayer membranes need to fuse and that the fusion of whole cells might represent an extreme case of vesicle-cell fusion , triggered by the experimental conditions . The fact that none of the recombined neurons in our inflammation models was binucleated underlines the fact that an inflammatory stimulus as such is not sufficient to lead to cellular fusion , in contrast to reports using various experimental approaches to generate blood chimeras [5] , [14] . The transfer of functional genetic material via EVs and concomitant changes in miRNA content of the receiving cells that we observe suggests a novel way in how the hematopoietic compartment may interact with multiple organs , including the brain , which deserves further study . Peripheral infections and inflammation have been linked to processes such as cancer [39] , neurodegenerative diseases [1] , and sickness behavior—that is , changes in cognitive functions due to peripheral infections [27] . So far , this link has been mainly explained as cytokine-mediated effects . Increasing evidence points to a central role of miRNAs in regulating neuronal function . In this respect our data open the possibility of a simultaneous lateral transfer of multiple miRNA species from blood cells via EVs as an additional factor contributing to inflammation-induced reactions . Interestingly , although the miRNA profile of nonrecombined Purkinje neurons from our analysis almost completely overlaps with published libraries specifically prepared from Purkinje neurons based on pcp2 expression [31] , we find significant differences in comparison to the miRNAs contained in the recombined Purkinje neurons ( Table S5 ) . EV-derived miRNAs could act as a general stabilizer of transcription to compensate effects of cellular stress [40] . A recent report of oligodendrocyte-derived EVs increasing resistance of neurons against different types of stress in vitro could be a reflection of this function [41] . With regard to specific functions of individual miRNAs , interestingly miRNA-574-3p that we detect only in recombined Purkinje neurons as well as in exosomes from peripheral blood regulates and in turn is regulated by TAR DNA-binding protein 43 ( TDP-43 ) [42] , [43] . TDP-43 is a heterogeneous nuclear ribonucleoprotein involved in RNA processing and stability and has a central role in neurological diseases such as amyotrophic lateral sclerosis and frontotemporal lobar degeneration as the main component of brain inclusions [44] . In the context of these diseases , miR-574-3p could be detected in the serum but also in cerebrospinal fluid , with a down-regulation in cerebrospinal fluid of affected patients [43] . miR-484 has been reported to regulate mitochondrial fission [45] , and mitochondrial fission is involved in oxidative stress , apoptosis , and many neurological diseases . Finally miR-16 has been demonstrated to be a repressor for the serotonin transporter and is implicated in the therapeutic action of the antidepressant fluoxetine [46] . Furthermore it is associated with neuronal differentiation [47] . Therefore , published evidence about the miRNAs that we identify in recombined Purkinje neurons is consistent with a functional role in neurons . Although we want to stress that our results are not formal proof for a direct transfer of miRNAs via EVs and that the presence of these miRNAs could also be the result of an indirect regulation , they nonetheless highlight the possibility of how miRNAs in body fluids may reach target cells in various organs , specifically neurons in the brain . Recently , it has been demonstrated that peripherally injecting exosomes containing exogenous siRNA against GAPDH and engineered to specifically target neurons could indeed lead to down-regulation of their target gene in the brain [28] . Our data suggest that a more widespread gene regulation via changes in miRNA content in neurons naturally occurs in vivo . Finally , there is increasing evidence that key proteins in the pathology of neurodegenerative diseases such as α-synuclein or tau can associate with exosomes [48] , [49] or can be intercellularly transferred by as yet unknown mechanisms in Parkinson's disease [50] , [51] . In this context , our findings extend the possibility of a spreading of diseases between tissues with the involvement of EVs and provide an accessible method for further studies in this exiting field .
All animal work has been conducted according to relevant institutional guidelines of the Frankfurt University Medical School ( ethical permission Gen . Nr . F94/19 ) , and the Philipps University Marburg . Vav-iCre , and Tie2-Cre transgenic mice used in this study have been previously published [6]–[8] . Both Cre lines were crossbred with Rosa26-LacZ ( JAX-mice stock number 003309 ) or Rosa26-EGFP Cre reporter mice ( JAX-mice stock number 004077 ) . Genotyping was performed as described previously . In brief , PCR of tail biopsy lysates was performed using the following primers: iCre F , 5′-CTCTGACAGATGCCAGGACA-3′ , R , 5′-ACACCATTCTTTCTGACCCG-3′; Cre F , 5′-GCCTGCATTACCGGTCGATGCAACGA-3′ , R , 5′-GTGGCAGATGGCGCGGCAACACCATT-3′; and LacZ 1 F , 5′-GCGAAGAGTTTGTCCTCAACC-3′ , 2 F , 5′-GGAGCGGGAGAAATGGATATG-3′ , R , 5′-AAAGTCGCTCTGAGTTGTTAT-3′ . Amplificates were visualized on a 2% agarose gel . To induce peripheral tumors , 250 , 000 LLC2s resuspended in 200 µl of PBS were injected subcutaneously into both flanks . Tumors were let to grow for a maximum of 12 d . Peritonitis was induced by a single i . p . injection of thioglycolate broth ( Merck ) ( 1 ml 3% in PBS ) . Animals were killed 4 d after injection . Unilateral transection of the left perforant path was performed as described previously [52] . Briefly the left medial entorhinal cortex was lesioned using a 2 mm broad stainless steel blade . The medial edge of the knife was adjusted to the following coordinates as measured from the λ: anterior–posterior , 0 . 4 mm; lateral , 1 . 2 mm; dorsoventral , down to the base of the skull . Animals were killed 4 d after the surgery . Brains from Vav-iCre/Tie2-Cre-ROSA26Lacz/GFP mice were removed immediately after cervical dislocation and placed in ice-cold culture medium ( DMEM , 10% FCS with added antibiotics ) . Slice cultures were prepared as described [53] . Briefly brain hemispheres were embedded in 2% low melting point agarose and cut in 300 µm sections on a vibratome ( Leica ) . The other hemisphere was directly placed in 4% paraformaldehyde ( PFA ) and stored for later comparison of recombined cells . Two to three slices were transferred to one membrane insert ( PICM0RG50; Millipore ) in six-well plates and incubated in a humidified 5% CO2 atmosphere at 37°C . On the next day , culture medium was replaced with serum-free Neurobasal medium , 2% B27 , HEPES buffer , and antibiotics . For an injury , slices were cut transversally with a scalpel blade . Three days later , slices were fixed on their membrane inserts with 4% PFA at 4°C overnight . Deeply anesthetized mice were perfused transcardially with PBS followed by 4% cold PFA in PBS . All organs were postfixed in 4% PFA in PBS for 24 h . For cryosectioning , organs were cryo-protected in 15% sucrose for an additional 24 h before they were embedded and sectioned ( 10–12 µm ) as a frozen block . Sagittal cerebellar sections ( 50–60 µm ) were cut on a vibratome ( Leica ) and kept in PBS at 4°C . Whole blood withdrawal was performed on deeply anesthetized Vav-iCre mice via cardiac puncture . After blood withdrawal , mice were killed by cervical dislocation . Legs were removed in order to obtain BMCs . Erythrocytes were depleted using red blood cell lysis buffer ( Roche ) . Cells were flushed through a 70 µm cell strainer and washed with PBS before culturing them in serum-free IMDM supplemented with 5% Panexin-NT ( PanBiotech ) , 100 µg streptomycin , 100 U/ml penicillin , glutamine , and 200 ng/ml LPS . Cells were seeded at a density of 5×106 cells/ml for 48–96 h at 37°C and 5% CO2 . From hematopoietic cell culture , the supernatant was collected and processed by differential centrifugation as described [54] , with additional filtering ( 0 . 22 µm ) of the supernatant preceding the ultracentrifugation steps . For sucrose density gradient fractionation , vesicles were loaded on top of a stepwise sucrose gradient at the following concentrations: 21M , 1 . 3 M , 1 . 16 M , 0 . 8 M , 0 . 5 M , and 0 . 25 M in TBS as described previously [54] . The gradient was centrifuged for 2 . 5 h at 100 , 000×g using a Beckman SW40 Rotor . Twelve 1 ml fractions were collected from the top of the gradient , diluted with PBS , and subjected to ultracentrifugation . The pellet was taken up and used for RT-PCR . For blood plasma and serum , peripheral blood was drawn by heart puncture from deeply anesthetized mice . Vesicle preparation was performed similar to preparations from the cell culture supernatant with the necessary elongation steps for more viscous liquids as described [11] . For blood serum , vesicle preparations were separated by a sucrose density gradient as described above , and only the exosomal fractions were used for further analysis . Formalin-fixed and paraffin-embedded tissue of human cerebella from the Edinger Institute archive was cut into 4 µm serial sections and stained after rehydration by serial incubation in xylol ( 2×10 min ) and ethanol ( 98% to water , 5 min each ) . Every fifth section was analyzed to avoid counting the same neuron twice . Antibodies that were used were anti-mouse Calbindin ( mouse monoclonal , Sigma ) , anti–β-Gal ( mouse monoclonal , Promega; rabbit polyclonal , MP Biomedicals ) , Alexa 488–conjugated anti-NeuN ( mouse monoclonal , Chemicon ) , anti-TH ( rabbit polyclonal or mouse monoclonal , both Chemicon ) , anti-Iba1 ( rabbit polyclonal , Wako ) , Alexa 488–conjugated anti-GFP ( rabbit polyclonal , Invitrogen ) , polyclonal rabbit anti–glial fibrillary acidic protein ( GFAP ) ( Dako ) , anti-human CD68 , CD15 , and leukocyte common antigen ( LCA ) ( all mouse monoclonal , Dako ) . For light microscopic staining , polylink and peroxidase label ( DCS ) were used in combination with the AEC substrate pack ( BioGenex ) followed by a hemalum counterstaining . Light microscopic images were recorded on an Olympus 80i microscope; fluorescent images were taken on a confocal LSM Nikon TE2000-E microscope . Images were processed using the EC-C1 3 . 60 software and ImageJ ( NIH ) . Figures were mounted in Adobe Photoshop CS4 . For the quantitation of cerebellar Purkinje neurons , all sections from one cerebellar hemisphere were stained for LacZ or GFP , and recombined Purkinje neurons were counted in every other section equaling one quarter of the population . RNA was purified using Qiagen RNeasy Micro Kit , according to the manufacturer's instructions . cDNA synthesis was performed with SuperScipt III reverse transcriptase ( RT ) ( Invitrogen ) and OligodT18 Primers ( Fermentas ) . Residual DNA contamination was excluded by controls in which RT was omitted during cDNA synthesis . For iCre mRNA detection , a heminested PCR was performed with 5 µl of vesicle cDNA preparation and Primers FORWARD-out and REVERSE ( FORWARD-out , 5′-GTGGGAGAATGCTGATCCACA-3′; REVERSE , 5′-ACACCATTCTTTCTGACCCG-3′ ) in the first reaction followed by the second PCR using 2 µl of the amplificate and FORWARD-in ( 5′-GGTTACCAAGCTGGTGGAGA -3′ ) and REVERSE . PCR products were visualized on a 3% agarose gel . For protein analysis , acetone was added to the vesicle containing sucrose fractions . Precipitates were boiled in SDS sample buffer to further perform SDS/PAGE and Western blot analysis with antibodies to anti-mouse CD9 ( R&D Systems , clone 139712 ) , anti-mouse ADAM10 ( sc-18869 , Santa Cruz ) , and anti–Cre-recombinase ( Covance , NMS-106P ) . Vesicle preparations were fixed and stained as described [11] with minor alterations . The uranyl-oxalate ( step 6 ) was substituted with 2% of uranyl-acetate , and the ratio of methylcellulose to uranyl-acetate was changed to 1% each ( step 7 ) . Grids were analyzed using Tecnai Spirit BioTWIN electron microscope ( FEI , Netherlands ) at 120 kV . Pictures were taken with an eagle bottom-mount CCD camera . Secreted membrane vesicle preparations obtained from hematopoietic cell culture were resuspended in HEPES buffer and injected i . v . ( 200 µl ) or directly into cerebellar hemispheres of anesthetized Cre-reporter mice ( 1 µl ) . Injections of 1 µl Cre recombinase protein ( New England Biolabs ) ( 1 U/µl or 91 n g/µl ) or lysate from Cre-expressing BMCs served as controls . One or two intracerebellar injections were performed per hemisphere with 1 µl each at 6 . 2 mm caudal and 2 mm lateral to bregma and 1 . 5 mm below the dura mater . Mice were kept for 4–5 d until they were sacrificed for subsequent analysis as above . All sections from the injected cerebellar hemisphere were stained with an anti–β-Gal antibody with AEC Kit , and recombined Purkinje neurons were counted in every section . Impedance measurements were performed with a cellZscope device ( nanoAnalytics ) as described previously [21] using the endothelial cell line bEnd5 . After reaching confluence , indicated by a plateau in the TEER and Ccl values , EVs prepared from the supernatant of 10×107 BMCs after 3 d in culture were added to the transwell units . The cell culture supernatant after the ultracentrifugation step was used at the same volume as the control ( 100 µl of EV preparation/supernatant diluted in 900 µl endothelial cell medium ) . Measurements were performed three times in quadruplicates with EVs from three separate preparations . Mice were intraperitoneally injected 5 times ( 24 h apart ) with MPTP ( 30 mg/kg; Sigma; 3 mg/ml saline ) or saline ( 10 ml/kg ) as the control . Animals were sacrificed and brains were carefully removed 15 d after the last injection . BMCs of donor mice were extracted by flushing femurs and tibias with chilled PBS supplemented with 1% bovine serum albumin ( PBS/BSA ) into 35 mm tissue culture dishes on ice . BMCs were washed in cold PBS/BSA , suspended in PBS/BSA to a final concentration of 25×106/ml , and kept on ice until further use . Recipient mice were lethally gamma-irradiated with a single dose of 9 . 5 Gy using a Cesium source with a dose rate of 1 Gy/min . Within 3 h after irradiation , 5×106 BMCs were injected into the lateral tail vein . After transplantation , mice were kept in filtertop boxes and received antibiotic prophylaxis ( 0 . 025% Baytril , Bayer ) p . o . in drinking water until engraftment . Mice were analyzed 5 wk or 2 mo after transplantation , respectively . For adoptive transfer , spleen and lymph nodes of donor mice with peritonitis induced the previous day were dissociated into PBS , and 20×106 cells were injected into the lateral tail vein . Animals were analyzed after 10 d . Brains from Vav-iCre-LacZ mice were rapidly removed from the cranium 4 d after induction of peritonitis , flash-frozen in tissue freezing medium on dry ice , and stored at −80°C until sectioning . For X-Gal staining , cerebellar parasagittal cryostat sections ( 8 µm ) were collected on polyethylene-naphthalene RNase-free slides ( Leica Microsystems ) . The sections were fixed in 4% PFA in PBS for 20 min and briefly washed in RNase-free water . Tissue sections were then fixed in 70% ethanol for 5 min , washed in RNase-free water for 5 min , and stained in X-Gal reaction buffer for approximately 16–20 h at 37°C . For cerebellar histology , brain slices were counterstained with nuclear fast red staining solution ( Sigma ) . A Leica DM6500 LMD system ( Leica Microsystems ) was used to dissect single X-Gal–positive Purkinje cells separately from adjacent X-Gal–negative Purkinje cells as controls . Cell pools of 50 and 100 pairs of recombined/nonrecombined Purkinje cells per animal were collected for further analysis as well as an approximate area ( size , ∼100 , 000 µm2 ) from the adjacent granule cell layer ( GCL ) as control . Total RNA was isolated from the microdissected PC and GCL using RNeasy Plus Micro Kit ( Qiagen ) . RNA integrity analysis using the Agilent 2100 bioanalyzer and RNA 6000 Pico LabChip Kit ( Agilent Technologies ) revealed highly intact RNA ( RIN , 8 . 2–8 . 8 ) . Total RNA was reverse transcribed using the High Capacity cDNA Reverse Transcription Kit ( Applied Biosystems ) . Real-time reverse transcription polymerase chain reaction ( RT-qPCR ) of distinct target genes ( Table S2 ) was performed on a StepOnePlus Real-Time PCR System ( Applied Biosystems ) using EXPRESS SYBRGreenER qPCR SuperMix ( Invitrogen ) or TaqMan Fast Universal PCR Master Mix ( Applied Biosystems ) with TaqMan Gene Expression Assays ( Applied Biosystems ) . miRNA from LCM Purkinje neurons or from serum exosomes was isolated using the miRNeasy Micro Kit ( Qiagen ) according to the manufacturer's protocol . The extracted miRNA was diluted in 15 µl H2O . Quantitative miRNA expression analysis was performed using real-time PCR ( LightCycler 480 Real-Time PCR System , Roche Applied Science ) . To screen for deregulated miRNA in the LCM Purkinje neurons or serum exosomes , the miRCURY LNA Universal microRNA Ready-to-Use PCR panel ( Exiqon ) was used according to the manufacturer's instructions . Prior to the analysis , plate-specific effects were normalized using the reference RNA included . Cp values from recombined Purkinje neurons were then compared to the Cp values measured in nonrecombined Purkinje neurons .
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Peripheral infections leading to an inflammatory response can initiate signaling from the hematopoietic system to various organs including the brain . The traditional view of this communication between blood and brain is that individual factors are released by immune cells that in turn bind to neuronal or nonneuronal target cells in the brain where they exert their effects . By using a genetic tracing system , we now show that extracellular vesicles , small membrane structures that can contain a multitude of different molecules , can transfer functional RNA directly from blood cells to neurons . Although this type of signaling is highly restricted in the healthy animal , inflammatory injuries increase both the frequency of transfer and the range of the neuronal target populations in the brain . By showing altered miRNA profiles in neurons receiving extracellular vesicle cargo , we predict a complex regulation of gene expression in neural cells in response to peripheral inflammation .
|
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2014
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Extracellular Vesicle-Mediated Transfer of Genetic Information between the Hematopoietic System and the Brain in Response to Inflammation
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Bartonellae are fastidious bacteria causing persistent bacteremia in humans and a wide variety of animals . In recent years there is an increasing interest in mammalian bartonelloses in general and in rodent bartonelloses in particular . To date , no studies investigating the presence of Bartonella spp . in rodents and ectoparasites from Nigeria were carried out . The aim of the current study was to investigate the presence of Bartonella spp . in commensal rodents and their ectoparasites in Nigeria . We report , for the first time , the molecular detection of Bartonella in 26% ( 46/177 ) of commensal rodents ( Rattus rattus , R . norvegicus and Cricetomys gambianus ) and 28% ( 9/32 ) of ectoparasite pools ( Xenopsylla cheopis , Haemolaelaps spp . , Ctenophthalmus spp . , Hemimerus talpoides , and Rhipicephalus sanguineus ) from Nigeria . Sequence analysis of the citrate synthase gene ( gltA ) revealed diversity of Bartonella spp . and genotypes in Nigerian rodents and their ectoparasites . Bartonella spp . identical or closely related to Bartonella elizabethae , Bartonella tribocorum and Bartonella grahamii were detected . High prevalence of infection with Bartonella spp . was detected in commensal rodents and ectoparasites from Nigeria . The Bartonella spp . identified were previously associated with human diseases highlighting their importance to public health . Further studies need to be conducted to determine whether the identified Bartonella species could be responsible for human cases of febrile illness in Nigeria .
Bartonellae are Gram-negative facultative intracellular alpha-proteobacteria belonging to the family Bartonellaceae . Many Bartonella species have been affecting human life for centuries [1] . Since the first Bartonella species discovery , namely Bartonella bacilliformis , in 1905 by Alberto Leonardo Barton Thompson , more than 30 species of Bartonella were identified [2] , [3] . Bartonella species have been found in a variety of mammals , and the numbers of Bartonella species and their respective reservoir hosts are constantly growing [4] . They are pathogens of emerging and reemerging significance , causing a wide array of clinical syndromes in human and animal hosts [5] . These bacterial species are transmitted between the reservoir and the final mammal host by hematophagous arthropods and insects such as fleas , sand flies , mites , lice and possibly ticks , usually by their bites [6]–[8] . The range of vectors involved in the transmission of the different species of this genus has not been fully characterized [9] . Bacteria belonging to the genus Bartonella are slow growers in vitro , and the most used diagnostic methods are isolation , serology and polymerase chain reaction ( PCR ) . The use of sequencing on PCR amplicons has been recommended in order to detect new species , especially when dealing with uncommon clinical presentations and settings [4] . Bartonella DNA has been detected in various hosts and possible vectors in many countries including , Israel [10] , [11] , Indonesia [12] , Nepal [13] , Thailand [6] , [14] , China [15] , Taiwan [16] , Korea [17] , USA [18]–[20] , UK [21] , [22] , Spain [23] and The Netherlands [24] . In Africa there are reports from Kenya [25] , the Democratic Republic of Congo and Tanzania [26] , Algeria [27] , [28] , Egypt [29] , [30] , Gabon [31] and South Africa [32] , [33] . However , there is no report of molecular screening of humans or animals and their ectoparasites for Bartonella spp . in Nigeria . Although there are no case estimates of fever of unknown origin ( FUO ) in Nigeria , the condition remains a challenging medical problem and unraveling the diagnosis could be a daunting task when investigating for common infective and non-infective causes . Moreover , since Bartonella spp . are difficult to diagnose and are seldom included in the differential diagnosis list in cases of FUO , specific Bartonella sp . treatment is rarely instituted to patients with FUO . The objectives of this study were to determine the possible infection of commensal rodents and their ectoparasites from Nigeria with Bartonella spp . , to investigate the presence of zoonotic Bartonella spp . in these rodents and ectoparasites and to evaluate genetic heterogeneity of circulating Bartonella strains in this country .
The study protocol was read and approved by The National Veterinary Research Institute Vom Ethical Committee on Animal Use and Care . Permission to place the traps in the study area was granted by the residents . Animals were treated in a humane manner and in accordance with authorizations and guidelines for Ethical Conduct in the Care and Use of Nonhuman Animals in Research of the American Psychological Association ( APA ) for use by scientists working with nonhuman animals ( American Psychological Association Committee on Animal Research and Ethics ) in 2010 . Rodents were live trapped in domestic and peri- domestic areas in Vom ( 9°44′N/8°47′E ) Nigeria between October–December 2011 . A total of 177 rodents ( 48 Rattus rattus , 121 Rattus norvegicus , 6 Mus musculus and 2 Cricetomys gambianus ) were captured . Trapping was done using wire cage traps baited with smoked fish and other food scraps set out in the evenings when rodents are known to leave their holes to scavenge in farmlands or nearby human habitations . Traps were checked for rodents early the next morning . Cages containing rodents were transported to the Parasitology Laboratory , National Veterinary Research Institute ( NVRI ) Vom Nigeria , where they were identified and classified by a zoologist . At the laboratory , the cages containing rodents were placed into a clear plastic bag , which was sealed at the opening . Halothane gas was applied into the bag and the activity of the rodents was monitored . Once the rodents were anaesthetized , they were removed from the cage and bled by cardiac puncture . Depending on the size , 0 . 5–3 ml of blood was drawn and aliquoted into an EDTA tube and labeled . Each rodent was checked for ectoparasites by brushing the fur with a tooth brush onto a white cardboard paper . Ectoparasites were placed in labeled vials containing absolute ethanol corresponding to the host from which they were removed and stored at −20°C . Both blood and ectoparasite samples were transported in a cool box to The Koret School of Veterinary Medicine , The Hebrew University of Jerusalem , Israel for analysis . The ectoparasites were morphologically identified by an entomologist ( KYM ) at the Department of Microbiology and Molecular Genetics in Jerusalem , Israel . Two hundred microlitres of thawed whole blood sample was plated onto chocolate agar . The plate was incubated at 35°C and 5% CO2 and checked for growth of Bartonella species on alternate days for up to 30 days . Suspected colonies were randomly selected and separately sub-cultured onto different fresh agar plates to obtain pure colonies . DNA was extracted from blood using BiOstic Bacteremia DNA Isolation Kit ( MO Bio Laboratories , Inc USA ) according to manufacturer's instructions . The ectoparasites collected from each rodent species were pooled ( 2–3 arthropods per pool ) according to genus and/or species . DNA was extracted from each pool using Illustra tissue and cell genomic Prep miniSpin kit ( GE Healthcare UK Limited ) according to manufacturer's instructions . Pure cultured colonies of Bartonella sp . were aseptically scooped into microfuge tubes containing 50 µl of sterile Phosphate Buffered Saline ( PBS ) . DNA was extracted from the bacterial colonies using Illustra tissue and cell genomic Prep miniSpin kit ( GE Healthcare UK Limited ) according to the manufacturer's instructions . The oligonucleotide primers: forward BhCS871 . p ( 5′ -GGGGACCAGCTCATGGTGG-3′ ) and reverse: BhCS1137 . n ( 5′-AATGCAAAAAGAACAGTAAACA-3′ ) [34] were used for amplification of a 379 bp region of the Bartonella citrate synthase gene ( gltA ) . Positive and negative controls were included in each PCR run . PCR was performed using reaction tubes , preloaded with a premier PCR master mix ( Syntezza PCR-Ready High Specificity , Syntezza Bioscience , Israel ) . 50 µl total volume was used as follows: 3 µl of DNA template , 1 µl of 10 mM each primer , 1 µl MgCl2 , 19 µl of ultra pure PCR water and 25 µl PCR master mix . Amplification was performed using a conventional thermocycler ( Biometra , Goettingen , Germany ) and the following program parameters: an initial denaturing at 95°C for five minutes , and 35 cycles of denaturation at 95°C for one minute , annealing at 56°C for one minute , and elongation at 72°C for one minute . Amplification was completed by holding the reaction mixture at 72°C for 10 minutes . PCR products were tested for the presence of amplicons of the correct size by electrophoresis of 6 µl of the products on 1 . 5% agarose gels stained with ethidium bromide and checked under UV light for the size of amplified fragments by comparison to a 50 bp DNA molecular weight marker . Amplicons of the proper size were identified by comparison to the positive control lane on the gel . Positive PCR products were purified using ( EXO-SAP IT USB , Cleveland , Ohio , USA ) and sequenced using the forward primer at the Center for Genomics Technologies , Hebrew University of Jerusalem , Israel . To avoid errors or misinterpretation of the sequencing results , we deleted primer sequences from the gltA sequences and removed all ambiguities in the sequences before sequence analysis was performed . Analysis of DNA sequences and phylogenetic relationships were done using MEGA 5 . Sequences were aligned by MUSCLE and the evolutionary history was inferred using the Maximum Likelihood method based on the Tamura-Nei model [35] . The bootstrap consensus tree inferred from 200 replicates was taken to represent the evolutionary history of the taxa analyzed . Branches corresponding to partitions reproduced in less than 50% bootstrap replicates are collapsed . The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test are shown next to the branches . All positions containing gaps and missing data were eliminated .
A total of 177 cardiac blood samples from four rodent species were examined in this study: 68 . 4% ( 121/177 ) from Rattus norvegicus rats; 26% ( 48/177 ) from Rattus rattus rats; 1 . 1% ( 2/177 ) from Cricetomys gambianus rats , and 3 . 4% ( 6/177 ) from Mus musculus mice . One hundred and seventy ectoparasites comprising of 85 ticks , Rhipicephalus sanguineus ( 79 ) and Haemaphysalis leachi ( 6 ) ; 13 fleas , Xenopsylla cheopis ( 8 ) , Ctenophthalmus spp . ( 5 ) and 62 Haemolaelaps spp . ( gamasid mites ) were recovered from the rodents . Ten additional Hemimerus talpoides ( earwig sp . ) were removed from the 2 C . gambianus captured ( Table 1 ) . Due to contamination problems , bartonellae could be cultured from a small subset of 30 rodent blood samples only . Nine of the latter 30 blood samples produced typical bartonellae growth . The colonies were creamy white in color , small , moist with metallic sheen and tended to pit on the agar . Initial growth of Bartonella sp . cultures were seen after 5–7 days of incubation . Colonies were sub cultured onto new plates to obtain pure cultures , which were harvested and preserved in 10% glycerol at −80°C until molecularly analyzed . Bartonella gltA gene fragments were detected in 46 of 177 ( 26% ) rodent blood samples screened in this study . One of 2 C . gambianus ( 50% ) , 36 of 121 R . norvegicus ( 29 . 8% ) , and 9 of 48 R . rattus ( 18 . 8% ) were positive for Bartonella sp . DNA . None of the 6 M . musculus examined was positive for Bartonella sp . gltA . Nine of 32 ( 28% ) ectoparasite pools removed from 48/177 ( 27 . 1% ) rodents were positive for Bartonella gltA DNA . All the ectoparasite species tested were positive for Bartonella sp . gltA except H . leachi ( Table 1 ) . Forty six gltA sequences were obtained from blood , 3 from bacterial cultures and 9 from ectoparasite samples . Selected Bartonella sequences were deposited in GenBank under the following accession numbers: JX0265667–JX0265697 for blood , JX026972 for culture , and JX 026997–JX027006 for ectoparasites . Sequences obtained were compared with Bartonella sp . sequences deposited in GenBank for sequence similarity . Thirty six sequences were obtained from R . norvegicus blood , 26 of which had 98–100% identity with GenBank deposited B . elizabethae sequence ( n = 2 , 100% identity; n = 23 , 99%; n = 1 , 98% ) . Nine of the sequences obtained from R . norvegicus blood had 97–98% identity with GenBank deposited Bartonella tribocorum sequence , while 1 sequence had 98% similarity with GenBank deposited Bartonella grahamii . Nine sequences were obtained from R . rattus blood , 7 of which had sequence identity of 98–100% with GenBank deposited B . elizabethae sequence ( n = 3 , 100% identity; n = 1 , 99%; n = 3 , 98% ) ( Table 2 ) . The sequence retrieved from the blood of C . gambianus had 99% identity with GenBank deposited B . elizabethae . Bartonella gltA sequences obtained from one pool each of X . cheopis , R . sanguineus , and 3 pools of Haemolaelaps sp . had 97–100% similarity to B . elizabethae deposited in GenBank while a sequence from Ctenophthalmus sp . pool had 97% identity with B . tribocorum sequence deposited in GenBank . Interestingly , Bartonella sp . DNA with 99% sequence identity to B . elizabethae deposited in the GenBank was detected from one pool of H . talpoides earwigs that were removed from C . gambianus rats . Bartonella spp . DNA was detected in 4 of 13 ( 30 . 8% ) rodents from which the ectoparasites were removed . However , only one ectoparasite , H . talpoides removed from C . gambianus had the same percent sequence identity ( 100% ) with that of the host . The DNA sequences from the ectoparasites had 97–99% identity with their first GenBank match ( Table 3 ) . The R . sanguineus pool that was positive for Bartonella spp . DNA was collected from R . norvegicus rat that was negative for Bartonella sp . DNA . The phylogenetic relationship among the genotypes obtained in the present study and previously described Bartonella species is presented in Figure 1 . Sequences of Bartonella sp . from this study formed 3 distinct clusters A-C along with B . elizabethae and B . grahamii ( Fig . 1 ) , but was distantly related to other sequences deposited in the GenBank . The first cluster ( cluster A ) consists of 4 sequences closely related to B . elizabethae . However , 5 other sequences that were 97–100% similar to B . elizabethae appear as single genotypes just below cluster A . Cluster B is made up of 2 sequences that were similar to B . grahamii deposited in the GenBank . The cluster C consists of 5 sequences that were 97–100% similar to B . tribocorum deposited in the genBank . Sequences were coded based on rodent or ectoparasites species from which they were detected , accession numbers are in parentheses; RR = Rattus rattus; RN = Rattus norvegicus; CG = Cricetomys gambianus; CS = Ctenophthalmus sp; HT = Hemimerus talpoides; RS = Rhipicephalus sanguineus , MS = Haemolaelaps spp . ; XC = Xenopsylla cheopis .
In this study , we report the molecular detection and genetic characterization of Bartonella species in rodents and ectoparasites from Nigeria , West Africa . Moreover , to the best of our knowledge , this is the first report of molecular investigation of Bartonella spp . in rodents and their ectoparasites in this country . The 26% prevalence of Bartonella DNA found in this study was higher than the 8 . 5% prevalence reported in small mammals from the Democratic Republic of Congo but lower than and 38% reported in Tanzania [26] . The differences between the findings in the latter studies and ours can be attributed to the fact that commensal rodents were screened in the current study while sylvatic rodents were screened in the DR Congo and Tanzania studies . Similarly , the 28% prevalence of Bartonella DNA by gltA PCR in ectoparasites in this study was slightly higher than the 21 . 5% reported in fleas from Algeria , targeting 3 genes and the inter-genic spacer ( ITS ) [28] . The high prevalence of detection of Bartonella spp . DNA in the ectoparasites attests to their role as vectors of these bacteria . Several Bartonella spp . that were associated with human diseases were identified in this study , including B . elizabethae , B . grahamii and B . tribocorum . Bartonella elizabethae was found in patients with endocarditis [36] . Bartonella grahamii was associated with neuroretinitis or bilateral retinal artery branch occlusions [37] . A Bartonella genotype closely related ( 97% ) to B . tribocorum was detected in the blood of human patient with fever from Thailand [25] . The finding of these zoonotic Bartonella spp . in commensal rodents from Nigeria demonstrates their importance as reservoirs for various zoonotic Bartonella species and warrants increased awareness of physicians and health care workers for these pathogens especially in unidentified febrile cases . In this study , no DNA sequence similar to B . tribocorum was obtained from R . rattus rats . The detection of B . tribocorum only in R . norvegicus rats is in agreement with the earlier report of Márquez et al . [23] which supports the hypothesis that there is specificity of Bartonella spp . for their rodent hosts [15] . Although the role of R . sanguineus ticks in transmitting Bartonella spp . in nature is not proven [38] it is important to note that we detected Bartonella DNA in R . sanguineus ticks . Detection of Bartonella DNA in ticks was previously reported also by other authors [8] , [17] , [39] . The Bartonella spp . DNA detected from one R . sanguineus tick pool had 97 percent identity to B . elizabethae sequences deposited in GenBank . It is worthy to note that the host from which the R . sanguineus ticks were removed was negative for Bartonella spp . DNA . This suggests that the ticks might have acquired the bacteria during previous feeding on an infected host . The ability of the tick to transmit this organism to a susceptible host during the next feeding stage or to its progeny is worth further investigation . Comparative analyses of the gltA sequences obtained from Bartonella spp . showed that commensal rodents in Nigeria harbor a diverse assemblage of Bartonella strains , some of which represent known Bartonella spp . and strains and others may represent distinct novel strains . Although only a portion of the citrate synthase gene ( gltA ) was used for phylogenetic analysis , this gene has been shown to be a reliable tool for distinguishing between closely related Bartonella genotypes [40] . By using this partial gene , it was possible to compare the variety of Bartonella genotypes isolated from rodents with homologous sequences of Bartonella strains found in other mammals , reported from other parts of the world . Finding considerable sequence diversity is typical for different species of Bartonella , although more characteristics are needed to describe novel Bartonella species [3] . In this study , the Bartonella genogroups identified in commensal rodents formed three separate clusters closely related to B . elizabethae but distantly related to other known Bartonella spp . Although BLAST searches shows some of the sequences had 97–100% similarity to B . tribocorum and B . grahamii sequences deposited in GenBank ( Fig . 1 ) . The findings of Bartonella sequences that were genetically distant from known GenBank deposited sequences requires further investigation in characterizing these genotypes and ascertaining whether they are pathogenic to animals and/or humans . Pools of H . talpoides collected from C . gambianus in this study contained Bartonella DNA . Hemimerus talpoides ( earwig sp . ) are presumed to feed on the epidermis of their host or as a saprophytic on fungus from the skin of the host . The detection of Bartonella sp . DNA in this ectoparasite is interesting and requires further investigation [41] . In conclusion , this study has resulted in the identification and genetic characterization of Bartonella genotypes in commensal rodents and ectoparasites from Nigeria , West Africa . A high prevalence and diversity of Bartonella spp . and strains was detected in commensal rodents and their ectoparasites in this study . Several zoonotic Bartonella spp . including B . elizabethae , B . grahamii and B . tribocorum were identified for the first time in Nigeria highlighting their importance for public health in this country .
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Bartonella species are zoonotic vector-borne bacteria that typically parasitize the erythrocytes of mammalian hosts , resulting in long lasting infections . They are responsible for a wide range of clinical manifestations in both immunocompetent and immunocompromised hosts . Rodents and a wide range of small mammals serve as reservoirs of bartonellae , usually with no apparent clinical manifestations . Close association between rodents and humans especially in rural communities as well as in the overcrowded cities facilitates transmission of these bacteria . There have been no studies investigating the presence of Bartonella spp . in rodents and ectoparasites from Nigeria . The aim of the current study was to investigate the presence of Bartonella spp . in commensal rodents and their ectoparasites in Nigeria and its public health implications . We report , for the first time , the molecular detection of Bartonella in 26% ( 46/177 ) of commensal rodents and 28% ( 9/32 ) of ectoparasite pools from Nigeria . Sequence analysis of the citrate synthase gene ( gltA ) revealed diversity of Bartonella spp . and genotypes in Nigerian commensal rodents and their ectoparasites . The Bartonella spp . detected in this study were identical or closely related to Bartonella elizabethae , Bartonella tribocorum and Bartonella grahamii previously associated with human diseases highlighting their importance to public health .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"veterinary",
"diseases",
"veterinary",
"epidemiology",
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2013
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Prevalence and Diversity of Bartonella Species in Commensal Rodents and Ectoparasites from Nigeria, West Africa
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We evaluate the association between Trypanosoma cruzi infection and strongyloidiasis in a cohort of Latin American ( LA ) migrants screened for both infections in a non-endemic setting . Case-control study including LA individuals who were systematically screened for T . cruzi infection and strongyloidiasis between January 2013 and April 2015 . Individuals were included as cases if they had a positive serological result for Strongyloides stercoralis . Controls were randomly selected from the cohort of individuals screened for T . cruzi infection that tested negative for S . stercoralis serology . The association between T . cruzi infection and strongyloidiasis was evaluated by logistic regression models . During the study period , 361 individuals were screened for both infections . 52 ( 14 . 4% ) individuals had a positive serological result for strongyloidiasis ( cases ) and 104 participants with negative results were randomly selected as controls . 76 ( 48 . 7% ) indiviuals had a positive serological result for T . cruzi . Factors associated with a positive T . cruzi serology were Bolivian origin ( 94 . 7% vs 78 . 7%; p = 0 . 003 ) , coming from a rural area ( 90 . 8% vs 68 . 7%; p = 0 . 001 ) , having lived in an adobe house ( 88 . 2% vs 70%; p = 0 . 006 ) and a referred contact with triatomine bugs ( 86 . 7% vs 63 . 3%; p = 0 . 001 ) . There were more patients with a positive S . stercoralis serology among those who were infected with T . cruzi ( 42 . 1% vs 25%; p = 0 . 023 ) . Epidemiological variables were not associated with a positive strongyloidiasis serology . T . cruzi infection was more frequent among those with strongyloidiasis ( 61 . 5% vs 42 . 3%; p = 0 . 023 ) . In multivariate analysis , T . cruzi infection was associated with a two-fold increase in the odds of strongyloidiasis ( OR 2 . 23; 95% CI 1 . 07–4 . 64; p = 0 . 030 ) . T . cruzi infection was associated with strongyloidiasis in LA migrants attending a tropical diseases unit even after adjusting for epidemiological variables . These findings should encourage physicians in non-endemic settings to implement a systematic screening for both infections in LA individuals .
In recent years , the significant increase in the number of Latin American migrants in Europe has meant the introduction of parasitic endemic infections , such as Trypanosoma cruzi infection and strongyloidiasis [1–3] . Both are neglected tropical diseases ( NTD ) , sharing a similar epidemiological profile in Latin America ( LA ) and producing life-long infections , usually silent , leading to high morbidity and mortality [4–6] . Chagas disease is a zoonosis endemic to LA countries [6 , 7] , caused by hemoflagellated protozoan Trypanosoma cruzi , usually after contact with faeces of blood-sucking triatomines [8] . Congenital , organ transplantation and transfusion-related transmission are other principal routes of T . cruzi infection , which have been described in non-endemic areas [9] . The acute infection is followed by an asymptomatic chronic stage during years . After 20–30 years , up to 30–40% of patients will develop the symptomatic chronic phase , with cardiac and/or digestive involvement [8 , 10 , 11] . Chagas disease diagnosis in the chronic phase is based on serological tests [10 , 11] . Strongyloidiasis is a highly prevalent ( over 30–100 million people worldwide ) nematode infection , with a unique life-cycle where Strongyloides stercoralis females reproduce parthenogenetically to produce an autoinfective cycle that can lead to life-long and barely symptomatic carriage [5 , 12 , 13] . Nonetheless , in the context of immunosuppression , it can cause severe forms with larvae dissemination to extraintestinal organs and high mortality rates [14 , 15] . The diagnosis of strongyloidiasis is challenging due to irregular larvae output resulting in low sensitivity of common parasitological methods . Serology is a very sensitive test ( 88–95% ) and it may be useful in the follow-up , as titers usually decrease after successful treatment [16–18] . In the context of migration and the increasing use of immunosuppressive treatments ( steroids , monoclonal antibodies… ) , T . cruzi infection and strongyloidiasis have emerged as an important public health problem in Europe , North America and other areas hosting Latin American population [3 , 19] . Some European countries including Spain , have implemented national programmes to control transfusional and mother-to-child transmission of T . cruzi [20 , 21] , and recent recommendations for the screening and management of strongyloidiasis in non-endemic areas have been published [22] . However , European countries are far from achieving an adequate control of the morbidity caused by these two silent chronic infections . Besides , little is known about the association between both infections in LA migrants and whether this eventual association should prompt a joint screening strategy in tropical diseases clinics . The main aim of the present study is to evaluate the association between T . cruzi infection and strongyloidiasis in a cohort of LA migrants screened for both infections in a non-endemic setting .
This is a retrospective case-control study performed at the Tropical Medicine and International Health Department in Hospital Clínic , Barcelona . Hospital Clínic is a tertiary teaching hospital and a national reference centre in Spain for Tropical Imported Diseases . Systematic screening of T . cruzi infection ( among others ) is performed among adult individuals who have lived for more than a year in endemic countries , or who are born from mothers with LA origin . Similarly , strongyloidiasis testing was incorporated in the systematic screening of these patients in January 2013 . Individuals are commonly sent to our outpatient clinic referred by friends or relatives , primary healthcare professionals or they come by their own initiative . Eligible participants of the study were selected from the cohort of adult individuals screened for T . cruzi infection and strongyloidiasis between January 2013 and April 2015 . Individuals were included as cases if they had a positive serology for S . stercoralis in the screening blood test . Individuals who had been diagnosed with strongyloidiasis prior to the study period or had previously been treated with ivermectin were excluded . Controls were randomly selected from the cohort of individuals who were screened for T . cruzi infection and tested negative for S . stercoralis serology . Two controls were randomly selected for each case included in the study . Eligible participants were invited to participate in the outpatient clinic , after signing the informed consent form . Individuals were asked for clinical and epidemiological data , which was filled in a standardized questionnaire . This contained data on sociodemographic variables , area of origin , residence in rural areas or potential risks of Chagas disease transmission ( contact with T . cruzi vector , history of maternal Chagas disease or blood transfusions ) . Blood samples were taken to perform serological tests for T . cruzi , S . stercoralis and HIV infection . Routine hematology and biochemistry tests ( including liver and renal function ) were performed in all cases . Eosinophilia was defined as >500 eosinophils/mm3 or a percentage ≥ 5% . Laboratory diagnosis of T . cruzi infection was established by two serological ELISA tests , following international recommendations [23] . One was a commercial ELISA with recombinant antigens ( BioELISA Chagas , Biokit S . A . , Barcelona , Spain ) , and the other was an in-house ELISA with whole T . cruzi epimastigotes antigen . Diagnosis of T . cruzi infection was defined by positivity in the two serological tests . S . stercoralis serological screening was performed with the commercial test IVD-ELISA ( IVD Research , Carlsbad , CA ) which detects IgG antibodies by using somatic antigens from larvae of the parasite . A cut-off of the sample absorbance/0 . 2 ( i ) >1 . 1 is defined as positive . Individuals were also asked to provide three stool samples from different days for direct microscopic examination . Agar plate culture was also performed in at least one stool sample ( when available ) per individual . Stata version 13 . 1 ( Stata Corporation , College Station , TX , USA ) was used for statistical analyses . Categorical variables were described by counts and percentages , whereas continuous variables were expressed as means and standard deviations ( SD ) or medians and interquartile ranges ( IQRs ) . The chi-square Pearson test was used to compare the distribution of categorical variables . The Mann-Whitney U test or the t-student test were used to compare the distribution of continuous variables . To analyze the association between exposure variables and strongyloidiasis , logistic regression models were built to estimate unadjusted or adjusted odds ratios ( ORs ) with their 95% confidence interval ( 95% CI ) . Results were considered statistically significant if the two-tailed p-value was <0 . 05 . The likelihood ratio test was used to obtain p-values . The Ethics Committee of Hospital Clínic approved this study . Data collection forms were completely anonymous . Written informed consent was obtained from participants for collecting clinical and epidemiological data .
During the study period , 392 patients were screened for T . cruzi infection in our center . Of these patients , 361 ( 92 . 1% ) were also screened for strongyloidiasis , and were then eligible for the study ( see Fig 1 ) . Overall , 52 ( 14 . 4% ) patients had a positive S . stercoralis serological result and were then included as cases and 104 out of 309 participants with negative results were randomly selected as controls by simple randomization . Table 1 shows the baseline characteristics of the cohort . The median age of the patients was 36 years ( IQR 29–43 ) and 100 ( 64 . 1% ) were women . The vast majority came from Bolivia ( 135 patients , 86 . 5% ) , and the other patients came from several different LA countries . Mean time in Spain prior to screening was 8 . 46 years ( SD 3 . 64 ) . There were 124 ( 79 . 5% ) patients who came from rural areas , 123 ( 78 . 8% ) had lived in an adobe house and 115 ( 74 . 7% ) referred having had contact with triatomine bugs . Most patients were asymptomatic and the most common complaints were abdominal bloating ( 19 . 9% ) , heartburn ( 11 . 5% ) and abdominal pain ( 9% ) . Absolute and relative eosinophilia were present in 30 ( 19 . 2% ) and 50 patients ( 32 . 1% ) , respectively . There were 76 ( 48 . 7% ) patients with a positive T . cruzi serology . Most were women ( 52; 68 . 4% ) and the median age was 37 years ( IQR 30–43 ) . Among those with a positive S . stercoralis serology , there were 34 ( 65 . 4% ) women and the median age was 38 years ( IQR 31–44 ) . Mean serology titers were 5 . 3 ( IQR 1 . 9–8 . 7 ) , and 28 patients ( 53 . 8% ) had titers greater than 2 . 50 . None were positive for HIV . From the 114 ( 73 . 1% ) patients who had provided at least one stool sample for examination , 2 ( 1 . 8% ) had one positive sample for S . stercoralis and 6 ( 5 . 3% ) had two positive samples . No other helminths were isolated from stool samples , although 25 ( 16% ) patients had other microorganisms isolated from stool: 3 Giardia lamblia , 10 Blastocystis hominis and 12 Entamoeba sp . Table 2 shows the main characteristics of patients regarding to whether they had T . cruzi infection and/or strongyloidiasis . Gender was not found to be associated with T . cruzi infection ( p = 0 . 273 ) nor strongyloidiasis ( p = 0 . 813 ) . The proportion of patients aged 35 or older was also similar among T . cruzi ( p = 0 . 307 ) and S . stercoralis ( p = 0 . 302 ) infected and non-infected participants . Factors associated with a positive T . cruzi serology were Bolivian origin ( 94 . 7% vs 78 . 7%; p = 0 . 003 ) , coming from a rural area ( 90 . 8% vs 68 . 7%; p = 0 . 001 ) , having lived in an adobe house ( 88 . 2% vs 70%; p = 0 . 006 ) and a referred contact with triatomine bugs ( 86 . 7% vs 63 . 3%; p = 0 . 001 ) . There were more patients with a positive S . stercoralis serology among those who were infected with T . cruzi ( 42 . 1% vs 25%; p = 0 . 023 ) . Epidemiological variables , such as Bolivian origin ( 88 . 5% vs 85 . 6%; p = 0 . 619 ) , coming from a rural area ( 80 . 8% vs 78 . 8%; p = 0 . 779 ) , having lived in an adobe house ( 78 . 8% in both groups ) and a referred contact with triatomine bugs ( 76 . 9% vs 73 . 5%; p = 0 . 647 ) were not associated with a positive strongyloidiasis serology . T . cruzi infection was more frequent among those with strongyloidiasis ( 61 . 5% vs 42 . 3%; p = 0 . 023 ) . No differences between both groups were found in clinical symptoms , such as abdominal pain ( 13 . 5% vs 6 . 7%; p = 0 . 166 ) , heartburn ( 15 . 4% vs 9 . 6%; p = 0 . 288 ) , and abdominal bloating ( 23 . 1% vs 18 . 3%; p = 0 . 478 ) . Isolation of other microorganisms in stool samples was also not associated with strongyloidiasis ( 16 . 7% vs 25%; p = 0 . 300 ) . After adjusting for sex , age , country of origin and rural area , T . cruzi infection was associated with a two-fold increase in the odds of strongyloidiasis ( OR 2 . 23; 95% CI 1 . 07–4 . 64; p = 0 . 030 ) ( Table 3 ) . We decided not to adjust for relative eosinophilia as this factor could cause collinearity with strongyloidiasis . Similarly , triatomine bug contact and living in an adobe house were also not included , since these variables could cause collinearity with rural area .
In this retrospective case-control study , we offer an evaluation of the clinical and epidemiological characteristics of LA migrants screened for both T . cruzi infection and strongyloidiasis in a reference unit for tropical diseases . The most important finding of our study is the association found between both strongyloidiasis and T . cruzi infection . Almost all individuals screened were young , with no comorbidities—probably reflecting the overall epidemiological characteristics of global population migrating for work . The proportion of T . cruzi infection was found to be high , as in other series of imported diseases centers and known to be altered by a positive selection bias [24 , 25] . This figure is highly conditioned by the fact that the majority of patients came from Bolivia , which is known to be a highly endemic country for Chagas disease [6] . In addition , most of them had lived in rural areas where the high prevalence of the vector is associated to suitable conditions for transmission such as the presence of adobe houses . Expectedly , T . cruzi infection was associated with Bolivian origin , having lived in an adobe house and a referred contact with triatomine bugs . For initially screened patients , strongyloidiasis prevalence was 14 . 4% . This prevalence rate seems accordant to that found in similar migrant populations in non-endemic areas [26 , 27] , although these studies were mostly conducted in HIV patients . Nonetheless , global prevalence of strongyloidiasis is generally underestimated and data on Bolivian prevalence of this nematode infection is especially scarce [5 , 28 , 29] . Considering the potential negative impact on patients of this life-long infection [15] , this high prevalence should prompt the inclusion of active screening strategies among susceptible populations from LA [22] . In our study , strongyloidiasis was neither associated to the epidemiological nor to the clinical variables recorded . It could have been expected to find an association between a positive serology and a rural origin or having lived in an adobe house [30 , 31] . A possible explanation for this is that the very high prevalence of these risk factors in the whole cohort ( around 80% ) could have masked a possible association , but it seems unlikely to be the only explanation . We found that strongyloidiasis and T . cruzi infection were associated even after adjusting for the main epidemiological variables . Few formal studies had previously analysed the possible association between both infections [32 , 33] . A possible explanation is that these two infections share an epidemiological burden where they are highly prevalent , but also the fact that both diseases are strongly influenced by socioeconomical factors such as soil contamination , barefoot walking or poor healthcare systems . Moreover , Salvador et al [33] reported a co-infection rate of 18% in those already diagnosed with T . cruzi infection and , interestingly , co-infected patients were found to have a higher proportion of positive T . cruzi RT-PCR in peripheral blood . The authors suggested that strongyloidiasis induction of Th2-immune response may lead to suppression of Th1-mediated immunity and therefore it may predispose to T . cruzi infection [33 , 34] . A recent cost-effectiveness study has shown that screening for Chagas disease in asymptomatic Latin American adults living in Europe is a cost-effective strategy [35] . In light of the high prevalence of strongyloidiasis found in T . cruzi infected patients , and that both diseases are prevalent and silent among Latin American migrants , a combined screening should be considered . The potential strongyloidiasis related complications and the benefits from ivermectin therapy are additional reasons to introduce systematic screening in susceptible populations . The strengths of this study are that serology was performed systematically on the first visit , minimizing a possible selection bias , and the fact that screening for both T . cruzi and strongyloidiasis was achieved in more than 90% of the patients . However , our study has some limitations that should be acknowledged . First of all , this is an observational retrospective study and had a relatively small sample size of patients with strongyloidiasis . Secondly , the diagnosis of strongyloidiasis relied solely in a positive serologic test . A limitation of serology is that it may have false-positive results due to cross-reaction with filariae and other helminthes [36] , and that it does not certainly indicate current infection [16] . Though these are important issues , especially in migrant patients where multiple parasite infections are frequent [37] , IVD-ELISA has shown reliable results in term of accuracy , with high positive and negative predictive values [38] . Actually , more than half of those diagnosed with strongyloidiasis had serology titers above 2 . 50 , which was correlated with the highest positive predictive value in one study [38] . Moreover , no other helminths were isolated from stool samples and the longtime living in Spain at screening reduces the possibility of a potential cross-reaction . Lastly , another limitation of our study is that the clear predominance of Bolivian patients compels us to be cautious with the generalizability of our findings , and further studies with higher proportions of LA migrants from other countries would be necessary . In conclusion , T . cruzi infection was found to be associated to strongyloidiasis in LA migrants attending a tropical diseases unit . These results suggest that both infections are prevalent in these individuals and increase the scarce knowledge about the possible relationship between these two parasites . Finally , our findings should encourage physicians to implement a systematic screening program for both infections in LA individuals . Further research is needed in order to explore this possible association and the underlying mechanisms .
|
Trypanosoma cruzi infection and strongyloidiasis are neglected tropical diseases , sharing a similar epidemiological burden in Latin America and producing life-long infections , leading to high morbidity and mortality . We conducted a case-control study in a non-endemic setting to evaluate a possible relationship between both infections . High prevalence of both diseases was found and importantly , T . cruzi infection was associated with a two-fold increase in the likelihood of strongyloidiasis even after adjusting for epidemiological variables . A possible explanation is that these two infections share an epidemiological burden where they are highly prevalent , but also the fact that both diseases are strongly influenced by socioeconomical factors such as soil contamination , barefoot walking or poor healthcare systems . Moreover , immune alterations produced by S . stercoralis may predispose to T . cruzi infection . As long as screening for Chagas disease in asymptomatic Latin American adults living in Europe has shown to be cost-effective and in light of the high prevalence of strongyloidiasis found in T . cruzi infected patients , a combined screening should be considered . The potential strongyloidiasis related complications and the benefits from ivermectin therapy are additional reasons to introduce systematic screening in susceptible populations .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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"invertebrates",
"medicine",
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"infectious",
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"helminthiases",
"organisms"
] |
2018
|
High prevalence of S. Stercoralis infection among patients with Chagas disease: A retrospective case-control study
|
Leprosy is the most common treatable peripheral nerve disorder worldwide with periods of acute neuritis leading to functional impairment of limbs , ulcer formation and stigmatizing deformities . Since the hallmarks of leprosy are nerve enlargement and inflammation , we used high-resolution sonography ( US ) and color Doppler ( CD ) imaging to demonstrate nerve enlargement and inflammation . We performed bilateral US of the ulnar ( UN ) , median ( MN ) , lateral popliteal ( LP ) and posterior tibial ( PT ) nerves in 20 leprosy patients and compared this with the clinical findings in these patients and with the sonographic findings in 30 healthy Indian controls . The nerves were significantly thicker in the leprosy patients as compared to healthy controls ( p<0 . 0001 for each nerve ) . The two patients without nerve enlargements did not have a type 1 or type 2 reaction or signs of neuritis . The kappa for clinical palpation and nerve enlargement by sonography was 0 . 30 for all examined nerves ( 0 . 32 for UN , 0 . 41 for PN and 0 . 13 for LP ) . Increased neural vascularity by CD imaging was present in 39 of 152 examined nerves ( 26% ) . Increased vascularity was observed in multiple nerves in 6 of 12 patients with type 1 reaction and in 3 of 4 patients with type 2 reaction . Significant correlation was observed between clinical parameters of grade of thickening , sensory loss and muscle weakness and US abnormalities of nerve echotexture , endoneural flow and cross-sectional area ( p<0 . 001 ) . We conclude that clinical examination of enlarged nerves in leprosy patients is subjective and inaccurate , whereas sonography provides an objective measure of nerve damage by showing increased vascularity , distorted echotexture and enlargement . This damage is sonographically more extensive and includes more nerves than clinically expected .
Leprosy is the most common treatable peripheral nerve disorder worldwide [1] . Leprosy is caused by a chronic granulomatous immune response to infection of the skin and nerves with Mycobacterium leprae , which resides in macrophages and Schwann cells and is the only bacterium known to affect myelination and cause peripheral neuropathy . Nerve damage , affecting mainly the ulnar ( UN ) , median ( MN ) , and posterior tibial ( PT ) nerves , results in nerve enlargement . Leprosy presents as a clinico-pathological spectrum [2] ranging from the localized paucibacillary tuberculoid form with anaesthetic hypopigmented skin patch ( TT ) to the generalized multibacillary , lepromatous leprosy . Between these poles are unstable forms of borderline tuberculoid , borderline borderline and borderline lepromatous leprosy . These are prone to episodic exacerbations ( reactions ) in 15–50% of patients during the course of the disease and after the completion of multidrug therapy . These states include type 1 ( reversal reaction ) , where only the skin patch shows inflammation with tenderness in the associated nerve , and type 2 [Erythema Nodosum Leprosum ( ENL ) ] reaction , manifesting with systemic symptoms of fever , erythematous nodules and joint pains . Though some nerve involvement may be seen in all types of leprosy , leprosy reactions lead to severe morbidity and acute neuritis requiring immediate treatment . Efforts to diagnose early ( or subclinical ) neuritis could ameliorate the nerve damage leading to functional impairment of limbs , ulcer formation and stigmatizing deformities . Hence , the most important goal in the management of leprosy is the prevention of disability via early detection of nerve impairment [1] . Careful clinical testing is useful , but can only detect the presence of neuropathy . However if neuropathy is found , there already is a substantial amount of nerve damage [1] . Nerve conduction studies or warm perception testing may improve early detection strategies , but these are usually not available in leprosy centers [1] . Since the hallmarks of leprosy are nerve enlargement and inflammation , we decided to use high-resolution sonography to demonstrate nerve enlargement ( even subclinically ) and inflammation . Inflammation can be detected by increased blood flow signals in the epi- and endoneurium of the involved nerves in leprosy patients [3] . Therefore , we performed an extensive sonographic study in 20 leprosy patients and compared the sonographic findings with 30 healthy Indian controls .
Thirty healthy volunteers 15 of each gender , aged between 17 to 58 years ( mean 33±10 ) without any evidence of diabetes , hypothyroidism , HIV and trauma-related peripheral nerve disease were included in the study to obtain normal values of the cross-sectional areas ( CSAs ) of the MN , UN , lateral popliteal ( LP ) and PT nerves . Twenty leprosy patients , diagnosed as per Ridley-Jopling classification [2] , who were in different stages of therapy with WHO multi-drug therapy [4] , were included for evaluation . The study was approved by Blue Peter Research Centre Ethics Committee during 6th IEC held on19th December 2007 . All the subjects were included in the study only after obtaining an informed written consent . All the volunteers and patients were examined by two clinicians trained in leprosy to assess bilaterally the UN , MN , LP and PT nerves . All the nerves were examined for their motor and sensory functions as follows . Sensory testing used Semmes-Weinstein monofilaments ( SW ) as previously described [6] , [7] . Sensory loss was considered to be present when the patient was unable to perceive 2 grams of target force on the hand and 300 grams target force on the foot by SW filaments . Muscle weakness was present when the MRC score was 4 or less . UN , LP and PTN were clinically graded after palpation as follows . Grade 0 was defined as a nerve not thicker than the contralateral nerve and with normal sensation; Grade 1 occurred when the affected nerve was thicker than the contralateral nerve; Grade 2 was a thickening of the affected nerve which felt rope-like; Grade 3 was a thickened nerve which felt beaded or nodular . Clinical grading of nerve thickening based on palpation could not be performed on MN due to its deeper location . Skin smears were taken from three sites for presence of acid-fast bacilli and to assess the Bacillary Index ( BI ) . Skin biopsy was performed to confirm the clinical diagnosis . All peripheral nerves were imaged by an independent radiographer blinded to the clinical diagnosis using US ( Voluson -730 Expert , GE medical , USA ) with broadband frequency of 10–14 MHz; CD frequency of 6–13 MHz and linear array transducer . Bilaterally , the MN at the wrist and forearm , the UN at the elbow and proximal to the medial epicondyle , LP at the fibula head and PT nerves at the ankle and proximal to the medial malleolus were examined and the length of abnormality of the nerve was determined by the presence of abnormal size and echo reflectivity of the nerves . All nerves were measured on transverse sections at a point where the nerve thickness was maximum in the visualized segment of the nerve . On transverse scans , the cross-sectional area of the nerve was determined from that area by one measurement within the hyperechoic rim surrounding the nerve . The echo reflectivity of the nerves assessed on imaging was arbitrarily graded as follows: mild = some hypo-reflectivity , moderate = obvious hypo-reflectivity; and severe = absence of any fascicular pattern . Color Doppler ( CD ) settings were chosen to optimize identification of weak signals from vessels with slow velocity . Pulse repetition frequency was set of 1 KHZ and Doppler gain was adjusted to the maximum level that thus not produce clutter . Band filter was set at 50 Hz . The presence of blood flow signals in the perineural plexus or intrafascicular vessels indicated hypervascularity of the nerve during CD imaging Statistical analysis was performed using SPSS software version 11/ graph pad prism version 4 . For comparison of group differences , the one-way nonparametric analysis of variance ( Kruskal-Wallis test ) or the Wilcoxon-Mann-Whitney test were used . For the comparison of proportions the χ2 test was used . Probability ( p ) values less than 0 . 05 were considered significant .
Thirty ulnar , median , and posterior tibial , and 23 lateral popliteal nerves ( not all female volunteers allowed the LP nerve to be examined due to cultural/social reasons ) were examined . On palpation , all the nerve trunks were of normal size ( grade 0 ) and not tender . On US , the peripheral nerves appeared as round to oval with occasional internal punctuate echoes giving a ‘honey comb pattern’ in transverse scans ( Fig . 1A ) , and as hypoechoic tubular structures with parallel linear internal echoes suggestive of ‘bundles of straw’ in longitudinal scans ( Fig . 1B ) . The epi- and perineurium were uniformly hyperechoic with an absence of endo- and epineural blood flow signals on CD imaging . The mean CSA for all 4 nerves showed no age or gender related differences ( p>0 . 1 ) . The ulnar nerve showed the highest mean CSA as compared to the other nerves ( table 1 ) .
This study demonstrates the usefulness of US in detecting nerve damage in leprosy . Our findings may have clinical and therapeutical consequences . Peripheral nerves are often enlarged in leprosy , and these are more accurately assessed by US than by clinical palpation . UN is the most commonly involved nerve . Nerve enlargement is more often present in patients with type 1 or type 2 reaction and the nerves of these patients often showed an increased vascularity in both the clinically involved nerves and in nerves far distant from those clinically affected . There is a growing interest in US as a diagnostic tool for diseases of the peripheral nervous system including mononeuropathies , polyneuropathies and peripheral nerve tumors [8]–[12] . US is noninvasive , amenable to studying structural changes in nerve sites that cannot be biopsied for histopathology , and is more cost effective than magnetic resonance imaging . Moreover , with US the nerve can be probed for a longer length than MRI examination which is limited to defined segments . Technical developments leading to improved image quality and reduced sizes of US equipment together with a reduction in price will make it possible for US to become a tool that can be used in countries where leprosy is still endemic . This is the second study that shows the value of US and CD as additional tools for evaluation of neuritis in leprosy [3] . Martinoli et al . [3] examined the median , ulnar and posterior tibial nerve in 23 leprosy patients ( 58 nerves ) both with sonography and MRI . Based on the sonographic ( or MR ) imaging appearance , a nerve could be classified as normal ( group I ) , enlarged with fascicular abnormalities ( group II ) or having no fascicular structure at all ( group III ) . The nerves in group II were thicker than in group III . The nerve swelling found in group II was gradual and fusiform , and typically occurred proximal to osteofibrous tunnels . However , their main finding was that nerves which showed a reversal reaction towards a more intense immune response had a hypervascular pattern demonstrated by Doppler studies ( or by a marked T2 intensity and increased gadolinium enhancement on MR ) . That study had some limitations . It took the authors 3 . 5 years to examine 23 consecutive patients , but more importantly , the patients had mean disease duration of 15 years compared to 24 . 7 months in our study . Moreover , only affected clinical nerves were examined by sonography or MRI , while we examined the MN , UN , LP and PT nerves systematically in all leprosy patients . Finally , they did not compare the imaging results with clinical findings . As expected , we also found that nerves are often enlarged in leprosy patients , especially in patients with a type 1 or 2 reaction . One of the three key signs of leprosy is the presence of enlarged nerves . Ascertaining the presence of enlarged nerves can be difficult [1] , and for some nerves this is impossible because of their location . Additionally , it is impossible to assess the length of nerve abnormality by palpation . There is considerable inter-observer variability in assessing the presence of enlarged nerves by palpation [1] , [13] . In contrast , US is a very precise assessment method as shown in a study of cadavers [14] . Furthermore , inter-observer agreement between sonographic measurements is excellent [9] . Our study clearly indicates that the kappa between clinical palpation and assessment of nerve size by sonography is low and that ( taking the earlier observations into account ) clinical palpation to assess nerve enlargement is inferior to US . It has to be emphasized that palpation of nerves in our study was performed by very experienced clinicians . We conclude that clinical examination of enlarged nerves is subjective and inaccurate , whereas sonography provides an objective measure of the nerve dimensions in addition to revealing structural changes over a longer length of the nerve . Besides enlargement , nerves in leprosy patients exhibited varying degrees of structural abnormalities such as fusiform enlargement or loss of fascicles , edema and increased neural vascularity . This confirmed earlier findings [3] . Nerves that showed increased blood flow signals in the endo/perineurium belonged to patients with leprosy reactions , as Martolini et al . also demonstrated . As compared to the nerve size or echotexture , the above feature discriminates leprosy reactions from non-reaction leprosy . However , in the present study , sonography was unable to discriminate between reversal and ENL reactions , although multiple nerve involvement was seen more often in ENL reactions . Interestingly , increased blood flow was seen in contralateral nerves and in multiple nerves distant to the affected dermal lesion , indicating that inflammation in the nerve may be more wide spread than suggested by the dermal lesions . We found that the more enlarged the nerve , the more often CD flow signals were present . Possibly , an increased blood flow signal in the nerve is the first sign of possible nerve damage . For example in 12 of 23 UN , CD flow signals were found while no sensory or motor nerve impairment was observed . Moreover , some nerves ( 8% ) have increased CD blood flow signals without being enlarged . A prospective study is ongoing to assess the presence of increased CD flow signals on the development of nerve enlargement and clinical nerve impairment . Our observations confirm the findings that nerve enlargements extend far proximal to the compression sites of the UN and MN [3] , occasionally with a nerve length abnormality of 22 cm . However , our preliminary data indicate that the maximum nerve enlargement is not just proximal to the possible compression sites , but for the MN approximately 4 cm from the proximal carpal tunnel inlet and for the UN 4–6 cm above the sulcus . This suggests that for these sites the temperature of the nerves could be lowest and more prone to infection by Mycobacteriun leprae , which is thought to favor lower body temperatures [15] , [16] . These findings need confirmation , since it may indicate that nerve release surgery at entrapment sites is based on inadequate assumptions . The increased neural vascularity taken together with interfascicular edema may reflect immune-mediated inflammation known to occur during leprosy reactions [17] , [18] . Though in general such nerves showed both abnormal echotexture and higher CSA , the exception of 7 nerves with normal CSA and echotexture leads us to believe that increased vascularity may be a better marker of acute neuritis associated with leprosy reactions . Increased CSA and abnormal echotexture may reflect chronic , long term effects of leprosy . We believe that using sonography , these processes and progressive nerve damage can be followed and a follow-up study is ongoing to assess the long-term value of US in leprosy . The anti-reaction treatment is discontinued upon clinical amelioration and some patients develop repeated leprosy reactions even after a full course of treatment . In such cases , color Doppler imaging may assist in judging the return to normalcy following neuritis and the time that anti-reaction treatment is needed .
|
Mycobacterium leprae , which causes leprosy , infects peripheral nerves resulting in functional impairment , ulcer formation and stigmatizing deformities . Early diagnosis of nerve involvement is important to avoid nerve related complications . We used non-invasive , high-resolution sonography ( US ) and color Doppler ( CD ) imaging to study the ulnar ( UN ) , median ( MN ) , lateral popliteal ( LP ) and posterior tibial ( PT ) nerves in 20 leprosy patients and compared 30 healthy Indian controls . The nerves were significantly thicker in the patients ( p<0 . 0001 for each nerve ) . One of the key signs of leprosy is the presence of enlarged nerves . The kappa for clinical palpation and nerve enlargement by sonography was 0 . 30 for all examined nerves . Increased neural vascularity , the sign of inflammation was observed in 26% ( 39/152 ) of nerves by CD imaging . Increased CD was observed in multiple nerves in 3 of 4 patients with type 2 reaction . Significant correlation was observed between clinical parameters of grade of thickening , sensory loss and muscle weakness and US abnormalities of nerve echotexture , endoneural flow and cross-sectional area ( p<0 . 001 ) . We conclude that sonography is a better diagnostic tool to predict nerve damage as compared to clinical assessment . Nerve damage was sonographically more extensive and was observed in nerves considered clinically normal .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/neglected",
"tropical",
"diseases"
] |
2009
|
High-Resolution Sonography: A New Technique to Detect Nerve Damage in Leprosy
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The sequential use of signaling pathways is essential for the guidance of pluripotent progenitors into diverse cell fates . Here , we show that Shp2 exclusively mediates FGF but not PDGF signaling in the neural crest to control lacrimal gland development . In addition to preventing p53-independent apoptosis and promoting the migration of Sox10-expressing neural crests , Shp2 is also required for expression of the homeodomain transcription factor Alx4 , which directly controls Fgf10 expression in the periocular mesenchyme that is necessary for lacrimal gland induction . We show that Alx4 binds an Fgf10 intronic element conserved in terrestrial but not aquatic animals , underlying the evolutionary emergence of the lacrimal gland system in response to an airy environment . Inactivation of ALX4/Alx4 causes lacrimal gland aplasia in both human and mouse . These results reveal a key role of Alx4 in mediating FGF-Shp2-FGF signaling in the neural crest for lacrimal gland development .
The lacrimal gland plays an essential role in protecting the ocular surface by secreting the aqueous components of the tear film . Defects associated with the lacrimal gland are the main cause of dry eye disease , which is highly prevalent in the geriatric population [1] . Left untreated , dry eye disease may progress from eye irritation and corneal scarring to eventual vision loss . However , lacrimal gland dysfunction is currently incurable and the common treatment option for the resulting dry eye pathology is the application of artificial tears that provides only temporary relief . Recent studies have shown that engraftment of lacrimal gland germ can restore lacrimation in animal models , but the procurement of lacrimal gland cells remains an unresolved challenge [2] . A better fundamental understanding of lacrimal gland development may inform cell-based therapies to repair or regenerate the lacrimal gland , which holds great promise for the treatment of dry eye disease [3] . The neural crest is a multipotent stem cell population that gives rise to many diverse tissues , including craniofacial bones and cartilage , smooth muscle , neurons and ganglia of the peripheral nervous system , adipose cells and melanocytes [4 , 5] . Upon induction at the neural plate border , the neural crest undergoes an epithelial-to-mesenchymal transition to delaminate from the dorsal neural tube . These cells then migrate to different regions of the embryo and differentiate into distinct cell types , guided by both their origins along the anterior-posterior axis and the signaling cues they are exposed to in their immediate environment [6] . Once at their destination , neural crest cells closely interact with their host organs , influencing their patterning and morphogenesis [7] . The cranial neural crest cells originating from the midbrain are the source of the periocular mesenchyme , which expresses the chemoattractive signal of Fgf10 to regulate lacrimal gland development [8 , 9] . By binding to Fgfr2b and heparan sulphate proteoglycan co-receptors , Fgf10 induces the invasion and branching of the lacrimal gland epithelium [8 , 10 , 11] . This essential role of Fgf10 in branching morphogenesis is conserved in glandular organs that include the lung , prostate , and pancreas . Nonetheless , the control of Fgf10 expression in the neural crest derived tissues remains unknown . In this study , we showed that FGF signaling mediated by the protein phosphatase Shp2 is required for the proper patterning and differentiation of the neural crest-derived mesenchyme to produce Fgf10 . Genetic evidence further demonstrates that Shp2 is recruited by Frs2 to activate Ras-MAPK signaling downstream to Fgfr1 and Fgfr2 but not to Pdgfrα in the neural crest . By differential gene expression analysis , we identified the homeodomain transcription factor Alx4 as the key effector of Shp2 signaling to control the expression of Fgf10 in the periocular mesenchyme . Importantly , the Alx4 binding sequence in the Fgf10 gene locus is conserved in land species from human to lizard , but not in aquatic animals such as frog and fish , which provides a new genetic insight into how the lacrimal gland arose as an evolutionary innovation of terrestrial animals to adapt to the dry environment . Alx4 conditional knockouts disrupted lacrimal gland development in mouse models and a homozygous ALX4 mutation causes lacrimal gland aplasia in human . Our results reveal a FGF-Shp2-Alx4-Fgf10 axis in regulating neural crest and lacrimal gland development .
FGF signaling is important for development of the neural crest derived craniofacial structures [12–18] . Using the neural crest specific Wnt1-Cre , we observed that conditional knockout of Fgfr1 resulted in significant craniofacial abnormalities , whereas deletion of Fgfr2 did not exhibit any obvious effect ( Fig 1A , 1C and 1E , arrows ) . Lacrimal gland development begins with the invasion of an epithelial bud from the conjunctiva into the periocular mesenchyme at embryonic day 14 . 5 ( E14 . 5 ) ( Fig 1B , arrowhead ) . In Fgfr1 and Fgfr2 single mutants , lacrimal gland development was mostly unaffected ( Fig 1D and 1F , arrowheads ) . Combined deletion of both Fgfr1 and Fgfr2 , however , abrogated lacrimal gland budding ( Fig 1G and 1H , arrows ) , indicating that Fgfr1 and Fgfr2 can compensate for each other in the neural crest during lacrimal gland development . Fgfr1ΔFrs and Fgfr2LR alleles encode the mutants Fgfr1 and Fgfr2 that lack the docking site for the adaptor protein Frs2 [16 , 19] . Although Fgfr2LR homozygous mice were viable and fertile , the craniofacial and lacrimal gland mutant phenotypes were observed in both the Wnt1-Cre;Fgfr1 f/ΔFrs;Fgfr2 f/f and Wnt1-Cre;Fgfr1 f/f;Fgfr2 f/LR mutants ( Fig 1I–1L , arrows ) . The essential role of Frs2 in the neural crest for lacrimal gland development was further demonstrated in Wnt1-Cre;Frs2f/f mutants , which displayed a less severe craniofacial phenotype than Fgfr mutants , but a similar cessation of lacrimal gland budding ( Fig 1M and 1N , arrows ) . Finally , lacrimal gland development was also aborted in Wnt1-Cre;Frs2f/2F animals , which carried mutations in two tyrosine residues of Frs2 ( Frs22F ) required for the binding of the Shp2 protein phosphatase ( S1 Fig , n = 6 ) [20] . In contrast , although Pdgfrα was expressed in the periocular mesenchyme and required for craniofacial development , its neural crest specific knockout failed to impair lacrimal gland development ( Fig 1O–1Q , arrows ) . These results demonstrated that lacrimal gland development specifically requires FGF-Frs2-Shp2 signaling in the neural crest . To investigate the potential downstream targets of neural crest FGF signaling occurring during lacrimal gland development , we next generated Wnt1-Cre;Shp2f/f mutants , which failed to develop a lacrimal gland as expected ( Fig 2A and 2B , dotted lines , n = 6 ) . Consistent with the idea that the neural crest is the main contributor of the periocular mesenchyme , immunostaining confirmed that Shp2 protein was depleted in the periocular mesenchyme , but preserved in the ectoderm-derived conjunctival epithelium ( Fig 2C and 2D , arrows and dotted lines ) . Although the epithelial cells maintained Pax6 and E-cadherin staining , there was no increase in Col2a1 expression , a hallmark of the nascent lacrimal gland bud ( Fig 2E–2H , dotted lines ) . By contrast , the periocular mesenchyme expression of Col2a1 was preserved , suggesting that the identity of these neural crest-derived cells was unchanged . The Wnt1-Cre transgene was recently reported to cause ectopic expression of Wnt1 in the midbrain-hindbrain boundary [21] . To ensure that this complication did not compromise our results , we used another neural crest-specific deletor , Sox10-Cre , to ablate Shp2 , which also resulted in the dysgenesis of the lacrimal gland ( S2A and S2B Fig , arrows ) . Altogether , these results show that Shp2 signaling in the neural crest is required for lacrimal gland budding in a non-cell autonomous manner . The initial budding of the lacrimal gland requires the inductive signal of Fgf10 that emanates from the periocular mesenchyme . In E13 . 5 control embryos , Fgf10 was found to exist in a ring-type pattern along the presumptive eyelid surrounding the eye ( Fig 2I , arrowheads ) , with the strongest signal occuring in the mesenchyme adjacent to the future lacrimal gland bud ( Fig 2I and 2K , arrows ) . In both Wnt1-Cre;Shp2f/f and Sox10-Cre;Shp2f/f mutants , however , Fgf10 was absent in the entire periocular mesenchyme ( Fig 2J and 2L , arrows and arrowheads , and S2C and S2D Fig ) . As a result , ERK phosphorylation was maintained in the adjacent retina but abolished in the conjunctival epithelium ( Fig 2M and 2N , dotted lines ) , suggesting a specific loss of FGF signaling in the lacrimal gland primordia . This evidence was further supported by the observed down regulation of FGF signaling response genes , Etv4 and Etv5 , in the presumptive lacrimal gland epithelium ( Fig 2O–2R , dotted lines ) . Considering the essential role of Fgf10 signaling in inducing lacrimal gland budding , we concluded that the absence of Fgf10 expression accounted for the lacrimal gland aplasia seen in neural crest Shp2 mutants . FGF signaling is known to activate the Ras family of small GTPases , which play important roles in cell proliferation , migration and differentiation . Previous studies have identified multiple downstream targets of Ras , including Raf kinases , type I phosphoinositide ( PI ) 3-kinases , Ral guanine nucleotide exchange factors , the Rac exchange factor Tiam1 , and phospholipase C3 [22] . Among these molecules , Raf kinases activate the mitogen-activated protein kinase ( MAPK ) cascade that culminates with the phosphorylation of Mek kinases ( Mek1 and 2 ) and their direct Erk kinase targets ( Erk1 and 2 ) [23] . At E10 . 5 , ETS transcription factors Etv1 , 4 and 5 were strongly expressed in tissues known to have active FGF signaling ( Fig 3A , arrows ) . In both Wnt1-Cre;Shp2f/f and Wnt1-Cre; Mek1f/f;Mek2-/- embryos , these expression patterns were significantly down regulated in the cranial neural crest-derived mesenchyme in the midbrain , branchial arches and nose ( Fig 3A , arrowheads ) , supporting the claim that Shp2 and Mek operate in the same signaling cascade leading to Etv1 , 4 , and 5 expression . Furthermore , lacrimal gland development was never initiated after the genetic removal of Mek1/2 in the neural crest ( Fig 3B , arrowhead , n = 8 ) . Interestingly , however , a small lacrimal gland protrusion was seen in Wnt1-Cre; Erk1-/-;Erk2f/f embryos , suggesting that Mek may have additional key targets other than Erk ( Fig 3B , arrowhead , n = 2 ) that participate in budding morphogenesis . Furthermore , by taking advantage of a conditional allele of oncogenic Kras ( LSL-KrasG12D ) , we showed that constitutively active Ras signaling in the neural crest rescued the Shp2 deficiency during lacrimal gland budding ( Fig 3B , arrow , n = 10 ) , supporting the downstream role of Ras-MAPK activation in the FGF-Shp2 signaling cascade in the neural crest [24–27] . The faithful expression of Etv1 , 4 and 5 in response to Ras-MAPK activity prompted us to investigate the functional significance of these three transcription factors . Surprisingly , even the combined inactivation of Etv1/4/5 in the neural crest lineage failed to perturb lacrimal gland development ( Fig 3C , n = 8 ) , suggesting that these genes may be compensated by other ETS domain transcription factors that share similar binding specificity . To overcome this genetic redundancy , we used a Cre-inducible transgene ( R26-EtvEnR ) to express Etv4 fused with the Engrailed repressor domain , which acts as a dominant negative ETS transcription factor [28] . Wnt1-Cre; R26-EtvEnR embryos not only exhibited the previously observed craniofacial defect ( Fig 3D , arrow ) , but also showed reduced elongation of the lacrimal gland ( Fig 3D , arrowheads , n = 8 ) . This result suggests that ETS domain transcription factors are downstream effectors of FGF-Shp2-Ras-MAPK signaling in neural crest development . FGF signaling has been implicated in the induction , proliferation , migration and differentiation of neural crest cells [13 , 29–32] . The periocular mesenchyme originates from the neural tube in the midbrain , where active FGF signaling indicated by Etv5 expression coincides with Fgf8 expression ( Fig 4A , arrows ) . This suggests that Fgf8 may activate FGF signaling during the induction of cranial neural crest cell progenitors . Considering that Fgf15 is also expressed in the midbrain , we ablated Fgf8 in the midbrain-hindbrain junction using En1-Cre in the Fgf15 null background . As expected , both Fgf8 and Etv5 midbrain expressions were absent in En1-Cre;Fgf8f/f;Fgf15-/- embryos ( Fig 4A , arrowheads ) , demonstrating a loss of FGF signaling . Nevertheless , the lacrimal gland bud still developed normally in these mutants ( Fig 4A , asterisks; n = 3 ) , showing that FGF signaling at the induction of cranial neural crest cells is not required for lacrimal gland development . After induction at the dorsal neural tube , the neural crest progenitors express Sox10 as they begin to migrate toward their final destination . At E10 . 5 , although Sox10-positive neural crest cells were present in the cranial mesenchyme in Wnt1-Cre;Shp2f/f mutants , both their number and extent of migration were slightly reduced as compared to those in the control embryos ( Fig 4B , arrows ) , suggesting that the loss of Shp2 produces subtle defects in neural crest proliferation and migration . This phenotype was reproduced in Wnt1-Cre; Mek1f/f;Mek2-/- embryos , but ameliorated in Wnt1-Cre;Shp2f/f;LSL-KrasG12D embryos ( Fig 4B , arrowheads ) , supporting a role for Shp2-Ras-MAPK signaling in post-inductive neural crest cells . Previous studies in zebrafish suggested that Shp2 may have a MAPK-independent function in preventing p53-mediated apoptosis in the neural crest [26] . Using lysotracker dye to stain acidic lysosomes in cells undergoing apoptosis , we observed extensive cell death in the first pharyngeal arch in E10 . 5 Shp2 mutant embryos ( Fig 4C , arrows ) . In sections , cleaved-caspase 3 staining also detected abnormal cell apoptosis in the periocular mesenchyme , although the apoptotic regions were far removed from the conjunctiva ( Fig 4C , arrowheads ) . We reasoned that if the apoptosis induced by the Shp2 deletion was indeed dependent on p53 , then the apoptotic events may be avoided by the removal of p53 . However , ablation of p53 in Shp2 mutants failed to prevent cell death in the first pharyngeal arch or to rescue any craniofacial phenotype ( Fig 4C , arrows and arrowheads ) . Further , in lacrimal gland development , budding morphogenesis was still aborted in Wnt1-Cre; Shp2f/f;p53f/f embryos ( Fig 4C , asterisks , n = 6 ) . Therefore , p53 was not responsible for either the neural crest cell death or the lacrimal gland aplasia observed in Shp2 mutants . To determine whether these early onset neural crest defects affect periocular mesenchyme development , we crossed Wnt1-Cre mice with those containing the R26R Cre reporter to follow the fate of the neural crest cells . Interestingly , by the time of lacrimal gland budding at E13 . 5 , the periocular mesenchyme adjacent to the conjunctival epithelium was already occupied by the neural crest derived cells in Shp2 mutants ( Fig 4D , arrows ) . Furthermore , the expression of Pitx2 and Foxc1 , two markers of the neural crest derived periocular mesenchyme , were similar in wild-type control and Shp2 mutant eyes ( Fig 4E , arrows ) . Therefore , despite causing an initial delay in neural crest migration and abnormal apoptosis , Shp2 ablation did not disrupt the occupancy of the periocular mesenchyme by the neural crest-derived cells at the time of lacrimal gland budding . We thus concluded that the subtle neural crest migration , survival and proliferation defects seen in Shp2 mutants were unlikely to account for the complete failure of lacrimal gland development . To determine the molecular basis of the lacrimal gland defect observed in Shp2 mutants , we isolated the E14 . 5 periocular mesenchyme via laser capture micro-dissection and subsequently performed RNAseq analysis ( Fig 5A ) . Among genes that were downregulated at least two folds in Shp2 mutants , the third and eighth most highly expressed transcription factors were Alx4 and Alx1 , respectively ( Fig 5B ) . These results were confirmed by a qPCR analysis of micro-dissected tissues , which also showed significant reductions in Shp2 and Fgf10 expressions as expected ( Fig 5C ) . We next focused on Alx4 and Alx1 as downstream targets of Shp2 signaling . At both E10 . 5 and E11 . 5 , Alx4 was widely expressed in the cranial mesenchyme surrounding the wild-type eye , but the expression was moderately reduced in Shp2 mutants ( Fig 5D , arrows ) . At E12 . 5 , a more pronounced reduction of Alx4 expression was evident at the temporal side of the mutant eye , where the lacrimal gland bud would have normally emerged . By E13 . 5 , Alx4 expression was absent in all areas of the periocular region except the dorsal side , but recovered in Wnt1-Cre;Shp2f/f;LSL-KrasG12D embryos ( Fig 5D , arrowheads ) . Immunostaining on sections further confirmed that Shp2 deletion led to a progressive down regulation of Alx4 in the periocular mesenchyme , until it was entirely lost by E14 . 5 ( Fig 5E , arrows ) . Similarly , Alx1 in control wild-type embryos was expressed just anterior to the elongating lacrimal gland bud at E14 . 5 , but this domain of Alx1 expression eventually vanished in Shp2 mutant embryos ( Fig 5E , arrowheads ) . These results demonstrate that the periocular expressions of both Alx1 and Alx4 are regulated by Shp2 signaling . The results above revealed a close resemblance of Alx1 and Alx4 expressions in the periocular mesenchyme to that of Fgf10 during embryonic development . To evaluate this further , we examined their expression patterns in the neonatal lacrimal gland . At postnatal day 0 ( P0 ) , Fgf10 was detectable in the mesenchymal cells , whereas the FGF-inducible gene Etv5 was expressed in the adjacent ducts and acini , suggesting that FGF signaling remained active at this stage ( Fig 6A , arrows ) . As expected , both Alx1 and Alx4 mRNA were also found in the lacrimal gland mesenchyme . Through immunostaining , we further demonstrated that the P3 lacrimal gland expressed the Alx4 protein , which was separated from both the epithelial marker Pax6 and the myoepithelial marker SMA ( Fig 6B ) . Finally , to trace the origin of these Alx4-expressing cells in the lacrimal gland , we crossed Wnt1-Cre with an R26TdT ( Ai14 ) reporter to indelibly label the neural crest-derived cells with tdTomato fluorescence . We then confirmed through immunostaining that Alx4 resided exclusively in the tdTomato-positive cells , demonstrating that Alx4 persisted in the neural crest lineage throughout lacrimal gland development . Based on the similarities observed between Alx1/4 and Fgf10 expression patterns during lacrimal gland development , we hypothesized that Alx1 and Alx4 were direct regulators of Fgf10 transcription . Because formation of the lacrimal gland was an adaptation of terrestrial animals to an airy environment , we searched the Fgf10 locus for regions that were evolutionarily conserved from human to chicken but not in stickleback fish ( Fig 6C ) . We next overlaid these regions with DNase hypersensitive sites in a 3T3 fibroblast cell line identified by the ENCODE project , because this cell line expressed both Alx4 and Fgf10 at high levels [33] . Finally , we screened these sequences using the Alx1/3/4 binding motif and identified a perfect match within intron 1 of Fgf10 ( Fig 6D ) . Interestingly , sequence alignment showed that this site was evolutionarily conserved among reptiles that have the lacrimal gland , such as the lizard , but not in Xenopus frog , which lacks one ( Fig 6C ) [34] . To ascertain whether this sequence was a bona fide Alx binding site , we performed chromatin immunoprecipitation in 3T3 cells followed by qPCR using specific primers . Compared to the IgG control , there was a ~3 fold enrichment of this putative Alx binding element in chromatins pulled down by the Alx4 antibody ( Fig 6E ) . This was further validated in vivo by Alx4 chromatin immunoprecipitation using the lacrimal gland mesenchyme isolated from neonatal pups , which resulted in a ~11 fold enrichment . We next knocked down Alx1 and Alx4 using siRNAs in cultured lacrimal gland mesenchymal cells ( Fig 6F ) . Interestingly , Alx1 depletion led to a modest reduction in Fgf10 mRNA levels , but the effect was not statistically significant ( Fig 6G ) . In contrast , the Alx4 knockdown decreased Fgf10 expression by ~50% , which was not further reduced by the combined treatment of both Alx1 and Alx4 siRNAs . This result suggested that Alx4 plays a more dominant role than Alx1 in regulating Fgf10 within the lacrimal gland mesenchyme . To determine the functional role of Alx4 in lacrimal gland development , we analyzed Alx4lst-J mice , which carried a frameshift mutation that removed both the homeodomain and downstream CAR domain . Homozygous Alx4lst-J animals displayed craniofacial defects , dorsal alopecia and preaxial polydactyly at birth as previously reported in Alx4 knockouts [35 , 36] . At E14 . 5 , Alx4lst-J homozygous embryos maintained normal expression levels of Connexin43 and Col2a1 in the periocular mesenchyme , but the domain of Alx1 expression was more restricted ( Fig 7A , arrows ) . Importantly , there was a drastic reduction of Fgf10 adjacent to the lacrimal gland bud , accompanied by a downregulation of FGF-target genes Etv4 and Etv5 in the lacrimal gland epithelium ( Fig 7A , dotted lines ) . At E16 . 5 , histology and immunostaining revealed a complete loss of Alx4 expression in the periocular mesenchyme and a much shorter Pax6-expressing lacrimal gland bud , characterized by reduced phospho-Histone H3 ( pHH3 ) and increasing TUNEL signal ( Fig 7B , dotted lines ) . By P1 , no lacrimal gland was detectable by Carmine staining in Alx4lst-J homozygous pups ( Fig 7B , black arrows ) . These results demonstrated that inactivation of Alx4 markedly disrupted Fgf10 expression and downstream FGF signaling , affected cell proliferation and survival , and ultimately caused a failure of lacrimal gland development . In human , ALX4 loss-of-function mutations underlie autosomal recessive frontonasal dysplasia 2 syndrome , characterized by skull defects , wide nasal bridge , notched nares , depressed nasal tip , hypertelorism and alopecia ( OMIM 613451 ) . We reanalyzed one patient carrying a homozygous c . 503delC mutation in exon 2 of the ALX4 gene , which resulted in the truncation of the homeobox ( HD ) and C-terminal OAR domains [37] . MRI imaging in that patient revealed a bilateral absence of lacrimal glands ( Fig 7C , arrows ) . The patient lacked tearing and experienced irritable eyes and multiple episodes of eye infection since birth . This finding is consistent with the role of ALX4 in human lacrimal gland formation .
In this study , we show that FGF signaling in the neural crest is required for Fgf10 production within the periocular mesenchyme , thereby triggering a second round of FGF signaling in the conjunctival epithelium to form the lacrimal gland ( Fig 7D ) . This is mediated by Frs2 and Shp2 , which together activate the Ras-MAPK pathway to control the survival , migration and differentiation of the cranial neural crest cells . The downstream effector of Shp2 signaling in the periocular mesenchyme is the homeodomain transcription factor Alx4 , which binds a terrestrially conserved element to regulate Fgf10 expression in the periocular mesenchyme , reflecting the evolutionary history of the lacrimal gland . Our results highlight the sequential use of FGF signaling in neural crest development and reveal the etiology of lacrimal insufficiency in an ALX4 patient . RASopathies represent a spectrum of congenital abnormalities caused by aberrant Ras-MAPK signaling , but the particular RTK signaling pathway mediated by Ras in the normal development of a specific tissue is not always clear [38 , 39] . Using mouse genetics , we showed that defective FGF signaling , and not PDGF signaling , in the neural crest reproduced the Shp2 conditional knockout phenotype seen in the lacrimal gland , thereby positioning FGF receptors as the primary regulators of Shp2 function in the neural crest cells that partake in directing the development of the lacrimal gland . Contrary to a previous study in zebrafish , we did not observe that Shp2 acts upstream of p53 to suppress neural crest cell apoptosis [26] . This discrepancy could be due to differences either intrinsic to the species used or to the experimental approaches utilized as we took advantage of conditional knockouts in mice whereas the zebrafish study used a morpholinos knockdown . Instead , our genetic evidence demonstrates a fundamental role for the Shp2-Ras-Mek-Erk signaling cascade in neural crest survival and development . MAPK is known to phosphorylate and induce the ETS domain transcription factors , which act as downstream effectors in gene regulation . In particular , the expressions of Pea3 family genes Etv1/4/5 correlate closely with FGF signaling activities during embryonic development [10] . While deletion of all three Pea3 family genes in the neural crest failed to produce any craniofacial or lacrimal gland defects , the overexpression of a dominant-negative Etv4 lead to stunted lacrimal gland growth . This suggests that other members of the ETS domain transcription factors , which recognize similar binding sites as Etv1/4/5 , can play redundant roles in transmitting FGF-MAPK signaling during neural crest development . Our study demonstrates that Alx genes are the ultimate downstream effectors of Shp2 signaling in the periocular mesenchyme . Alx4 shares both sequence and structural homologies of paired-type homeodomain and C-terminal aristaless domain with two other transcription factors , Alx1 and Alx3 . These proteins are present within the craniofacial mesenchyme and limb bud , displaying overlapping expression patterns [40] . Members of this family of transcription factors also exhibit functional redundancies as shown by genetic interactions in specific tissues . Alx3 knockout mice were morphologically normal , but Alx3/4 double mutants displayed more severe defects in the neural crest-derived craniofacial structures than the Alx4 knockout alone [40] . Alx1 null mice showed craniofacial defects distinct from Alx4 mutants and combined deletion of both genes led to developmental abnormalities not found in either of the single mutants , indicating that Alx1 and Alx4 have both unique and redundant roles [36] . The lacrimal gland mesenchyme expresses Alx1 and Alx4 , but not Alx3 . Although we did not observe a synergistic effect of Alx1 and Alx4 in our in vitro experiments , it remains possible that Alx4/Alx1 double knockout mice will present comparably severe lacrimal gland defects as the neural crest Shp2 mutant did . The precise level of FGF10/Fgf10 expression in the periocular mesenchyme is critical for lacrimal gland induction . This is clearly shown by aplasia of the lacrimal and salivary glands ( ALSG ) and Lacrimo-auriculo-dento-digital ( LADD ) syndromes , in which even heterozygous mutations in human FGF10 can lead to congenital lacrimal gland defects [41 , 42] . Our study has demonstrated that neural crest FGF signaling is required for Fgf10 expression in the periocular mesenchyme , but the ligand of the neural crest FGF signaling that leads to lacrimal gland development remains an open question . It is unlikely to be the autocrine signaling of Fgf10 , because deletion of Fgfr2 , the cognate receptor for Fgf10 , in the neural crest only produced minor defects in lacrimal gland development ( Fig 1 ) . In limb development , the mesenchyme-derived Fgf10 signals the epithelium to induce Fgf8 and later Fgf4 , Fgf9 and Fgf17 , which in turn act on Fgfr1 and Fgfr2 in the mesenchyme to maintain Fgf10 expression [43–45] . During lung development , Fgfr1 and Fgfr2 in the mesenchyme respond to Fgf9 expressed by the lung epithelium and mesothelium . This maintains the mesenchymal expression of Fgf10 that signals back to the epithelium [46 , 47] . Submandibular salivary gland development is yet another example where the epithelium-mesenchyme interaction plays an important role . In this case , Fgf10 in the mesenchyme originated from the cranial neural crest is modulated by ectodermal-derived Fgf8 [48] . However , neither a systemic knockout of Fgf9 nor deletion of Fgf8 using Cre transgenes specific to the midbrain-hindbrain junctiondisrupt lacrimal gland development ( S3 Fig and Fig 4A ) . Considering the complexity of the FGF family , further work is needed to identify the relevant FGF ligands for the neural crest FGF signaling pathway during lacrimal gland development . The main and accessory lacrimal glands secrete the aqueous component of the tear film , and thereby play an important role in maintaining the health and transparency of the ocular surface . Because the tear is only necessary for land animals whose eyes are constantly exposed to the air , the lacrimal gland emerged relatively late in the evolution of the vertebrate tetrapod . Even among animals living both on land and in water , the lacrimal gland is only present in reptiles such as the alligator , but not in amphibians such as the frog ( S4 Fig ) . In this study , we show that the Alx4 binding site in the Fgf10 locus lies within a region that’s conserved from humans to alligators , but not in frogs or fish . This suggests that , although both Alx4 and Fgf10 arose in more primitive organisms , these two genes were most likely not functionally linked until the emergence of the lacrimal gland in reptiles . Considering that Fgf10 lies at the top of the genetic cascade for inducing branching morphogenesis in many glandular organs , this represents an example of evolution that coopts an existing genetic circuitry to develop new organs that enable the adaptation to new environments . By showing that the Alx4-Fgf10 axis is conserved from mouse to human , our study contributes to the understanding of the role of Alx4 in human neural crest cell and lacrimal gland development and points in the direction of generating the lacrimal gland from pluripotent stem cells .
The animal experiments were approved by Columbia University Institutional Animal Care and Use Committee ( IACUC ) . Mice carrying Erk1-/- , Erk2flox , Frs2αflox , Frs2α2F , Mek1flox , Mek2KO , Shp2flox alleles were bred and genotyped as described [20 , 49–52] . We obtained Etv1flox mice from Dr . Silvia Arber ( University of Basel , Basel , Switzerland ) , Etv4-/- and Etv5flox mice from Dr . Xin Sun ( University of California at San Diego , San Diego , CA ) , En1-Cre and R26-EtvEnR from Dr . James Li ( University of Connecticut Health Center , Farmington , CT ) , Fgf8flox from Dr . Suzanne Monsour ( University of Utah , Salt Lake city , UT ) , Fgf15-/- from Dr . Steven Kliewer ( UT Southwestern Medical Center , Dallas , TX ) , Fgfr1ΔFrs from Dr . Raj Ladher ( RIKEN Kobe Institute-Center for Developmental Biology , Kobe , Japan ) , Fgfr2LR from Dr . Jacob V . P . Eswarakumara ( Yale University School of Medicine , New Haven , CT ) and Fgf9-/- and Fgfr2flox from Dr . David Ornitz ( Washington University Medical School , St Louis , MO ) [16 , 19 , 28 , 53–58] . LSL-KrasG12D mice was obtained from the Mouse Models of Human Cancers Consortium ( MMHCC ) Repository at National Cancer Institute [59] . Alx4lst-J ( Stock No: 000221 ) , Fgfr1flox ( Stock No: 007671 ) , p53flox ( Stock No: 008462 ) , Pdgfrαflox ( Stock No: 006492 ) , R26R ( Stock No: 003474 ) , R26RTdT ( Ai14 , Stock No: 007914 ) , Sox10-Cre ( Stock No: 025807 ) and Wnt1-Cre ( Stock No: 009107 ) mice were obtained from Jackson Laboratory [16 , 40 , 60–64] . Animals were maintained on mixed genetic background . Wnt1-Cre or Shp2flox only mice did not display any lacrimal gland phenotypes and were used as controls . Histology , carmine staining , TUNEL assays and immunohistochemistry are performed as previously described [11 , 65] . The following primary antibodies were used: Alx4 ( sc-33643 , Santa Cruz Biotechnology ) , E-cadherin ( U3254 , Sigma , St Louis , Missouri ) , Cleaved-caspase 3 ( #9664 , Cell signaling Technology ) , Col2a1 ( ab34712 , Abcam ) , Connexin43 ( #3512 , Cell signaling Technology ) , pHH-3 ( #06–570 , Millipore ) , Pax6 ( PRB-278P , Covance , Berkeley , CA , USA ) , RFP ( #600-401-379 , Rockland ) , α-SMA ( #C6198 , Sigma-Aldrich ) . E13 . 5 embryos were incubated in 4% PFA for 1 hr at 4°C and washed twice in PBS containing 0 . 02% NP-40 , 0 . 01% sodium deoxycholate and 2 μg/ml MgCl2 for 30 min each , followed by overnight incubation in X-gal staining solution ( 1 mg/ml X-gal , 10 mM Potassium Ferricyanide , 10nM Potassium Ferrocyanide , 2 μg/ml MgCl2 in PBS ) at 4°C . The samples were then cryopreserved in OCT ( Sakura Finetek ) , sectioned at 10 μm thickness and counter-staining with nuclear red . RNA in situ hybridization was performed as previously described [66] . The following probes were used: Alx1 ( from Dr . Terence Capellini , Harvard University , Boston , MA ) , Alx4 ( from Dr . Yang Chai , University of Southern California , Los Angeles , CA ) , Etv4 , Etv5 ( from Dr . Bridget Hogan , Duke University Medical Center , Durham , NC , USA ) , Foxc1 ( from Dr Anthony Firulli , Indiana University School of Medicine , Indianapolis , IN , USA ) , Fgf10 ( for whole mount ) ( from Dr . Suzanne Monsour , University of Utah , Salt Lake city , UT ) ) , Fgf10 ( for sections ) was generated from a full length cDNA clone ( IMAGE: 6313081 Open Biosystems , Huntsville , AL , USA ) , Pitx2 ( from Dr . Valerie Dupé , CNRS , Strasbourg , France ) . Freshly harvested embryos were frozen in the OCT , sectioned at 10μm thickness and transferred to PEN slides ( Zeiss ) . Slides were dipped in 95% ethanol for 2 min to fix the samples and stained with crystal violet stain ( 3% in ethanol ) on ice . This was followed by dipping in 70% ethanol for 30–40 sec to remove the OCT and dehydration in 100% ethanol for 2 min . The periocular mesenchymal tissue was micro-dissected using Laser capture microscope ( Zeiss AxioObserver . Z1 inverted microscope ) . 500 pg of RNA was isolated from each sample , converted to cDNA and amplified using Nugen Ovation kit ( Nugen ) to obtain 2–3 μg cDNA , which was then converted to cDNA library for RNA-sequencing analysis at core facility in Columbia University . The RNAseq data is available at the GEO repository under accession number GSE103402 . Lacrimal glands mesenchymal culture was performed as described previously [67] . Briefly , glands were isolated from P0-P2 pups and trypsinized ( Gibco 1:250 ) at 4°C for 1 hr . After neutralizing trypsin , the mesenchyme was manually separated from the epithelium using fine needle and grown in the complete medium ( DMEM+10% FBS with antibiotics ) for 3 days before passage . The primary mesenchymal cells were transfected with siRNA using Lipofectamine RNAimax as previously described and harvested after 24–48 hrs [68] . For Alx1 and Alx4 , the results were confirmed using two different predesigned Silencer® Select siRNAs from Ambion ( Life technologies ) . Quantitative-PCR was performed as described [69] . Primer sequences used were , Alx4: 5’-ACACATGGGCAGCCTGTTTG3’ , 5-TGCTTGAGGTCTTGCGGTCT-3’ , Alx1: 5’ GGAGGAAGTGAGCAGAGGTG-3’ , 5’- TTCAAATGCGTGTCCGTTGGT-3’ , Fgf10: 5’ CAATGGCAGGCAAATGTATG-3’ , 5’- GGAGGAAGTGAGCAGAGGTG-3’ , Gapdh: 5’-AGGTCGGTGTGAACGGATTTG-3’ , 5’-TGTAGACCATGTAGTTGAGGTCA-3’ , Shp2 ( exon 4 ) : 5’- CTGACGGAGAAGGGCAAGCA-3’ , 5’- CGCACGGAGAGAACGAAGTCT-3’ . The Chromatin Immunoprecipitation ( ChIP ) assays were performed in 3T3 fibroblasts cells and primary lacrimal gland mesenchymal cells as described [70] . Briefly , the cells grown in DMEM/10% FBS with antibiotics were crosslinked with 1% Formaldehyde for 8–10 min with gentle shaking . This was followed by quenching with 125 mM glycine or 5 min , 3X washing with cold PBS and addition of 1 ml of cold CHIP lysis buffer . After incubation for 10 min at 4°C , the lysed cells were centrifuged at 3000 rpm for 3 min and the pellet were stored at -80°C until later use . The pellet was then resuspended in 1 . 2 ml of RIPA buffer , sonicated on ice for 8 min using probe sonicator ( 1 sec “on” , 2 sec “off” , power 3 . 5 ) and centrifuged at 13000 rpm for 15 min at 4°C . The supernatant was precleared by adding 45 μl Protein G agarose beads ( 50% slurry , Millipore ) and incubated for 2 hrs at 4°C on rotor . After centrifugation at 5000 rpm for 1 min , the supernatant was transferred to a fresh tube and the protein concentration was measured by Bradford assay . For pull down , 1 μg of antibodies were added per 1mg of protein for overnight incubation at 4°C , followed by addition of 20 μl agarose beads for another 1–2 hours incubation . After brief centrifugation , the beads were washed 1X with RIPA buffer at room temperature , 3X with cold RIPA buffer , 2X with cold Wash buffer A and Wash buffer B , 1X with TE/150mM NaCl . Next , the samples were decrosslinked in Elution buffer containing RNAase ( 40μg/ml ) and Proteinase K ( 20μg/ml ) for 1 hr at room temperature and 50°C overnight . After brief centrifugation , the supernatant was treated with equal vol . of Phenol/Chloroform and the DNA was precipitated with 2 . 5 vol . of 100% ethanol and Glycoblue for 1 hr at -80°C and dissolved in 20 μl sterile water for qPCR analysis . The antibodies used were IgG as isotype control ( sc-2028 , Santa Cruz Biotechnology ) and anti-Alx4 ( sc-22066 , Santa Cruz Biotechnology ) . Buffer recipes: CHIP lysis buffer- 10mM Tris-Cl , pH8 , 85mM KCl , 0 . 5% NP-40 , 5nM EDTA , 0 . 25% Triton; RIPA- 1% Triton , 150mM NaCl , 0 . 1% SDS , 0 . 1% Na-Deoxycholate , 10mM Tris-Cl , pH8 , 5mM EDTA; Wash buffer A- 50mM HEPES , pH7 . 9 , 500mM NaCl , 1mM EDTA , 1% Triton , 0 . 1% Na-deoxycholate , 0 . 1% SDS , Wash buffer B- 20mM Tris-Cl , pH8 , 1mM EDTA , 250 mM LiCl , 0 . 5% NP-40 , 0 . 5% Na-deoxycholate; Elution Buffer- 1% SDS , 30 mM Tris-Cl ( pH8 ) , 15mM EDTA , 200mM NaCl . Protease inhibitor cocktail is added prior to use in all the buffers until ready to elute . The primers used for CHIP in intron 1 of Fgf10- F- 5’-GGTTGGAGCTTGTTGTGTGT-3’ , R- 5’-GCTCTGCTAATAAAGGTCTCCC-3’ . We retrieved 200 KB upstream and 100 KB downstream of Fgf10 transcription start site from Mouse Genome assembly GRCm38/mm10 and analyzed this sequence for evolutionary conservation using UCSC genome browser . These sequences were also overlaid with the DNase-hypersensitivity data from 3T3 cell line retrieved from ENCODE database and scanned for Alx4 consensus binding sites based on TRANSFAC ( release 2013 . 1 ) database using MATCH algorithm , with minFP as parameter to identity sites with minimum false positives .
|
The dry eye disease caused by lacrimal gland dysgenesis is one of the most common ocular ailments . In this study , we show that Shp2 mediates the sequential use of FGF signaling in lacrimal gland development . Our study identifies Alx4 as a novel target of Shp2 signaling and a causal gene for lacrimal gland aplasia in humans . Given this result , there may also be a potential role for Alx4 in guiding pluripotent stem cells to produce lacrimal gland tissue . Finally , our data reveals an Alx4-Fgf10 regulatory unit broadly conserved in the diverse array of terrestrial animals from humans to reptiles , but not in aquatic animals such as amphibians and fish , which sheds light on how the lacrimal gland arose as an evolutionary innovation of terrestrial animals to adapt to their newfound exposure to an airy environment .
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2017
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Alx4 relays sequential FGF signaling to induce lacrimal gland morphogenesis
|
Infection of bones and joints remains one of the most commonly described complications of brucellosis in humans and is predominantly reported in all ages and sexes in high-risk regions , such as the Middle East , Asia , South and Central America , and Africa . We aimed to systematically review the literature and perform a meta-analysis to estimate the global prevalence of osteoarticular brucellosis ( OAB ) . Major bibliographic databases were searched using keywords and suitable combinations . All studies reporting the incidence and clinical manifestations of osteoarticular brucellosis in humans , and demonstrated by two or more diagnostic methods ( bacteriological , molecular , serological , and/or radiographic ) were included . Random model was used , and statistical significance was set at 0 . 05% A total of 56 studies met the inclusion criteria and were included in the systematic review and meta-analysis . There was an evidence of geographical variation in the prevalence of osteoarticular disease with estimates ranging from 27% in low-risk regions to 36% in high-risk regions . However , the difference was not significant . Thus , brucellosis patients have at least a 27% chance of developing osteoarticular disease . The prevalence of OAB is not dependent on the endemicity of brucellosis in a particular region . Hence , further research should investigate the potential mechanisms of OAB , as well as the influence of age , gender , and other socioeconomic factor variations in its global prevalence , as this may provide insight into associated exposure risks and management of the disease .
Brucellosis is a neglected disease worldwide and a growing public health concern in high-risk countries . It is caused by facultative , intracellular Brucella species . Brucella abortus ( cattle ) , Brucella melitensis ( goats and sheep ) , and Brucella suis ( pigs ) are known to be the most pathogenic to their target hosts as well as humans [1–5] . Humans are considered incidental hosts of brucellosis , and can acquire the disease via various routes , including oral , conjunctival , respiratory , cutaneous , transplancental , blood , and rarely by bone marrow transplantation [1 , 2 , 6–8] . However , infection is typically by direct exposure to contaminated animal products ( e . g . consumption of unpasteurized milk ) , genital secretions , aborted fetuses , infectious aerosols , and accidental vaccine inoculations [5–7 , 9–13] . In humans , brucellosis manifests as a non-specific , flu-like illness characterized by undulant fever , headache , myalgia , arthralgia , lymphadenopathy , hepatomegaly and splenomegaly , among others . The risk of adverse pregnancy outcomes has also been reported in pregnant women infected with Brucella species [1 , 14–18] . Although brucellosis causes minimal mortality , the severe debilitating morbidity associated with the disease is of negative socioeconomic impact due to the time lost by patients and care-givers from normal daily productive activities , and the detrimental effects of antibiotic resistance resulting from prolonged use of antibiotics for treatment of the disease [3 , 19–22] . Infection of bones and joints remains one of the most commonly described complications of brucellosis in humans [13 , 21 , 23–26] , and is predominantly reported in all ages and sexes in high-risk regions , such as the Middle East , Asia , South and Central America , and Africa [27–38] . Frequently , B . melitensis is isolated in cases of osteoarticular brucellosis ( OAB ) in high-risk regions . However , in low-risk regions , such as the United States , B . abortus is the most commonly encountered Brucella species , followed by B . suis [13 , 32 , 39–43] . Osteoarticular brucellosis ( OAB ) can be acute , subacute , or chronic . It is often diagnosed because of complaints of pain in joints or an evidence of infection at one or more locations of the musculoskeletal system [29 , 44 , 45] . These symptoms can present as inflammation ( such as swelling , pain , functional disability , heat , tenderness , and redness ) of bone and/or joints , or radiological evidence of bone anomalies [24 , 29 , 44–46] . Osteoarticular involvement can occur at any time during brucellosis infection and the main sites of the musculoskeletal system that are affected include the joints , spine , extraspinal tissues , tendon sheaths , as well as muscles [13 , 45 , 47–49] . Generally , OAB presents as sacroiliitis , peripheral arthritis , spondylitis , and osteomyelitis . Sacroiliitis is the inflammation of one or both sacroiliac joints . The onset of sacroiliitis may be preceded by non-specific flu-like symptoms such as fever , chills , sweats , and malaise [50] , and is associated with severe pain in affected individuals [13 , 29 , 32 , 51] . The associated severe and acute pain has led to several misdiagnoses of this condition as leg monoplegia , fracture of the neck of femur , and prolapsed intervertebral discs [13 , 29 , 32 , 52] . The incidence of sacroiliitis varies widely ( about 2% to 45% ) depending on Brucella endemicity of the reporting region ( 14 ) . Peripheral arthritis is one of the most common complications associated with brucellosis [13 , 23 , 32 , 45 , 48] , and may affect patients of any age [24 , 29 , 46 , 53] . Arthritis may present as monoarticular , oligoarticular , or polyarticular distribution accompanied by pain and swelling of the affected region , especially in acute conditions [28 , 29 , 46 , 52 , 54 , 55] . The incidence of Brucella-induced arthritis is about 3% to 77% ( 13 , 31 , 38 ) . Large joints such as the knees and hip are the most frequently involved peripheral joints , and less commonly , ankles , shoulders , elbows , wrists , and sternoclavicular joints are affected as well [23 , 32 , 45 , 46 , 48 , 56–59] . Clinical presentations of Brucella-induced arthritis are not specific , and should be differentiated from other types of arthritis by clinical history and a positive blood or synovial fluid culture of Brucella in infected individuals . Brucella-induced spondylitis is an inflammation of the spine and large joints that causes more serious complications than arthritis [54 , 60–63] , and it typically begins at the disco-vertebral junction , but may spread to the whole vertebrae and to adjacent vertebral bodies [13] . The most commonly affected region is the lumbar spine , especially at the L4 and L5 levels . Other sites affected are the thoracic and cervical spine [26 , 54 , 62 , 63] . The diffuse form of spondylitis covers the entire vertebral body , and may extend to the adjacent disc , vertebrae and epidural space [51 , 64] . Destructive brucellar lesions of the spine are commonly reported in adults and can occur in any spinal region at single or multiple levels [13 , 30 , 32 , 65–68] . Apart from serology and culture , clinical history is valuable in the diagnosis of spondylitis since the presenting features are similar to other causes of spinal disease such as tuberculosis ( 13 ) . Brucella-induced osteomyelitis is an infection of bone resulting in its inflammatory destruction and necrosis . It presents as motor weakness or paralysis and has been associated with a high rate of therapeutic failure and functional sequelae [69] . Several clinical reports suggest that individuals with Brucella infection commonly present with osteoarticular complication . Moreover , the prevalence of OAB is variably reported ( 2%-77% ) , depending on the virulence of Brucella species involved , age group and sex of the individuals affected , diagnostic methods , and endemicity of the reporting region [21 , 36 , 45 , 48 , 59 , 60 , 67 , 70 , 71] . Until this study , no attempt has been made to integrate all published studies and reports to derive a robust prevalence estimate of OAB . Therefore , the objective of this report was to systematically review the literature and perform a meta-analysis to estimate a well-grounded prevalence of OAB , which will help to establish disease awareness , facilitate early detection of the pathogen , facilitate development and validation of diagnostic tests , as well as demonstrate the need for vaccine development for prevention and control .
All studies reporting the incidence and clinical manifestations of osteoarticular brucellosis in humans , or where prevalence of the disease could be calculated from available data were included in this current study . Studies reporting infection of the bones and/or joints , demonstrated by two or more diagnostic methods ( bacteriological , molecular , serological , and/or radiographic ) were included . Studies involving co-infection with other pathogens , evaluating therapeutic or surgical responses in osteoarticular brucellosis patients , as well as animal experimentations were excluded . Furthermore , review articles , case-control studies , conference proceedings , and book chapters were excluded . Six databases were searched on March 6 , 2018: Medline ( Ovid ) , Global Health ( Ovid ) , Northern Light Life Sciences ( Ovid ) , CINAHL ( Ebsco ) , Agricola ( Ebsco ) , and Embase ( Ovid ) . The searches included 3 concepts: brucellosis , prevalence or epidemiologic studies , and bone and joint infections or common manifestations of osteoarticular brucellosis such as arthritis , osteomyelitis , spondylitis , and sacroiliitis . ( See S1 Text: Supplementary File for the details of the Medline ( Ovid ) search ) . The search was restricted to English Language reports and not restricted by year . In addition , references from the brucellosis entry from the Global Infectious Disease and Epidemiology Network ( GIDEON ) were collected . Cited and citing references of included and related reviews were retrieved using Scopus . Citations were uploaded to Rayyan , an application designed for sorting citations [72] . Titles and abstract were screened . Those that seemed relevant were added to RefWorks and the full-text were reviewed . Equivalent information was extracted from all included studies . This information comprised of the geographical region , sample size infected with brucellosis as well as those with osteoarticular involvement , age , sex , type of joints affected , and diagnostic methods ( such as inflammatory signs , bacteriological culture , immunoassays , and radiographic imaging techniques ) . Prevalence and 95% confidence interval were calculated or extracted from the reported data . The prevalence estimates for osteoarticular brucellosis in this review were based on the total number of individuals with confirmed brucellosis ( denominator ) and a proportion of these individuals with one or more osteoarticular disease manifestations . The meta-analytic integration of the individual study prevalence estimate was carried out using Stata15 and its “metaprop” and “galbr” commands . The “metaprop” command was developed specifically for meta-analysis of proportions and is based on the Freeman-Tukey double arcsine transformation for stabilizing variances . The “galbr” command produces a graphical display of the amount of heterogeneity among studies included in a meta-analysis . The “metaprop” command uses the numerator and denominator and carries out the Freeman-Tukey double arcsine transformation and then applied as fixed and/or random effects models using inverse variance weighting . The numerator and denominator data were used to estimate prevalence and these data were transformed into the Freeman-Tukey double arcsine equivalent with standard errors using Excel , and the data were then used to generate the galbraith plots .
A total of 974 publications were identified , which led to 515 articles being analyzed for full-text review . After full-text review , 56 published studies met the inclusion criteria and were used in the meta-analysis . Fig 1 details the process of article screening and selection following the Preferred Reporting Items for Systematic Review and Meta-Analyses ( PRISMA ) statement guidelines [73] . All articles included in this study were either prospective ( 32% ) or retrospective ( 64% ) , and the authors reported acute or chronic cases of human brucellosis and associated complications . Most of the included studies were from the Middle East , especially Turkey ( 37 . 5% ) , Iran ( 16% ) , Saudi Arabia ( 9% ) , Israel ( 3 . 5% ) , Kuwait ( 3 . 5% ) , Jordan ( 1 . 8% ) , and Iraq ( 1 . 8% ) . In Europe , studies were also reported from Spain ( 9% ) , Macedonia ( 7% ) , Germany ( 1 . 8% ) , Portugal ( 1 . 8% ) , and Kosovo ( 1 . 8% ) . Only one report was from South America , specifically Peru ( 1 . 8% ) . The most represented countries were Turkey , Iran , Spain , and Saudi Arabia , respectively , and B . melitensis was the predominant species isolated from either blood or bone marrow cultures of infected individuals . The age range of the study population was 0–88 years old . 25% of the included studies reported childhood OAB while 8% reported OAB in adults . Most studies reported varying proportion of osteoarticular brucellosis in both males and females . Table 1 details the characteristics of all the studies included in this review . For all individuals in the included studies , brucellosis was diagnosed based on the presence of inflammatory signs ( pain , swelling , and tenderness ) of the affected joints and one or more of other diagnostic methods including positive blood or synovial fluid culture; serology ( using 2-mercapthoethanol-Standard Agglutination Test ( 1/160 ) , Brucella Tube Agglutination Test ( 1:1280 ) , Brucella Skin Test , Complement Fixation test , Rose Bengal test , Coomb’s test ( 1/320 ) , Wright agglutination test , Immunofluorescence , or ELISA IgG and IgM ) ; and anomalies of the bones and joints evident by varying imaging techniques . Participants in most of the included studies were diagnosed based on clinical signs and serology ( 70% ) , and only a few reported additional positive blood or synovial fluid culture ( 30% ) ( Table 2 ) . The prevalence estimates ranged from 27% in low-risk regions ( e . g . Europe and North America ) to 36% in high-risk regions ( e . g . Middle East and South America ) , with no significant difference between the two estimates , indicating that the prevalence of OAB is independent of the endemicity of a particular region . High-risk regions included those countries where high number of cases or incidence of brucellosis have been consistently reported , such as in the Middle East , Asia , South and Central America , and Africa [27–29 , 32–36] . Fig 2 reflects the “metaprop” results of 56 prevalence estimates ( converted back from the Freeman-Tukey transformations ) . The overall fixed effect estimate of prevalence was 0 . 29 ( 95%CI: 0 . 28 to 0 . 30 ) . The fixed effect estimate was statistically heterogeneous with an I2 of 98 . 66% . The random effects estimate was 0 . 34 ( 95%CI: 0 . 28 to 0 . 39 ) . Fig 3 reflects the 56 prevalence estimates stratified by risk regions , which was determined based on previous reports of a high brucellosis incidence [27–29 , 32–36] . Both subgroups ( high-risk and low-risk regions ) , as well as the overall result ( Fig 3 ) were statistically heterogeneous . Stratification by risk regions alone was insufficient to explain the degree of heterogeneity . Random effects estimate for the two strata as prevalences were 0 . 36 ( 0 . 31 to 0 . 40 ) for high-risk countries and 0 . 27 ( 0 . 15 to 0 . 41 ) for low-risk countries . The estimates for the two strata were not statistically significantly different , suggesting that the prevalence of OAB is independent of the exposure risk of a particular region . Sources of heterogeneity between studies can also be explored using meta-regression . To determine the source of heterogeneity in these studies , as well as influence of age and gender on the prevalence of OAB , we collected data on age and gender . However , these data were problematic , and thus , meta-regression could not be used . The age of individuals was typically reported as a range , for example , 16 to 75 years of age . Mean and/or median age of the population would have been more desirable for a meta-regression analysis . Additionally , some studies did not report any age data . Therefore , these studies were dropped out of the meta-regression analysis . Studies that did not report gender proportions were also excluded , and thus , meta-regression could not be used to further explore heterogeneity based on both age and gender . Outlier analysis has also been used to explain , and by removal of studies one at a time , explain heterogeneity . The degree of heterogeneity was such that outlier study removal would have left too few studies to calculate reasonable estimates . Galbraith plot is provided ( Fig 4 ) using the Stata command “galbr” and the Excel computation of the Freeman-Tukey double arcsine transformation of prevalence values . Outlier studies are recognized as those outside the 2 parallel galbraith bands at values 2 and -2 .
The main objective of this study was to systematically review the literature and perform a meta-analysis to estimate the global prevalence of osteoarticular brucellosis ( OAB ) . A total of 56 studies met the inclusion criteria and were included in the systematic review and meta-analysis . Although there was an evidence of geographical variation in the prevalence of OAB with estimates ranging from 27% in low-risk regions to 36% in high-risk regions , the difference was not significant . This result indicates that the prevalence of OAB is not dependent on the endemicity of brucellosis in a region , and that brucellosis patients have at least a 27% chance of developing osteoarticular disease . In addition , the result also suggests that brucellosis remains an important public health concern in both high-risk [74 , 75] , and low-risk regions [42 , 43 , 76] . Therefore , early pathogen detection using sensitive and specific validated diagnostic techniques as well as treatment of the disease to stop disease manifestation are paramount in the control of OAB . Classification of a region into high or low-risk was determined based on previous reports of consistently high incidence of brucellosis in different countries in Africa , Asia , Eastern Europe , Mediterranean Basin , Middle East , South and Central America , and The Caribbean , as signified by the Centers for Disease Control and Prevention [27–29 , 32–36] . Low-risk regions include those countries with little or no reports of incidence of brucellosis such as North and South America , and some parts of Europe . In low-risk regions , very few occasional cases of brucellosis occur , and are usually travel-related . For example , consumption of raw animal products ( e . g . raw milk or cheese ) while visiting high-risk countries [77] . Furthermore , OAB may be difficult to diagnose in low-risk regions , not due to a lack of appropriate infrastructure , but because the disease is less common and may be confused with other causes of arthritis in humans ( e . g . Lyme disease ) , hence , an under-diagnosis or misdiagnosis of the disease . In this current study , although not statistically significant , the higher prevalence of OAB in high-risk regions correlates with previous findings reporting a high incidence of brucellosis in Middle Eastern countries such as Iran and Saudi Arabia [75 , 78 , 79] . Moreover , prevalence variation in different parts of the world may be due to varying environmental and socioeconomic factors such as sanitation , availability of medical facilities for optimum treatment and care , brucellosis awareness in communities , diagnostic capabilities for prompt detection of the disease condition , amongst others [80–83] . Another important factor is under-diagnosis or misdiagnosis of brucellosis because the disease manifests as flu-like symptoms and may bear resemblance to other diseases with similar symptoms such as malaria , or dengue fever , which are common disease conditions in many parts of Africa , thus leading to delays in detection and appropriate treatment of the disease [81 , 82] . Also , Brucella-induced arthritis in older individuals is commonly overlooked as arthritis due to old age . Hence , an understanding of patients’ history and thorough clinical examinations are recommended . Additionally , higher prevalence in high-risk regions may be due to close interactions with domestic animals such as raising animals in close proximity to human living areas , low public awareness of brucellosis as a serious debilitating disease , resistance to slaughtering infected animals , and the customary beliefs of raw milk ingestion [79 , 84] . Focal complications of brucellosis , such as osteoarticular involvement are frequently reported in children , especially in high-risk regions . For example , childhood OAB ( age ≤ 18years ) was reported in 25% of the studies included in this current meta-analysis study [85–91] , while 8% of the studies reported OAB in adults only . Clinical presentation of childhood brucellosis is similar to those observed in other flu-like illness such as malaria , influenza , or dengue and is often misdiagnosed and mistreated , especially in resource-limited settings [13 , 81 , 82 , 92–94] . In most reported cases , contact with contaminated animal products or consumption of raw unpasteurized milk has been shown as a risk factor for contracting the disease . For example , in some resource-limited settings , the available milk is rather given to children than adults , and if the milk is contaminated , it poses an increased risk of brucellosis infection . Most commonly , B . melitensis is the main causative agent in infected children , although other species such as B . abortus and B . suis have also been identified [13 , 36 , 38 , 91] . Most studies in this review reported age range for individuals presenting OAB , for example , an age range of 16–75 years , while some studies did not report any age data . Therefore , it was impossible to determine the actual variation in prevalence based on age of the study population . Thus meta-regression could not be used to further explore heterogeneity . As regards gender , both sexes are affected equally , although some reports claim that the disease is more prevalent in males ( 80% ) than in females ( 19% ) because of the nature of the male job in such regions , which facilitates increased exposure to animals and their products , as observed in herdsmen , ranchers , pastoralists and abattoir workers [29 , 38 , 51] . In this current study , because of the limited information provided in the selected articles , it was impossible to determine variation in OAB prevalence based on gender of the study population . There are several diagnostic tools for brucellosis . The gold standard of brucellosis diagnosis is the positive culture of Brucella from tissues and bodily fluids ( e . g . blood , bone marrow , synovial , and cerebrospinal fluid ) of infected patients , although culture yield is inversely related to the duration of illness [95–97] . For example , culture yield is greater during the acute stage of brucellosis while it is less in later stages of the disease or during occasional relapses [84] . Additionally , the likelihood of isolation in patients with chronic disease and focal complications can be improved by using sampling material from affected sites , such as synovial fluid in OAB cases [97] . Various Brucella culture systems including automated continuously monitored blood culture systems such as Bactec ( BD Diagnostics , Sparks , MD , USA ) and BacTAlert ( bioMerieux , Durham , NC , USA ) give higher yields than the conventional culture method and facilitate the detection of bacterial growth [84 , 98] . However , these culture systems are not routinely used in most high-risk regions because of insufficient infrastructure as well as trained personnel . Hence , classical bacteriological culture is a common diagnostic method for brucellosis in these regions because it is easily accessible [95–97] . Due to inconsistent yield of Brucella from culture systems , increased risk of personnel infection , as well as the lack of validated molecular-based diagnostic techniques , the common standard for diagnosis of brucellosis is serological assays , which include Serum Agglutination Test ( SAT ) , Microagglutination Test ( MAT ) , Enzyme Linked Immunosorbent Assay ( ELISA ) , Indirect Coombs ( Anti-Human Globulin ) Test , Brucellacapt , Wright agglutination test , Rose Bengal Slide Agglutination Test ( RB-SAT ) , Complement Fixation Test ( CFT ) , Indirect immunofluorescence test ( IF ) , and Immunochromatography Lateral Flow Assay . Among the serological assays , SAT is the most frequently used and standardized test . SAT is based on measuring an agglutination titer of different serum dilutions ( 1:20–1:1280 ) against a standardized concentration of whole Brucella cell suspension . The highest serum dilution showing more than 50% agglutination is considered the agglutination titre . A positive titre is 1:160 or more [84 , 98 , 99] . Multiple testing at 4–8 week intervals is recommended to overcome the drawback of inconsistent results . ELISA is another commonly used serological assay for diagnosing brucellosis . It is considered specific ( 95% ) and sensitive ( 98% ) and has been consistently shown to diagnose both focal and chronic brucellosis [98 , 100] . Generally , because of the variability in the specificity and sensitivity of the conventional serological tests routinely used in high-risk regions , a combination of varying serological tests ( e . g . SAT and either indirect Coombs , Brucellacapt , or ELISA for IgG and IgM ) is recommended for the definitive diagnosis of human brucellosis [97 , 98 , 100–102] . Correspondingly , all studies included in this systematic review and meta-analysis used a combination of serological tests including SAT ( 1/160 ) , ELISA ( IgG and IM ) , CFT , IF , Wright agglutination test , Brucella Tube Agglutination Test ( 1:1280 ) , Brucella Skin Test , Coomb test ( 1/320 ) , or RB-SAT ( Table 2 shows the diagnostic tests used by the respective studies ) . All individuals presenting OAB in the articles selected for the current analyses were positive for two or more of the reported diagnostic methods ( such as clinical signs of fever , inflamed joints , myalgia , arthralgia , and/or bacteriological culture , and serology ) Table 2 . In addition to clinical and serological diagnosis of OAB , radiographic abnormalities of the bones and joints , which manifest as arthritis , sacroiliitis , and spondylitis , have been described using varying radiological techniques such as radionuclide bone scan , plain radiography , joint sonography , computed tomography , and contrast-enhanced magnetic resonance imaging , amongst others . The abnormalities in the affected osteoarticular regions included joint space narrowing or widening , subchondral erosion , subchondral sclerosis and/or soft tissue swelling [37 , 46 , 63 , 103] . For example , bone scans were considered positive for abnormalities when there was increased uptake of the compound in the respective osteoarticular regions [46] . Generally , radiological diagnosis of OAB in humans is nonspecific and inconsistent , but varying degrees of abnormalities of affected regions have been described [46 , 63] . In this current study , most of the individuals had a report of varying bone abnormalities evident by the respective imaging techniques . Overall , the prospects of OAB diagnosis by a physician in high-risk versus low-risk regions differ . Brucella-induced osteoarticular involvement can be easily suspected in high-risk regions based on clinical signs and history of contact with animals and raw animal products , thereby facilitating rapid diagnosis and treatment . However , in low-risk regions , especially where brucellosis has been eradicated , a knowledge of patients’ clinical history ( e . g . a travel-related exposure to and consumption of raw animal products such as milk and cheese ) is particularly valuable to the diagnosis of OAB ( 13 ) . Since the clinical features of OAB are not specific and there is yet to be a single consistent definitive diagnostic technique , the clinical history of animal contact or consumption of raw animal products is especially important , as well as a combination of diagnostic methods ( bacteriological culture , serology and imaging ) . The purpose of the current study was to estimate the prevalence of OAB among brucellosis patients worldwide by performing a meta-analysis . For the first time , we have demonstrated that the prevalence of OAB is independent of brucellosis endemicity of a particular region , and that brucellosis patients have at least a 27% chance of developing an osteoarticular disease . Thus , brucellosis remains a public health concern in both high-risk and low-risk countries [104] , although there are some limitations to this current study , such as incomplete data representation . For example , lack of vital demographics precluded the feasibility of estimating OAB prevalence based on age and gender . Nevertheless , the current review is still very valuable , and has contributed to our understanding of the global prevalence of Brucella-induced osteoarticular disease . Hence , this study has provided the basis for increased awareness of OAB , the need for the development and validation of diagnostic tests , and appropriate treatment regimen to reduce disease manifestation . Therefore , further research should investigate the potential mechanisms of OAB , as well as the influence of age , gender , and other socioeconomic factor variations in its global prevalence , as this may provide insight into associated exposure risks and management of the disease .
|
Brucellosis continues to be a global public health concern . It is caused by facultative , intracellular Brucella species . The most commonly described complication of brucellosis in humans is the infection of bones and joints , which is predominantly reported in all ages and sexes in high-risk regions , such as the Middle East , Asia , South and Central America , and Africa . In this current study , we systematically reviewed the literature and performed a meta-analysis to estimate the global prevalence of osteoarticular brucellosis . We demonstrated an evidence of geographical variation in the prevalence of osteoarticular brucellosis with estimates ranging from 27% in low-risk regions to 36% in high-risk regions . However , the difference was not significant . Therefore , the prevalence of osteoarticular brucellosis is not dependent on the endemicity of brucellosis in a particular region , and brucellosis patients have at least a 27% chance of developing osteoarticular disease .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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"pathogens",
"tropical",
"diseases",
"microbiology",
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2019
|
A systematic review and meta-analysis of the prevalence of osteoarticular brucellosis
|
Infection with the protozoan Trypanosoma cruzi manifests in mammals as Chagas heart disease . The treatment available for chagasic cardiomyopathy is unsatisfactory . To study the disease pathology and its inhibition , we employed a syngeneic chicken model refractory to T . cruzi in which chickens hatched from T . cruzi inoculated eggs retained parasite kDNA ( 1 . 4 kb ) minicircles . Southern blotting with EcoRI genomic DNA digests revealed main 18 and 20 kb bands by hybridization with a radiolabeled minicircle sequence . Breeding these chickens generated kDNA-mutated F1 , F2 , and F3 progeny . A targeted-primer TAIL-PCR ( tpTAIL-PCR ) technique was employed to detect the kDNA integrations . Histocompatible reporter heart grafts were used to detect ongoing inflammatory cardiomyopathy in kDNA-mutated chickens . Fluorochromes were used to label bone marrow CD3+ , CD28+ , and CD45+ precursors of the thymus-dependent CD8α+ and CD8β+ effector cells that expressed TCRγδ , vβ1 and vβ2 receptors , which infiltrated the adult hearts and the reporter heart grafts . Genome modifications in kDNA-mutated chickens can be associated with disruption of immune tolerance to compatible heart grafts and with rejection of the adult host's heart and reporter graft , as well as tissue destruction by effector lymphocytes . Autoimmune heart rejection was largely observed in chickens with kDNA mutations in retrotransposons and in coding genes with roles in cell structure , metabolism , growth , and differentiation . Moreover , killing the sick kDNA-mutated bone marrow cells with cytostatic and anti-folate drugs and transplanting healthy marrow cells inhibited heart rejection . We report here for the first time that healthy bone marrow cells inhibited heart pathology in kDNA+ chickens and thus prevented the genetically driven clinical manifestations of the disease .
Epidemiological methods have been used to evaluate the incidence and distribution of endemic Chagas disease , to associate epiphenomenal clinical manifestations with the recognized pathology of the disease . Acute T . cruzi infections often resolve spontaneously , but the individual remains chronically infected for his or her lifetime , even in the absence of clinical manifestations [1] . Approximately 18 million people on five continents harbor often-asymptomatic chronic T . cruzi infection [1] , [2] . In humans , approximately 30% of the individuals infected with T . cruzi develop chronic Chagas heart disease ( 94 . 5% ) and/or megacolon and megaesophagus ( 5 . 5% ) [3] . Additionally , Chagas disease can present with complex clinical manifestations , such as myositis and weakness , and peripheral nervous system involvement , which translates into a neuroendocrine syndrome [4]–[6] . The relationship between cryptic T . cruzi infection and late manifestations of Chagas heart disease , three or more decades after parasite acquisition , has been debated [1] . Two main theories have been proposed for the pathogenesis of Chagas disease . The parasite persistence theory suggests that the mechanical disruption of the parasitized cells by the parasite weakens the heart and initiates heart failure [3 , 7 , and 8] . The autoimmune theory suggests that the pathogenesis of Chagas disease , which is usually seen decades after acute infection , when the parasite is not in close proximity , might be associated with the rejection of target cells by competent effector lymphocytes [9] , [10] . The foundation of immunology is based on the premise that the immune system evolved primarily to protect against pathogens , foreign cells , and inorganic and organic toxic substances . The body maintains homeostasis through immunologic surveillance [11] . In this regard , immunologic surveillance is permissive to the continuous elimination of aging cells in the body , with minimal rejection under physiological conditions [12]–[14] . However , the phenomenon of autoimmunity occurs when the immune system recognizes components of a healthy individual's body [15] due to a breakdown in immune tolerance to the body's own constituents [16] . The disruption of immune tolerance and the resultant exacerbation of anti-self immune reactions and noxious rejection of target tissue define autoimmune disease [17] . More than 100 autoimmune diseases can be accurately diagnosed on the basis of symptoms that are validated by pathology results revealing multifaceted , unresolved inflammation [18] , [19] . However , the etiology of almost all autoimmune diseases is unknown , thus precluding effective treatment to block the as-yet-undisclosed factors that can trigger hypothetical antigenic mimicry , whereby the immune system destroys its own tissue [20] , [21] . Although the pathology of autoimmune diseases has long been thought , the exact event that triggers the onset of inflammation is controversial . The relationship between infectious agents and autoimmunity has been considered a first signal for the induction of autoimmunity by molecular mimicry , yet its origin has not been recognized . The triggers of auto immune diseases under complex circumstances associate genetic susceptibility and exposure to uncertain environmental factors capable of activating the first signal-specific immune system reaction pathways [21] , [22] . Additionally , a second signal non-specific reaction includes several factors , such as adjuvant effect , bystander activation of auto reactive T cells; and modified immunogenic self-antigens can lead to autoimmunity against self-antigens , resulting from random mutations and protein synthesis errors , which can be recognized as foreign by B and T lymphocytes [23] , [24] . Accordingly , the self-antigen might interfere with peripheral immune tolerance during an infection that calls out bystander activation of T and B cells with specificity for mutated self-antigen , which leads to epitope spreading , production of immune competent cells reactive against self-antigens or against putative super antigens , and enhanced presentation of self-antigens , cytokines release post-immune cell activation , and necrobiosis following apoptosis , and general inflammation [24] . Recent studies have focused on the relationships between autoimmune diseases and environmental factors , searching for endogenous intracellular factors in tissue and exogenous triggers on the cell surface [25] . Identifying candidate environmental factors has been difficult due to an unpredictably long period of time between the onset of infection and the appearance of recognizable clinical autoimmune disease . Furthermore , latency-prone microbes can remain within their hosts for decades without triggering autoimmune disease in the majority of patients . The identification of internal and external autoimmune factors from conception to death requires exposome and infectome approaches , focusing on identifying candidate infectious agents from environmental exposure that can trigger autoimmune diseases [26] . These approaches have implicated 16 bacterial species , three viruses , two protozoa , one helminth , and one fungus as inducers of autoimmune diseases [27]–[29] . Although an autoimmune disease can begin before the onset of overt symptoms , it is believed that subclinical manifestations , which might have occurred for an undefined period of time , could improve our understanding of late-phase ailments , as well as frequent remissions and relapses . A familial predisposition to autoimmune diseases has been recognized , and studies in homozygotic twins have suggested the involvement of certain genes in the pathogenesis of this category of human diseases [30] , [31] . The role of antibodies for self-tissue antigens has been proposed to explain the onset of mechanisms whereby infectious agents may induce sensible alterations in the immune responses , disruption of immune tolerance , and clinical manifestations of autoimmune disease . Although antibody production increases with aging , its pathological effect translating clinical manifestation was difficult to determine [32]–[34] . In a non-human primate model the development of autoantibody response in healthy baboons was shown , gradually increasing concentrations of antinuclear antibodies , whole-cell-antibodies , and natural autoantibodies from youth to old age [34] . In the primate model , the immunoglobulin levels and cytokines that promote immune deregulation remained unchanged , and thus the autoantibodies were produced in absence of clinical and pathological manifestations [33] . A body of evidence has been produced that favors a role of cardiac myosin autoantibodies in patients with dilated cardiomyopathy ( DCM ) , as IgG3 reactivity correlated with myocardial dysfunction [35] . Also , it has been proposed that myocarditis often produced by viral infection may develop into autoimmune inflammatory cardiomyopathy and heart failure [36] . The significant role of myosin IgG autoantibodies , which reacts with the beta-adrenergic receptor and triggers cAMP-dependent protein kinase A signaling in heart cells , was suggested [36] . The cross-reactive autoantibodies against human cardiac myosin and the beta-adrenergic receptor in the heart may play a mechanistic role in the pathogenesis of DCM [36] . Additionally , subclass specific autoantibodies against myosin has been considered pro-inflammatory moieties , and it has been postulated that pro-inflammatory IgG3 antibodies may play a role in the autoimmune mechanisms of injury in DCM patients [37] . Viral myocarditis and valvulitis are often associated with autoimmunity because anti-heart antibodies have been identified in the sera of patients with this ailment , as well as in low titers in healthy individuals [38] . Furthermore , the induction of experimental autoimmune myocarditis ( EAM ) and of CVB3 myocarditis in several mouse strains and in the Lewis rat requires emulsification of cardiac myosin and CVB3 , separately , with adjuvant for elicitation of a second signal pro-inflammatory response , but the clinical implications of such anti-self antibodies and its prognostic significance has not been fully understood [39] . Chagas disease has been considered a clinical condition potentially fatal due to an inflammatory autoimmune cardiomyopathy [40] . In this regard , the molecular mimicry between the cardiac myosin heavy chain and T . cruzi protein B13 has suggested that the Chagas heart lesions may be triggered by parasitic antigen-specific effectors T cells [41] . Interestingly , a putative role played by autoantibodies in human Chagas heart disease was controversial [42] , because autoantibodies produced against troponin and myosin were not essential for cardiac inflammation [43]–[47] . It was further shown that cardiac damage induced by immunization with heat-killed T . cruzi was not antibody mediated [48] . Attempts to produce the autoimmune lesions of Chagas heart disease by immunization with T . cruzi antigens B13 , Cha , cruzipain , and 45-kDa calreticulin required adjuvant in order to produce inflammatory infiltrates in the heart [49]–[54] . Some animal model experiments have been proposed to complement hypothetical autoimmunity mechanisms [20] . To substantiate these mechanisms , experimental animal models were described for Hashimoto's thyroiditis [55] , experimental acute encephalitis ( EAC ) [56] , and CVB3 myocarditis [57] , whereby lesions were produced , respectively , by injections of thyroglobulin , myelin basic proteins ( MBP ) , or CVB3 antigens , each mixed with adjuvant [56]–[57] . The presence of antibodies against myosin has been recorded for several inflammatory myocardiopathies and for DCM . Furthermore , attempts to reproduce the autoimmune lesions of Chagas disease by injections of myosin , and of wild or recombinant T . cruzi antigens mixed with Freund's adjuvant revealed small lymphocyte infiltrates in the heart and other bodily tissues [58]–[61] . Interestingly , the attempts of passive transfer the autoimmune phenomenon by injections of lymphocytes from immunized animals to naive recipients produced localized inflammatory reactions in the absence of specific clinical symptoms and of gross lesions [1] , [58]–[64] . Yet , it has been proposed that the origin of autoimmune diseases can be associated with somatic mutations [17] . In the case of Chagas disease , it has been described that somatic mutations , resulting from the T . cruzi kDNA minicircle sequences that integrate into the genomes of humans , rabbits , and chickens can lead to severe autoimmune disease manifestations and gross pathology [10] , [65]–[66] . Moreover , in the absence of clinical manifestations of autoimmune disease associated with autoantibodies [42] , [45]–[47] , we hypothesize that Chagas disease can be an antigen-independent autoimmune phenomenon , whose first signal is provided by somatic mutations driven by the overreactivity of cytotoxic T lymphocytes , which infiltrates and reject target tissues in the parasite-free transkingdom chicken model system . The eukaryotic protozoa in the order Kinetoplastida and family Trypanosomatidae include the highly diversified T . cruzi [66]–[68] . Insect-borne primary infections result from the contamination of skin wounds and mucosal surfaces with T . cruzi forms . Upon entry into the host's histiocytes , the trypomastigotes congregate and transform into amastigotes , which are replicative forms that de-differentiate and reinitiate their life cycles in non-phagocyte host cells [1] , [67]–[71] . Electron microscopy analysis of T . cruzi forms has revealed two organelles containing DNA , namely nuclear DNA ( nDNA ) and the symbiotic mitochondrial kinetoplast DNA ( kDNA ) [71] . The incidences of nuclear and mitochondrial phylogenetic incongruences indicate that widespread genetic recombination continues to influence the structural diversity of the T . cruzi population [70] . The kDNA constitutes approximately 25% of the total cellular DNA and is organized into a network of maxicircles and minicircles . There are a few dozen maxicircles of 40 kb and approximately 20 , 000 T . cruzi minicircles averaging 1 . 4 kb , which translate guide RNA ( gRNA ) to edit the maxicircle genes [68]–[70] . The T . cruzi kDNA minicircle encloses four variable regions ( VRs ) , interspersed by conserved regions ( CRs ) ; each region encodes constant sequence blocks – CSB1 , CSB2 , and CSB3 – in which cytosine- and adenine-rich ( CA-rich ) motifs represent specific sites for the initiation of replication , transcription , recombination , and lateral DNA transfer [65]–[67] . The T . cruzi mitochondrial symbiont kinetoplast network organization , which signals the replication and/or latency of parasitic forms living free in the mammalian cytoplasm , favors host–parasite coevolution , horizontal gene transfer and genetic diversity [65]–[71] . Studies that were conducted in human chagasic families revealed instances of naturally occurring T . cruzi mitochondrial kDNA minicircles integrated mainly into retrotransposable long interspersed nuclear element ( LINE-1 ) loci on various chromosomes [10] , [65]–[67] . The kDNA integration events documented the contemporary transfer of eukaryotic DNA to the human genome and its subsequent inheritance by descendants , suggesting that the disease pathogenesis intermingles new concepts of evolutionary biology and medicine [10] , [65]–[67] . Vertebrate genomes are largely composed of highly repetitive viral sequences , and these molecules have been used to decipher the evolution of the genome architecture over millions of years [71]–[78] . Approximately 50% of the human genome consists of class 1 and 2 transposable elements ( TEs ) of viral origin [79]–[82] . The class 1 TEs include the autonomous LINEs , which are retrotransposons that encode endogenous machinery for accomplishing reverse transcription [77]–[81] and mobilization of the DNA copy to different distant sites in the genome [66 , 67 , 76 , 81 , and 83] . The human genome contains 400 , 000 truncated copies of retrotransposons , including 400 active autonomous LINEs , which are homologs of CR1 repeats in the chicken genome [84] , [85] . Coincidentally , a variety of repetitive elements act as open gates for the integration of exogenous DNA in TEs . These gates are natural tools for further mobilization , recombination , hitchhiking and shuffling [62 , 65 , 80 , and 81] . Interestingly , the mitochondrial minicircle kDNA sequences from T . cruzi can integrate into LINE-1 with CSB-mediated microhomology , and through the target site , reverse transcription can copy the kDNA minicircles that hitchhike to second- and third-party coding regions , which later appear recombined and shuffled at various chromosomes [62 , 65 , and 81] . The previous demonstration of the lateral kDNA transfer ( LkDT ) of minicircle sequences from T . cruzi to vertebrate animals [10] , [65]–[67] , [81]–[87] suggested the concept of genotype modifications as the main driving force for autoimmunity , and therefore , a possible relationship between insertional mutagenesis and the pathogenesis of human Chagas disease was considered [1] , [10] , [65]–[67] , [81]–[87] . Based on our previous demonstration of gross Chagas heart pathology in kDNA-mutated outbred chickens , a growing body of evidence has suggested a relationship between LkDT and Chagas disease [1] , [10] , [65]–[67] . This hypothesis was bolstered by findings from a chicken model system that is refractory to the infection and that eliminates any contamination with DNA from cryptic T . cruzi infections [10] , [65] . The infection of fertilized eggs prior to incubation promptly reached the embryonic stem cells , and the minicircle kDNA sequences were retained in the chicken genome , but the parasites were all eliminated at an early stage of immune system development . Therefore , later effects were caused by insertional mutagenesis [10] , [65]–[67] . A dearth of research has examined the innate and immune effector systems' roles in normal embryo development or during congenital infections [88]–[90] . The innate chicken non-immune effector system is neither developed nor functional in the embryo , and the cellular aspects of the immune response mature sequentially in the fetus [65–67 , and 88] . The lack of a functional immune system makes the embryo highly susceptible to invading microbes , which may lead to abortion , natal death , or perinatal death , but the chick's survival and development often occur in the presence of immune tolerance [1] , [10] , [65]–[67] , [88]–[90] . The parasite-free chicks that hatch from T . cruzi-inoculated eggs nonetheless transfer the kDNA minicircles retained within their genomes to their progeny via breeding . The kDNA-mutated chickens ( kDNA+ ) reject the heart , exhibiting lysis of the myocardium fibers by immune effector cells . Notably , the kDNA mutations are vertically transferred to progeny that subsequently develop Chagas-like heart disease [10] , [65]–[67] . This study was based on our hypothesis that the genetically driven autoimmune rejection of heart tissue in Chagas-like disease [10] , [65]–[67] can be prevented by replacing sick ( kDNA+ ) bone marrow cells ( BMCs ) with healthy cells from naive control ( NC ) chicken marrow cells . Accordingly , we used the Prague syngeneic lines CB ( B12/B12 ) and CC ( B4/B4 ) to avoid some graft rejections that can occur in partially inbred or even outbred chickens [91]–[95] . We then introduced genetic modifications into the genomes of the syngeneic chickens by the inoculation of T . cruzi into fertilized eggs . The chickens that hatched from the T . cruzi-infected eggs retained the parasite mitochondrial kDNA in their genomes and rejected the parasite-free hearts . Parental breeding generated kDNA+ descendants , and heart disease was documented in these progeny . These results confirm and extend our findings in the outbred chicken model system [10] , [65]–[67] . Moreover , this study focused on the hallmark manifestation of Chagas heart disease and revealed that healthy bone marrow transplantation inhibited heart pathology in the kDNA+ chickens and thus prevented the pathology-driven clinical manifestations of the disease .
The fertile eggs of the Prague congenic lines CB ( B12/B12 ) and CC ( B4/B4 ) were a donation from the Institute of Molecular Genetics , Academy of Sciences of the Czech Republic . Chicks that hatched and grew to adulthood were bred to generate flocks at the Laboratory Animal Facility of the University of Brasilia . The chicken house was maintained at an average temperature of 24°C , under positive-pressure filtered air and exhaust . To prevent contamination , only authorized personnel entered the chicken house , and these personnel wore boots , apron , gown , and mask when handling the flock . The chickens were fed commercial chow supplemented with essential amino acids and oyster shell as the calcium source . The CB and CC chickens were housed in cages located in separate aisles , and the eggs fertilized through artificial insemination were incubated at 37°C for 21 days to hatch chicks that reached sexual maturity at eight months of age . The protocols ( UnBdoc #35714/2008 ) used in these studies were approved by the Institutional Ethical Committee in Animal Research - CEUA , Institute of Biology , University of Brasilia , in accordance with the International Animal Welfare Act ( 7 U . S . C . 2131 et . seq . ) , applicable guidelines and policies , and the U . S . government's Principles for the Utilization and Care of Vertebrate Animals Used in Testing , Research and Training . The trypomastigote and epimastigote forms of the T . cruzi Berenice stock were grown as described elsewhere [10] , [65]–[67] . Fertile eggs laid by chickens with haplotypes CB ( B12/B12 ) and CC ( B4/B4 ) were inoculated with 100 T . cruzi trypomastigote forms in 10 µl of culture medium through a 1-mm-diameter aperture made in the shell at the air chamber [66] . An equal number of mock control eggs ( mock ) were inoculated with 10 µl of culture medium . The holes in the shell were sealed with adhesive tape , and the eggs were immediately transferred to incubation chambers maintained at 37°C with 65% humidity , with gentle rolling for 1 min every 30 min . The embryo development was monitored periodically , and DNA from the mononuclear cells of chicks that were hatched from naive control ( NC ) chickens that had never been under exposure to T . cruzi and from infected chicken eggs was used to assess the parasite kDNA integration [10] , [64]–[67] . Ten months-old NC chickens were used in the immunization procedures . The T . cruzi epimastigotes grown in liver-infusion tryptose medium were harvested in the exponential growth phase . The cells collected by centrifugation at 1000 g×20 min were washed in PBS and suspended in 4% ( w/v ) paraformaldehyde ( Sigma-Aldrich ) . A total of 10×107 formalin-killed epimastigotes suspended in 1 ml of PBS , pH 7 . 4 , was injected subcutaneously in the tights of 10 pre-immune NC chickens . Three subcutaneous injections were administered one week apart and blood for immune serum was collected one week after the last injection . Also , sera collected from pre-immune and from immune NC chickens were stored in glycerol ( 1∶1 v/v ) at –20°C . DNA was extracted from peripheral blood mononuclear cells of kDNA+ chickens , NC chickens , and mock control chickens hatched from eggs inoculated with culture medium only . DNA was extracted also from somatic cells of the heart , kidney , skeletal muscle , large bowel , liver , and spleen from kDNA+ and from NC chickens . The T . cruzi epimastigotes' mitochondrial kDNA was extracted as described [10] , [66] . The samplings of genomic DNA were used as templates for PCR amplification with the specific T . cruzi nDNA primers Tcz1 and Tcz2 [96] and the kDNA primers s35 and s36 [97] . The amplification reaction was run with 200 ng template DNA under the following conditions: 0 . 2 µM of each primer , 2 . 5 U Taq DNA polymerase , 0 . 2 mM dNTP , and 2 mM MgCl2 in a 25 µL final volume . Triplicate amplification reactions were performed using the recommended temperatures for nDNA ( 95°C for 5 min; 30 cycles of 95°C for 30 s , 68°C for 1 min , and 72°C for 1 min; and final extension at 72°C for 5 min ) and kDNA primers ( 95°C for 5 min; 35 cycles of 95°C for 30s , 62°C for 1 min , and 72°C for 1 min; and final extension at 72°C for 5 min ) . The amplicons were resolved in 1 . 3% agarose gel , transferred to a positively charged nylon membrane ( GE Life Sciences ) by the alkaline method , and subsequently hybridized with specific [α-32P] dATP-labeled probes using a Random Primer Labeling Kit ( Invitrogen ) . Southern hybridizations were performed with EcoRI digests of DNA samples from NC and from kDNA+ chicken DNA . The positive control was the wild-type kDNA purified from T . cruzi . The EcoRI enzyme made a single cut in the kDNA minicircles to generate a linear fragment that was detected as a 1 . 4 kb band . The protocols for Southern hybridization are described elsewhere [10] , [66] . The tpTAIL-PCR was employed as described [10] , [67] . The chicken primers annealing to a specific locus were obtained by the alignment of Genbank chimera sequence FN599618 within the locus NW_001471673 . 1 on chromosome 3 in the Gallus gallus genome [10] . The primers used in these studies with their respective annealing temperatures are shown in S1 Table . The kDNA primers were used in separate combinations with 0 . 04 µM of the CC1 to CC6 primers set , and three cycles of amplifications were run , as described elsewhere [10] . Clones selected by hybridization with a radio labeled kDNA probe were sequenced commercially . The tpTAIL-PCR was validated in a mix of 300 pg of kDNA from T . cruzi with 200 ng of DNA from control birds never exposed to kDNA [10] . The chimera-specific genes were selected , and primers were obtained for the metal transporter CNNM2 ( chromosome 6 , NW_003763812 . 1 ) , the mitochondrial NADP-dependent malic enzyme ( NADPME ) ( chromosome 1 , locus NW_003763650 . 1 ) , and the dystrophin gene ( chromosome 1 , locus NW_001471534 . 2 ) chimera sequence ( FN598991 ) . The primer sets for dystrophin and NADPME were used in combination with the kDNA primers in a nested PCR , and the dilutions from the previous amplifications were maintained [10] . The hearts from kDNA+ and from naive control chickens ( NC ) were excised at the base of the large vessels and washed in three changes of cold PBS , pH 7 . 4 to eliminate the blood cells . The myocardium was cut into small ( 2 to 3 mm ) fragments , which were suspended in hypotonic lysis buffer ( 0 . 1 mM HEPES , pH 7 . 9 at 4°C , 1 , 5 mM MgCl2 and 10mM KCl ) with protease inhibitors ( 0 . 2 mM leupeptin , 0 . 5 mM TLCK and 0 . 5 mM DTT ) [61] . The fragments were teased with a blade , and the cell clumps were mechanically disrupted in a glass tube with a teflon pestle accelerated at 14 , 000 rpm on ice . After six disruption cycles of 30 seconds separated by 5 min intervals , the crushed material was centrifuged at 5 , 000 rpm for 15 min at 4°C . The pellet was discarded , and the supernatant was centrifuged at 14 , 000 rpm for 10 min . The T . cruzi soluble antigens were obtained from 10×107/ml epimastigotes forms [98] . The cells were centrifuged at 1000×g for 20 min , at 4°C , and the pellet was suspended in lysis buffer and centrifuged at 10 , 000×g for 10 min . The soluble proteins in the supernatant from the last centrifugation were obtained [98] . The heart and the T . cruzi soluble fractions were stored at −80°C until use . The T . cruzi epimastigotes ( 10×107/ml ) in 10 ml of medium were centrifuged at 1000×g for 20 min , and the pellet was washed in three changes of cold PBS . The pellet was suspended in lysis buffer , and the soluble proteins in the supernatant from the last centrifugation were obtained , as described [99] . The heart and the T . cruzi soluble fractions were stored at −80°C until use . We sensitized 96-well flat-bottom microplates with 1 µg of protein in 50 µL PBS/well [99] for each soluble fraction: i ) kDNA+ chicken heart; ii ) kDNA- chicken heart; and iii ) T . cruzi epimastigote antigen . The antigen in the well was incubated for 18 h at 4°C in a moist chamber . ELISA was performed as described [99] . Briefly , the alkaline phosphatase-conjugate ( Sigma ) anti-chicken immunoglobulin were used at 1∶1000 dilution in PBS , pH 7 . 4 containing 2% nonfat milk . The color reaction was developed by the substrate p-nitrophenyl phosphate ( pNPP , 50 µL/well ) dissolved in diethanolamine buffer , pH 7 . 9 ( Sigma ) . Test and control serum assays were run in triplicate , and the mean ODs ± standard deviation were recorded . A serum dilution yielding absorbance 0 . 150 or above was considered to be a positive reaction . This cut-off determined in standard serum samples was used to separate positive from negative controls . The ELISA test for the human T . cruzi antibody was conducted as described [99] . Heart tissue was cut into 1 mm pieces , washed with PBS containing a protease inhibitor cocktail ( 5 mM EDTA , 100 µM PMSF , 100 µM TLCK , 100 µM TPCK , 1 µM pepstatin A , 100 µM leupeptin ) at 4°C , and centrifuged three times at 10 , 000 rpm with supernatant discarding . Lysis buffer ( 7 M urea , 2 M thiourea , 2% Triton X-100 , 1% DTT and the protease inhibitor cocktail ) was then added in the proportion of 6 mL to 1 g of tissue , followed by vortexing for 2 min and sonication in a refrigerated bath for 2 min three times . The material was left at 4°C for 20 min and finally centrifuged at 14 , 000 rpm for 15 min . The supernatant ( heart extract ) was submitted to protein quantitation using the PlusOne 2D Quant Kit ( GE Life Sciences ) . The protein profiles of heart extracts ( 100 µg ) were analyzed by 2DE [100] . Peptide mass fingerprinting [63] was used for protein identification via an Autoflex II mass spectrometer ( Bruker Daltonics ) and the Mascot software ( Matrix Science ) . The NCs and the kDNA+ B12/B12 chickens were used . Twenty milliliters of venous blood collected in 20 units of heparin was layered on top of Ficoll-Hypaque ( GE Healthcare ) and centrifuged at 3500×g for 30 min at room temperature . The plasma in the supernatant was removed and centrifuged at 4000×g for 10 min to obtain the platelet-poor plasma fraction . The white blood cells on the interface were collected and washed thrice in PBS , pH 7 . 4 , by centrifugation at 1000×g for 10 min . A total of 10×106 mononuclear cells were stained with 1×10−6 dilution of the fluorochrome ( 10 µL PKH +10 mL Sigma diluents C ) . After incubation for 10 min at room temperature , the cells were washed five times in PBS . The pellet of the last centrifugation was suspended in the platelet-poor plasma fraction . For in vivo labeling of BMCs from kDNA+ and NC chickens , a series of injections of 1×10−6 M of fluorochrome PKH26 red dye ( 1st and 3rd doses ) and PKH67 green dye ( 2nd and 4th doses ) were administered two weeks apart in the wing vein of 3-month-old chickens . Sections of bone marrow , spleen , and heart of adult birds were processed for histopathology , and the distribution of the labeled immune cells in the tissue was recorded . The standardization procedure showed the staining of BMC and of lymphoid spleen cells with the fluorochromes PKH26 red and PKH67green . The bone , muscle , and connective tissues revealed UV fluorescence , often seen in unstained sections . The double labeling allowed confirmation of specific immune cells staining , and merge image collection ( Fig . 1 ) . Skin grafts [16] were performed between birds with CB ( B12/B12 ) haplotypes and between birds with CC ( B4/B4 ) haplotypes to show the integrity of the tolerance mechanism in the absence of graft rejection . Skin grafts were also performed between chickens with different haplotypes ( from CB to CC and from CC to CB ) to show the active recognition of non-self mechanism in the presence of incompatible MHC-BL genes . The grafts were inspected daily for evaluation of the surgical incision site and removed at day 11 , 14 , or 17 post-implant . The tolerance mechanism regulated by the MHC-BL genes was further assessed through the reporter heart graft experiment . One-day-old chicks hatched from healthy NC or from kDNA+ chicken donors received a graft of the NC or kDNA+ adult chicken receptor . The chick's heart was excised and placed into a Petri dish with cold PBS , pH 7 . 4 . The heart's blood was completely washed after longitudinal sections were cut and two changes of cold PBS were made [100]–[102] . One half of the donor heart was grafted in the subcutaneous tissue at the interscapular region of the receptor chicken , at the site where feathers were plucked under anesthesia . The skin wound was closed with stitches , and the wound was inspected daily . At day 11 , 14 , or 17 post-graft , the heart implant was excised , fixed in formalin , embedded in paraffin , and cut into 4-µm-thick sections , which were hematoxylin and eosin ( H-E ) -stained for histopathology or used unstained for immunohistochemistry study . The second half of the donor heart was fixed in formalin and processed for histopathology and immunohistochemistry study . The reporter heart grafting was conducted in kDNA+ and in healthy NCs of CB chickens with or without fluorochrome labeling , in order to phenotype the immune cells involved in the pattern of graft rejection or non-rejection . The quantitation of the number of blast cells in BMC aspirates from chicken femur bone was determined accordingly with morphologic criteria [103]–[105] . The cells were collected in 5 IU sodium heparin , and 10 µl of the aspirate was immediately smeared on two glass slides , fixed in methanol and H-E-stained . The microscopic exam revealed total count 14 . 1±2 . 4×104 blasts/ml , and 3±1 ml of BMC aspirate was injected in recipient chickens through an ulnar vein . Groups of eight-month-old congenic chickens of the Prague line received folate inhibitor ( Myleran , 14 mg/kg diluted in 10 ml PBS ) through a cannula per os and cytostatic methotrexate ( 150 mg/kg ) through injections in the wing vein [106]–[109] . Two days after the ablation of the bone marrow with the drugs , the chickens received marrow cells aspirates from the femoral bone of NC or kDNA+ congenic chickens . The NC and kDNA+ groups of chickens that underwent bone marrow ablation were employed in the transfer of BMCs through injection into the femurs of chickens in separate groups: i ) kDNA+ chickens that received bone marrow aspirates from NC; ii ) healthy NCs that received bone marrow aspirates from kDNA+ chickens; iii ) healthy NCs that received BMC aspirates from NC; and iv ) mock control NC chickens that did not undergo ablation but received BMCs from counterpart controls . Thirty days after bone marrow transplantation , the chickens had a one-day-old kDNA+ chick's heart implanted in the subcutaneous pouch at the interscapular region . The graft heart was removed for histological analysis at set times , and 4-µm-thick histological sections were made unstained for microscopic examination under UV filters , or H-E-stained for bright field microscopy . The pathology was examined in the tissue of F0 chickens that died in the course of the study and in the tissue of F1 , F2 and F3 progeny sacrificed at 10 to 12 months of age . The sections of the heart , skeletal muscle , large bowel , liver , kidney , and spleen tissues were fixed in 10% buffered formalin , embedded in paraffin , sectioned at 4 µm thick in a rotation microtome , and stained with H-E for histopathological study . The unstained tissues from chickens that had been injected with the fluorochromes were examined under an Olympus BX51 under UV light with filters for the red dye PKH26 ( 551 and 567nm ) and for the green dye PKH67 ( 490 and 502 nm ) , and images were collected simultaneously . The tissues from NC , from kDNA+ chickens , and from chick's heart graft haves , which were fixed in formalin and embedded in paraffin , were cut in a microtome . The unstained sections were subjected to three consecutive baths in xylene and hydration in an alcohol gradient ( 100% , 90% , 80% and 70% ) . Non-specific binding was blocked with 10% nonfat milk powder in PBS , pH 7 . 4 for 45 min at 37°C in a moist chamber . The tissue sections rinsed in PBS were incubated with a biotinylated mouse anti-chicken monoclonal antibody ( Mab ) for 2 h at 37°C . After three washes in PBS , the slides were incubated for 30 min with a Cy3-conjugated mouse anti-biotin Mab ( Sigma ) . After incubation , the slides washed twice in PBS were counterstained with Harris hematoxylin for 2 min , dehydrated in alcohol , treated with xylene and mounted with a glass cover . Quality control was ensured by omission of the primary antibody for the control and for the test exam . The reading was performed under an Olympus BX51 microscope under UV under suitable filter wavelengths for DAPI , FITC and CY3 . The photograph was captured by an Olympus DP76-U-TV0-63XC micro camera equipped with the cellSens Dimension image analysis program . We used eight primary mouse Mabs against chicken immune system cell surface proteins ( Southern Biotechnology ) used at 1/20 dilution , as described in the manufacturer's protocol: 1 ) anti-CD8-α specific for 34-kDa alpha-chain T cells , which is expressed in 80% of thymocytes , 15% of peripheral blood mononuclear cells and 50% of spleen cells; 2 ) anti-CD8-β , expressed in approximately 45% of peripheral blood mononuclear cells and 50% of spleen cells; 3 ) anti-TCRγδ specific for T CD8+ γδ cells; 4 ) anti-TCRvβ1 , which reacts with approximately 40–50% of peripheral blood mononuclear cells and 40% of splenocytes; 5 ) anti-TCRvβ2 , which reacts with approximately 9% of thymocytes , 15–20% of peripheral blood mononuclear cells and 13% of splenocytes; 6 ) anti-CD4 , which is a glycoprotein expressed on approximately 70% of thymocytes , 10% of spleen cells and 45% of peripheral blood lymphocytes; 7 ) anti-CD28; and 8 ) anti-CD45 , which is a transmembrane glycoprotein present on the surface of T and B cells . We also used CY3-labeled anti-biotin , at 1/200 dilution , and annexin V-FITC° to produce enhanced fluorescence signals with high photostability [110]–[112] for the rapid detection of apoptosis in living cells via fluorescence microscopy ( Sigma-Aldrich , USA ) . The annexins bind to phosphatidyl serine , which flips to the outer leaflet of the phospholipid bylayer membrane on apoptotic cells , and allows its identification . The greenish cytoplasm of apoptotic cells was identified by the annexin V-FITC antibody , whereas the nucleus was stained red by the propidium iodide . The mononuclear inflammatory cells that infiltrates the heart , skeletal muscle , large bowel , liver , kidney , and spleen of chickens that died in the course of the study , as well as in the F1 , F2 and F3 progeny sacrificed at 10 to12 months of age , were recorded . The intensity of the inflammatory infiltrate in the tissues was evaluated by the minimal rejection unit ( MRU ) , which defines the lysis of single target host cell by the immune lymphocytes [10] . The quantity of MRUs in four 2×1 cm sections indicated the intensity of the inflammatory infiltrates ( + MRU to ++++ diffuse MRUs ) . The H-E-stained sections of the myocardium from F0 that died in the course of the experiments and of the progeny ( F1 to F3 ) that were sacrificed at 10-to-12 months of age were examined under a bright field microscope , and the intensity of the inflammatory lesions was estimated . The chimera sequences were subjected to searches in BLASTn and BLASTx ( http://www . ncbi . nlm . nih . gov/genome/seq/BlastGen/BlastGen . cgi ? taxid=9031 ) . The T . cruzi sequences were analyzed at the Database Others ( Nucleotide collection ) and Somewhat similar sequences . The chicken DNA sequence was subjected to Database Gallus gallus genome reference and Somewhat similar analyses . CLUSTALW alignments were performed , and statistical significance ( p<0 . 001 ) was determined for the scores ( e-values ) recorded ( Tables S2 and S3 ) . The chimeras' repetitive DNA sequences were identified by the CENSOR-GIRI software ( http://girinst . org/censor/index . php ) . The KISS search tool was employed to identify potential gRNAs in the kDNA sequences as well as a work bench for RNA editing analysis in kinetoplastids with the aid of WU-Blastn-modified-matrix http://www . plosntds . org/article/info%3Adoi%2F10 . 1371%2Fjournal . pntd . 0001000 - pntd . 0001000-Lopez1 ( http://www . biomedcentral . com/content/supplementary/1471-2164-8-133-s1 . fas ) . Student's t test was used to detect differences between heart/body weight indexes , and the Kolmogorov-Smirnov test was used to compare mortality ratios between groups of chickens hatched from T . cruzi-inoculated eggs and from mock controls . The results were expressed as the means ± standard deviation . p<0 . 05 was considered statistically significant . This analysis included NCs and kDNA+ chickens that underwent natural death . The survival time ( months ) and the size ( weight ) of the heart of each chicken were recorded . The heart weight ( g ) and the chicken body weight ( kg ) were used to calculate the heart weight index: HWI = heart weight ( g ) /body weight ( kg ) .
The congenic lines CB ( B12/B12 ) and CC ( B4/B4 ) were used [91]-[95] . These lines are genetically identical except for the MHC class I ( B-F ) and class II ( B-L ) , and for other multigene families outside the MHC BF-BL region , which appears to determine effective antigen presentation during mature immune responses [94] , [95] and acute rejection of grafts . These congenic lines were used to demonstrate the heart graft rejection-dependent genotype modifications in the chicken genome following T . cruzi minicircle kDNA mutagenesis . In the skin graft experiment between CB ( B12/B12 ) and CC ( B4/B4 ) chickens we showed the integrity of the tolerance mechanism in the absence of graft rejection . Skin grafts were also performed between chickens with different haplotypes ( from CB to CC and from CC to CB ) to show the disruption of the tolerance mechanism in the presence of incompatible BF-BL region and other gene families outside the MHC complex [94] , [95] . The results demonstrated that skin grafts between MHC-compatible chickens ( within a particular syngeneic line ) were accepted permanently , whereas grafts exchanged between the CB and CC chickens were acutely rejected within 11 days . Further experiments were performed to evaluate heart tissue grafting in groups of five syngeneic chickens . One half of each one-day-old NC chicken heart was inserted into the inter-scapular subcutaneous tissue of an adult recipient . Three groups of independent experiments were performed for the following transplants: i ) between compatible CB ( B12/B12 ) chickens; ii ) between compatible CC ( B4/B4 ) chickens; iii ) between incompatible CB donors and CC recipients; and iv ) between incompatible CC donors and CB recipients . At different time points , the heart grafts were removed and subjected to histopathological evaluation . In combinations i and ii , the chickens with compatible MHC-B haplotypes did not reject the heart grafts . Microscopy revealed edema and vascular engorgement at 8 days , which was followed by loose connective tissue cuffing of the heart graft and no rejection of the donor myocardium at the 17th day . In contrast , the incompatible grafts ( groups iii and iv ) underwent rejection and inflammation-induced necrosis by the 14th day . The lymphocyte infiltrates in the reporter heart provided evidence of the disruption of immune tolerance and rejection . The second half of the heart sections , derived from control donors , exhibited normal histology . The results are summarized in Figs . 1A , 1B , and 1C . This system of syngeneic lines described above was used to evaluate the noxious ( auto ) immunity induced in syngeneic chickens subjected to genomic modifications by the integration of T . cruzi kDNA minicircles following the inoculation of 100 T . cruzi trypomastigotes through a hole in the air chamber shell [10] , [66] . The measurement of parasite template DNA in the chicken genome was conducted by PCR , with primer sets specific for the parasite kDNA and nDNA [96] , [97] . These assays revealed that the mitochondrial kDNA minicircle sequences were retained in the genomes of CB ( B12/B12 ) and CC ( B4/B4 ) chicks hatched from T . cruzi-inoculated eggs ( Fig . 2A ) . In view of the similar results obtained with these syngeneic lines , we chose the CB ( B12/B12 ) line , which grew faster than the CC ( B4/B4 ) line . The mock control chickens were nDNA- and kDNA-free . The specificity of the primers for the T . cruzi kDNA 330-bp and nDNA 188-bp sequences was demonstrated by the absence of the amplification of Leishmania braziliensis DNA [10] , [66] . The refractory nature of the CB chickens to T . cruzi infection was demonstrated by PCR , which had a sensitivity of 10 fg , 24-fold less than the total nDNA in a single T . cruzi trypomastigote [67] . Southern blot assays documented the integration of the kDNA minicircle sequences into the chickens' genomes . EcoRI digests of the DNA from the chicks hatched from T . cruzi-inoculated eggs and from mock control eggs were probed with radiolabeled wild-type kDNA minicircle sequences , revealing 18 kb band in the kDNA-mutated blood mononuclear cells , and 18 to 20 kb bands consistent with exogenous DNA integration in the chicken genome ( Fig . 2B and 2C ) . Southern blotting also revealed the absence of an nDNA band formed by the hybridization of the NC chicken DNA templates with the radiolabeled 188-nt DNA probe . Obtaining 18 and 20 kb bands instead of several size small bands consistent with a generalized mutagenesis stem from the repetitive sequences of TEs present in 164 out of 200 ( 82% ) integration spots , which shared microhomology with the minicircle CSBs present in CRs of the kDNA radio labeled probe [10] , [66] sequence smeared through the electrophoresis gel-transferred nylon membrane . These shared microhomology repeats dispersed in the chicken genome may explain the presence of 18 and 20 kb bands after overnight film exposure to a radio labeled membrane , and heavy smears instead of multiple small bands after film exposure to radiograph for 72 hours or more ( Fig . 2B and 2C ) . Similar findings were shown in previous papers [10] , [66] . CB chickens were used for immune cell labeling experiments to evaluate both kDNA+ chickens hatched from T . cruzi-inoculated eggs and syngeneic NCs that never had contact with T . cruzi . The PkH26 and the PkH67 fluorescent cell linkers ( Sigma-Aldrich ) were used to stably incorporate a dye with long aliphatic tails into the lipid receptors to labeling membrane of rapidly growing cells . These fluorochomes have been used for labeling epithelial cells in the gut [113] , leukemia cells [114] , lymphocytes [115] , epithelial and mesenchymal stem cells [116] , and cord blood T lymphocytes [117] for ex vivo and in vivo cell proliferation analysis . The chickens in these groups received alternate injections of red ( PkH26 ) or green ( PkH67 ) fluorochromes intravenously every two weeks ( see Methods ) . After 15 days , the double-fluorochrome-labeled chickens were euthanized , and sections of the bone marrow , spleen , and heart were subjected to microscopic analysis under bright-field and UV illumination , using the appropriate wavelength filters . The bone marrow stem cells and blast-derived immune lymphocytes , which were stored in the spleen and often infiltrated the heart , exhibited intense fluorescence , unlike the bone , connective tissue , smooth and striated muscles , which exhibited faint , natural fluorescence . The fluorochrome-labeled cells stored in the spleen follicles migrated freely into the blood vessels . H-E-stained sections from kDNA+-derived bone marrow often exhibited hyperplasia , in contrast to the normal histology in the NC chickens . In some kDNA+ spleens , the lymphoid follicles contained approximately twofold brighter immune cells compared with the NC chicken cells ( Fig . 1B ) . To demonstrate whether it was possible to transfer the lesions observed in the CB kDNA+ chickens , we performed passive transfer of blood mononuclear cells from these chickens to eight-month-old syngeneic , NC recipient chickens . In the test group , each of five NC chickens received injections of 107/ml kDNA+ blood mononuclear cells that were in vitro-labeled with red and green fluorochromes , administered one week apart through an ulnar vein . In the controls , the same protocol was used for the transfer of the fluorochrome-labeled mononuclear cells from NC to kDNA+ chickens . Fifteen days after the third weekly injection , the chickens from both groups were euthanized , and their tissues were examined . Histopathology revealed that the kDNA+ donor double-labeled immune lymphocytes formed scarce , small infiltrates in the hearts of the NC recipient chickens ( Fig . 3A ) , but there was no gross pathology . In contrast , passive transfer of fluorochrome-labeled mononuclear cells from NC birds did not infiltrate the recipient chickens' hearts . The kDNA+ chickens died naturally during the course of the experiments , and gross and microscopic pathology was recorded ( Fig . 3B ) . The microscopic examination of four 2 × 1 cm H-E-stained sections from the F0 chickens that underwent natural death , consistently revealed: 15 chickens ( 25% ) had evidence of an MRU ( + ) , herein defined as single target heart cell lysis by lymphocytes; seven chickens ( 12% ) exhibited a few ( ≤6 ) nearby MRUs ( ++ ) and lysis of target cells; six chickens ( 10% ) exhibited confluent MRUs ( ≥6 ) ( +++ ) and lysis of multiple heart fibers; three chickens ( 5% ) exhibited diffuse myocarditis ( ++++ ) and lysis; and 29 chickens ( 48% ) exhibited normal heart ( - ) histology . Body weight and heart weight were recorded . The development of heart pathology over time resulted in the early death of the kDNA+ chickens at 13 . 2±7 months , whereas the NC chickens survived 19 . 2±8 months; this difference was statistically significant ( Fig . 3C ) . The kDNA+ chickens developed gross cardiomegaly-induced shortness of breath , cyanosis , hydrothorax , and ascites , which were correlated with myocarditis and with the rejection of the heart fibers . Three progeny ( F1 , F2 and F3 ) showed muscle weakness . The heart weight ( g ) and body weight ( kg ) of the kDNA+ and NC chickens were used to generate indices , which exhibited significant differences ( Fig . 3D ) . The collective data revealed the kDNA+ chickens' loss of immune tolerance and rejection of compatible heart grafts . The kDNA- chickens , with their absence of heart pathology , exhibited no clinical manifestations . Three chickens with mutations in the dystrophin gene ( birds 3 , 15 , and 24 , S2 Table ) revealed foam cells and inflammatory infiltrates in the heart and skeletal muscles ( Fig . 4A and 4B ) . Additionally , inflammatory lesions alone were often of lesser intensity in the progeny than those shown in the F0 chickens . The heart sections revealed inflammatory infiltrates ( + to +++ ) and target cell lysis in 35 ( 25% ) progeny chickens ( F1 , 12; F2 , 13; F3 , 10 ) . The skeletal muscle sections of 15 ( 10 . 6% ) progeny chickens presented the inflammatory infiltrates ( Fig . 4C ) . The parasympathetic nervous system showed inflammatory infiltrates ( + to +++ ) and occasional neuronal cells lysis ( Fig . 4D and 4E ) in 10 ( 7% ) cases . Liver and kidney showed an absence of inflammatory infiltrates . The NC chickens revealed normal histology in the tissues and organs examined . Taken together , these findings reveal that kDNA+ chickens bear the potential to initiate autoimmune inflammatory lesions that appear to recrudesce over time . The pathologic findings in the kDNA+ progeny F1 , F2 and F3 ( Fig . 5 ) , which were euthanized at 10 to 12 months of age , was documented . The production of small inflammatory infiltrates in the heart of NC by passive transfer of blood mononuclear cells from kDNA+ chickens revealed an inherent heart homing activity by immune lymphocytes . This finding led us to conduct transfer experiments with growing BMC-derived lymphoblasts , aiming at the inhibition of the Chagas-like heart disease . The inhibition of the pathology in the hearts of adult kDNA+ chickens ( group i ) or its transfer to NC chickens ( groups ii and iii ) through BMC transplantation was evaluated in three independent experimental groups . The chickens from groups i , ii , and iii , each with five birds , had their BMCs destroyed by injections of cytostatic and anti-folate drugs ( see Materials ) . After two days , the kDNA+ chickens from group i received BMCs from NCs , the NC chickens from group ii received BMCs from kDNA+ chickens , and the NC chickens from group iii received BMCs from NC chickens . In group i , the kDNA+ chickens that received NC healthy BMCs exhibited no myocarditis and no heart rejection ( Fig . 5A ) . In group ii , the NC chickens that received BMCs from kDNA+ chickens exhibited myocarditis and heart rejection ( Fig . 5B ) . In group iii , the NC chickens that received BMCs from NC counterparts were completely healthy after six months . To assess the in vivo protective effects of healthy BMC transplantation into kDNA+ chickens , we used the syngeneic CB chicken model system to monitor the pathology through reporter heart grafting . One month after the transplantation of healthy NC marrow cells into the kDNA+ chickens that had their BMC destroyed with drugs , we grafted a syngeneic reporter heart to evaluate whether it was possible to reconstitute the chickens' immune tolerance . We observed in triplicate groups of five chickens that the reporter heart grafts were accepted and that the healthy grafts became encapsulated by fibrous tissue by day 17 ( Fig . 3C ) . In contrast , the compatible reporter hearts implanted into the NC chickens that received sick BMCs from kDNA+ donors were rejected by the recipients' immune cells . The healthy immune cells from NC chickens that replaced the kDNA+ chicken BMCs destroyed with drugs did not reject the syngeneic heart grafts . Control chickens ( group ii ) receiving the kDNA+ BMCs developed cardiomegaly and heart failure . The results presented in Fig . 5C illustrates group ii , second set experiments . We next sought to demonstrate clearly the familial predisposition and genetic susceptibility factors involved in the pathogenesis of heart rejection in syngeneic chickens . After breeding was conducted , we evaluated the pattern of inheritance of the T . cruzi kDNA minicircle sequences integrated into the parental genomes , which were vertically transferred to the progeny . The crossings produced the kDNA+ chicken families A , B , and C ( Fig . 6 ) . We observed that the kDNA minicircle sequences integrated into the somatic cells from the parents and from the progeny ( Fig . 2A , 2B and 2C ) . The somatic cells of 76 individuals from families A , B , and C consistently displayed evidence of the specific 330-bp kDNA minicircle band and its catamers . The crossings indicated that the kDNA minicircle sequences integrated into the genomes underwent vertical transfer to the descendants . These results confirm and extend previous knowledge of the heritability of kDNA mutations in an outbred chicken model system [10 , 66 , and 67] . In this study , we combined the kDNA primer sets ( S1 Table ) with host primers , targeting a flanking host DNA sequence ( FN599618 ) annealing to TEs [10 , 66 , and 67] . The somatic cell amplicon-derived clones exhibited evidence of host DNA-kDNA minicircle sequences , and revealed 200 non-redundant chimeras ( S2 Table ) . The kDNA integration into the somatic cells was distributed in 23 of 38 autosomes and in the Z sex chromosome ( HG531391 to HG531590 ) ( Fig . 7A ) . Integration into chromosomes 1–5 and Z accounted for 58% of the total number of events . At the chimera junction site , the CA-rich microhomology revealed 8- to 25-nt short repeats . This result suggests that microhomology-mediated end joining was the primary mechanism mediating the host DNA-kDNA integration in the chickens , and that the microhomology hotspots might represent repetitive , truncated , reshuffled sequences , likely from TEs . The longest host DNA-kDNA chimera sequence obtained was 1479 nt , and the kDNA minicircle maximal length reached 624 nt , with an average of 242±98 nt . The chimera minicircle alignments revealed an enormous diversity , explained by remarkable structural differences in their VRs . In contrast , almost perfect alignments among the kDNA CRs were obtained , and furthermore , the CSBs in the CRs revealed significant homology with highly conserved microhomology repeats dispersed in the chicken genome [10] , [66] . At the kDNA integration site , the minicircle VR ( average 230–250 bp ) was usually flanked by truncated CR and VR fragments ( 50 to 100 bp ) often observed amid CR segments , exhibiting high similarity scores ( p<0 . 05 ) . The variable topological patterns , consisting of host DNA flanking the kDNA minicircle at the junction site , suggested a variety of recombination events . To verify that the minicircle VR sequences originated from T . cruzi , we evaluated the sequences for the presence of gRNAs ( S3 Table ) . The full complement of 113 gRNAs [68] , [69] was predicted in the kDNA VR minicircle sequence chimeras in the chicken genome . In the syngeneic chicken model , DNA integrations were frequently observed at locus NW_003763650 . 1 , coding for the NADP-dependent mitochondrial malic enzyme ( NADPME ) [118] . Fifteen F1 , F2 , and F3 progeny chickens in families A , B , and C had kDNA minicircle integrations at the NADPME locus ( Fig . 7B ) . A total of 30 kDNA mutations had minicircles inserted exactly at nt 90092 of the NADPME gene sequence on chromosome 1 , and 15 of these kDNA mutations were obtained through template amplifications with gene-specific primer sets ( S1 Table ) . All of these mutations resulted in an identical 1 . 7-kb NADPME sequence at locus NW_003763650 . 1 ( S2 Table ) . In this series of kDNA mutations , some minicircle VRs were predicted to transcribe gRNAs ( S3 Table ) . The kDNA integrations in retrotransposon CR1 sequences were distributed in the parental chickens' genomes ( HG531593 , locus NW_003763785 . 1; HG531594 , locus NW_0037636687 . 1; HG 531596 , locus NW_001471668 . 2 ) and in the progeny genomes ( HG531644 , locus BX640540 . 3 ) . The repetitive TEs , mainly non-LTR retrotransposon CR1 represented ( 82% ) of chimera sequences kDNA-human DNA . In one instance , Hitchcock LTR retrotransposons ( 11% ) were found ( Fig . 7C ) . Additionally , the kDNA integrated into the CR1B segment ( HG531589 ) present in a gene sequence ( Fig . 7D ) , in which the host DNA coding region ( AC231413 . 2 ) might represent an intron-derived degenerate TE . Eight independent events documented the hitchhiking of a kDNA minicircle to a second chromosomal location ( Table 1 and Fig . 7E ) . The resultant mosaics can likely be explained by endogenous CR1 reverse transcriptase activity [10 , 65 , 66 , and 67] . The kDNA integrations were often found in the CR1 retrotransposons of syngeneic chickens , as previously documented in outbred chickens [10] , [66] . Interestingly , a series of kDNA integrations illustrated the enormous genetic diversity embodied in the mutation events . These chimeras exhibited almost perfect alignment ( 96% ) with the host DNA , and the kDNA exhibited 86% identity . Additionally , the kDNA mutations at locus NW_001471534 . 2 of the pyruvate dehydrogenase kinase gene were present in the genomes of birds 19 , 39 , and 73 , belonging to families A , B , and C , respectively . In all of these chickens , CLUSTALW analysis revealed perfect alignment of the gene sequence and almost perfect alignment of the kDNA minicircles ( HG531663 , HG 531708 , and HG531758 ) . The topological differences among the kDNA sequences integrated at the chicken coding regions could be explained by the unlimited genetic differences among thousands of minicircles' VRs inserted into early embryonic stem cells . Therefore , a single T . cruzi-infected gonium has the potential to generate a variety of kDNA-mutated gametes . This variety explains why the T . cruzi mitochondrial kDNA minicircle transfers , which resulted in topological modifications , did not allow for the identification of siblings through sequence analyses . Moreover , the phylogenetic sequence analyses of the chimeras , which exhibited evidence of recombination , hitchhiking , and reshuffling , could not verify the parents of individuals . The kDNA integrations into the chicken genome introduced new open reading frames ( ORFs ) with the potential for the translation of chimeric proteins ( S4 Table ) . The study revealed putative ORFs , and the BLASTp algorithm demonstrated that among those , there were 89 hypothetical chimeric proteins , 62 of which bore no significant similarity to existing proteins . Next , we searched for autoantibodies that recognize specific anti-self heart antigen in cases of organ specific myocarditis and DMC [35] , [49] , [50] . These experiments shown in Fig . 8 revealed the absence of anti-heart antibodies in the kDNA+ as well in the kDNA- chickens . Additionally , Fig . 8 also revealed the absence of auto-antibodies against T . cruzi antigens in the kDNA+ chickens , thus suggesting these birds are immunetolerant to specific parasite antigens . Interestingly , chagasic patients ( average 40±5 years of age ) showing high titers of specific antibody to T . cruzi showed the absence of autoantibodies against chicken heart antigens . These results align with our early hypothesis that genetically driven autoimmune myocarditis in the transkingdom model of Chagas disease may be antigen independent [10] . The functional consequences emerging from the integration of exogenous DNA into the vertebrate genome seem to be directed at BMC progenitors of effector lymphocytes , causing an autoimmune reaction against heart tissue . Accordingly , the conventional antigen-driven autoimmune hypothesis was tested by comparing heart tissue extracts from kDNA+ and kDNA- chickens by two-dimensional gel electrophoresis ( 2DE ) . The protein profiles were similar in three independent experiments ( S1 Fig . ) . Only a single protein spot in the kDNA+ sample was identified by peptide mass fingerprinting as C-reactive protein , which is overexpressed in inflammatory cardiomyopathy . We then searched for specific antibodies to heart cells and to T . cruzi neo-antigens in the serum of kDNA+ and NC chickens . ELISA revealed the absence of specific antibodies in the serum of kDNA+ and NC chickens ( Fig . 8 ) . Additionally , the results of control experiments revealed that the phenotype changes , transcribing putative chimeric ORFs ( S4 Table ) , did not translate any recognizable neo-antigen . Altogether , in the absence of antibodies to T . cruzi and to heart antigens , these findings can be clearly explained by the kDNA+ chicken immune tolerance achieved during early embryonic development . In the absence of anti-self antibodies , the antigen-driven molecular mimicry failed in the parasite-free chicken model system of Chagas-like heart disease . Next , we tested our hypothesis that genotype-modified bone marrow-derived immune effector cells can attack and destroy syngeneic cells in the absence of the conventional antigen-dependent autoimmune reaction . To demonstrate the genetically driven immune rejection of the syngeneic heart , we used the kDNA+ progeny and NC chickens . We subcutaneously grafted syngeneic reporter hearts into sexually mature chickens . Groups of five syngeneic birds were used in triplicate experiments: i ) kDNA+ chickens; ii ) NC chickens; iii ) kDNA+ chickens with drug-destroyed BMCs replaced by healthy NC marrow cells to inhibit heart pathology; iv ) NC chickens undergoing transfer of the heart pathology , with the drug-destroyed healthy BMCs replaced by the kDNA+ marrow cells; and v ) NC chickens with drug-destroyed BMCs replaced by NC marrow cells . In this study , we measured the clonal proliferation of the CD3+ , CD28+ , and CD45+ cell precursors of the thymus-dependent CD8α+ and CD8β+ effector cells expressing TCRγδ , vβ1 and vβ2 receptors , which infiltrated the adult hearts and the reporter heart grafts ( Fig . 9 ) . The results consistently revealed gross and microscopic effects similar to those present in the fluorochrome-labeled syngeneic chickens , whereby effector lymphocytes attacked and destroyed heart graft cells . In light of the absence of anti-self heart antibodies in the kDNA+ CB chickens , we performed further experiments to elucidate the mechanism behind the disruption of immune tolerance to self-antigens , in the absence of chimeric ORF-putative neo-antigens , which would result in T cell-mediated rejection of the heart graft . Subcutaneous grafts consisting of a one-day-old kDNA+ syngeneic chick heart were not rejected by NC chickens . Subcutaneous grafts of kDNA+ reporter hearts into NC birds that had their BMCs destroyed by drugs and replaced by healthy marrow cells were not rejected either . These findings suggest that putative neo-antigens in the kDNA+ heart grafts either were not expressed or were not 'visible' to the immune system of the syngeneic chickens [119] . Taken together , the data suggest that the rejection of the hearts in kDNA+ chickens was an antigen-independent autoimmune phenomenon resulting from parasite-induced genotype modification and clonal proliferation of the effector T cells that attacked the heart . Thus , our findings confirm and extend our previous results of genetically driven , antigen-independent autoimmunity in a chicken model of human Chagas disease [10] , [65] , [66] . The immune effector cells undergoing apoptosis in the hearts of kDNA+ chickens , as well as in the reporter heart grafts , were identified by fluorescence microscopy using FITC-conjugated annexin V antibody [110]–[112] . The apoptotic cells detected frequently in the hearts and spleens of adult kDNA+ chickens ( Fig . 9B ) were not observed in the tissues of control chickens . These results suggest that the apoptosis of the effector immune cells might be a down-regulation mechanism , preventing rejection of the target cells over a long time span in the genetically modified kDNA+ chickens , with disruption of immune tolerance and delaying of the onset of overt autoimmune heart disease .
The genes and loci in the text are as follows: The NADPME locus NW_003763650 . 1; CR1 locus NW_003763785 . 1 ( HG531593 ) ; locus NW_0037636687 . 1 ( HG531594 ) ; locus NW_001471668 . 2 ( HG 531596 ) ; and locus BX640540 . 3 ( HG531644 ) . SOX-6 locus NW_003763785 . 1 ( HG531593 , HG531392 ) ; dystrophin locus emb|V01390 . 1; gbL26251 . 1; pyruvate dehydrogenase kinase locus NW_001471534 . 1 ( HG531471 ) ; cytospin A locus NW_001471461 . 2 ( HG531751 ) ( see S2 Table ) .
|
Chagas heart disease , stemming from infection by the protozoan parasite Trypanosoma cruzi , which is endemic in South America , is now present on five continents . The treatment now available for the clinically manifested heart disease is considered unsatisfactory . We postulate that manifestations of Chagas-like heart disease in a chicken model system refractory to T . cruzi infection might be prevented by killing the sick bone marrow cells of the recipient with anti-folate and cytostatic drugs , and transplanting healthy bone marrow cells from a syngeneic donor . We performed triplicate experiments in various groups of five chickens . Chickens hatched from T . cruzi-inoculated eggs , retaining the parasite mitochondrial kDNA in the genome , developed Chagas-like heart disease and died . The integration of T . cruzi kDNA minicircles into the chicken genome and the heritability of that integration were recorded , and the genetic underpinning of the autoimmune heart disease was demonstrated . Genome modifications in kDNA-mutated chickens led to disruption of immune tolerance and thus explained , for the first time , the rejection of the heart . Moreover , we found that killing the sick kDNA-mutated bone marrow cells with cytostatic and anti-folate drugs and transplanting healthy marrow cells inhibited heart rejection . These results suggest that bone marrow transplantation could be used to prevent Chagas heart failure .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"immunopathology",
"medicine",
"and",
"health",
"sciences",
"animal",
"genetics",
"immunochemistry",
"chagas",
"disease",
"clinical",
"immunology",
"heredity",
"genetics",
"protozoan",
"infections",
"biology",
"and",
"life",
"sciences",
"immunology",
"tropical",
"diseases",
"parasitic",
"diseases",
"neglected",
"tropical",
"diseases",
"autoimmunity",
"gene",
"transfer"
] |
2014
|
Inhibition of Autoimmune Chagas-Like Heart Disease by Bone Marrow Transplantation
|
Palpalis-group tsetse , particularly the subspecies of Glossina palpalis and G . fuscipes , are the most important transmitters of human African trypanomiasis ( HAT ) , transmitting >95% of cases . Traps and insecticide-treated targets are used to control tsetse but more cost-effective baits might be developed through a better understanding of the fly's host-seeking behaviour . Electrocuting grids were used to assess the numbers of G . palpalis palpalis and G . fuscipes quanzensis attracted to and landing on square or oblong targets of black cloth varying in size from 0 . 01 m2 to 1 . 0 m2 . For both species , increasing the size of a square target from 0 . 01 m2 ( dimensions = 0 . 1×0 . 1 m ) to 1 . 0 m2 ( 1 . 0×1 . 0 m ) increased the catch ∼4x however the numbers of tsetse killed per unit area of target declined with target size suggesting that the most cost efficient targets are not the largest . For G . f . quanzensis , horizontal oblongs , ( 1 m wide×0 . 5 m high ) caught ∼1 . 8x more tsetse than vertical ones ( 0 . 5 m wide×1 . 0 m high ) but the opposite applied for G . p . palpalis . Shape preference was consistent over the range of target sizes . For G . p . palpalis square targets caught as many tsetse as the oblong; while the evidence is less strong the same appears to apply to G . f . quanzensis . The results suggest that targets used to control G . p . palpalis and G . f . quanzensis should be square , and that the most cost-effective designs , as judged by the numbers of tsetse caught per area of target , are likely to be in the region of 0 . 25×0 . 25 m2 . The preference of G . p . palpalis for vertical oblongs is unique amongst tsetse species , and it is suggested that this response might be related to its anthropophagic behaviour and hence importance as a vector of HAT .
Between 1997 and 2006 , about 250 , 000 new cases of Human African Trypanosomiasis ( HAT , or sleeping sickness ) were reported [1] . For >95%% of these cases , the disease started with a bite from one of four subspecies of tsetse: Glossina palpalis gambiensis ( in Guinea and Côte d'Ivoire ) , G . p . palpalis ( in Benin , Nigeria , western Cameroon , Equatorial Guinea , Gabon , south-western Republic of Congo , south-western Democratic Republic of Congo and western Angola ) , G . fuscipes fuscipes ( in eastern Cameroon , Central African Republic , western Republic of Congo , northern DRC , Sudan , Uganda ) , and G . f . quanzensis ( in southern DRC and northern Angola ) [2] . Efforts to tackle HAT have been based largely on case-detection and treatment in humans [1] rather than vector control , largely because methods for controlling tsetse are too expensive and logistically demanding [3] . The use of natural ( insecticide treated cattle ) or artificial ( traps and insecticide-treated targets , sometimes baited with attractants ) baits are the only techniques that might be applied by local communities [3]–[7] . However , their wider use is constrained by the low densities of livestock in HAT-affected areas [8] and/or the poor performance of artificial baits for Palpalis-group tsetse . In contrast to Morsitans-group tsetse , Palpalis-group species are less responsive to host odours [9] and hence artificial baits must be deployed at densities that are not affordable or sustainable for poor people . However , recent results have revived the prospects for the use of cost-effective baits against HAT . The performance of artificial baits can be enhanced by the use of attractants which double the capture rates [10] , [11] . Second , several studies [12]–[14] suggest that significant improvements in cost-effectiveness of baits for vectors of HAT might be achieved through the exploitation of the visual responses to hosts . For instance , studies of G . f . fuscipes in Kenya showed that reducing the size of the target from 1 m2 to 0 . 125 m2 only halved the number of tsetse that contacted the target thereby giving a four-fold improvement in the tsetse killed per dollar spent on cloth [12] . Of course , the material cost of targets is only part of the total cost of deploying them and we would expect that the logistical costs of deploying targets will also be considerably reduced when using tiny targets . The relationship between a target's size and the number of Palpalis-group tsetse differs markedly from that of Morsitans-tsetse – for the latter smaller targets are not cost-effective [15] . This suggests that there might be other differences in the visual responses of Palpalis- and Morsitans-group which tsetse which might be used to develop better targets . Hitherto , research to improve target design has focussed on responses to colour [16]–[20] and size [12] but not shape . However , studies of Morsitans-group tsetse suggest that shape is important . Various studies in Zimbabwe have shown that more G . morsitans and G . pallidipes are attracted to and land on horizontal-oblongs rather than vertical ones [21] , [22] . This shape recognition is thought to enable tsetse to discriminate their hosts from the environment . Important hosts , such as warthog and buffalo , are horizontal oblongs living in a visual environment of vertical oblongs formed by savannah woodland . This attraction to horizontal shapes is also thought to explain , at least in part , why Morsitans-group tsetse are not attracted to humans [21] . Intriguingly , Palpalis-group tsetse have a wider range of hosts which includes humans [23]–[25] and they are not confined to savannah woodlands . Hence , these species might be expected to display different behavioural responses to shape . An understanding of these responses would contribute to the rational development of more cost-effective designs of target . Consequently , this study assessed the responses of G . p . palpalis and G . f . quanzensis to targets of various shape . Separate studies have shown that target size has important effects on the numbers of tsetse attracted to and landing on a target [12]–[14] . We therefore also assessed whether responses to shape were affected by target size .
Arrangements of electrocuting grids were used to assess the responses of tsetse to various visual baits [27] . Two types of electrocuting grid were used:- The E-target and E-nets were often operated side-by-side and thus tsetse that approached the E-target but did not land on it were often caught by the adjacent E-net . The grids were mounted on metal trays ( 5 cm deep ) containing soapy water , which caught and retained electrocuted flies . E-targets were of varying dimensions , but E-nets were always 0 . 5 m wide ×1 . 0 m high ( see Fig . 1 for examples of arrangements of electrocuting grids ) . Studies of the numbers of tsetse attracted to and landing on small ( e . g . , 0 . 1×0 . 1 m ) E-targets face the problem that the framework which supports the grid of wires may itself be a source of visual stimuli . To overcome this , we conducted a second series of experiments where we placed an E-net next to various panels of black cotton cloth mounted on a simple wire frame . These panels were not enclosed in a grid , and hence , tsetse that landed on it were not caught . Instead , the catch from the flanking E-net provided a relative measure of the numbers of tsetse attracted to the target ( Fig . 1C ) . The visual targets are referred to as ‘inert targets’ to distinguish them from the electrified E-targets . All field experiments were carried out for a 4 h period between 09:30 hours and 14:30 hours local time , when G . palpalis and G . fuscipes are most active [29] , [30] . Visual baits were compared over 10–21 days in a series of Latin-squares , of days×sites×treatments . Experimental sites was at least 100 m apart . To facilitate comparisons across species and experiments , all experiments included a standard treatment comprising an E-target ( 1 m×1 m ) flanked by an E-net ( 1 m×1 m ) .
For both species , larger targets caught more tsetse but the increase was relatively slight . For instance , increasing from a 0 . 06 m2 to a 1 m2 target only doubled the catch of G . p . palpalis and had an even smaller effect for G . f . quanzensis . The results ( Fig . 6 ) show that for all targets , irrespective of shape and/or species , the catch density index decreases as the size of the target increases showing that it is more cost effective for control programmes to produce large numbers of small targets from the material available .
We demonstrate that catch increases with target size but the increase is not in proportion to the increase in target surface area . Hence , paradoxically , the numbers of tsetse killed per area of cloth , and by implication tsetse killed per dollar , decreases with increasing target size . The response to size shown here is similar to that of other Palpalis group species [12]–[14] . In particular , there is only a relatively modest doubling in the number of tsetse attracted to large ( 1 m2 ) targets versus small ( e . g 0 . 25×0 . 25 m ) ones . Given that tiny targets plus flanking nets ( 0 . 25×0 . 5 m ) use 1/8th and 1/24th the amount of materials required respectively for the large 1 m2 targets or biconical traps , which are currently used in control programmes , it is clear that considerable savings in costs are gained by using tiny targets in control operations . It is interesting to note that as the size of a target is increased , the number of tsetse attracted per unit area of target decreases for Palpalis-group species but increases for Morsitans-group tsetse [15] . We are only just beginning to understand the fundamental differences in host location behaviour between the groups . Beyond this general principle , the present results should be used with caution in identifying the optimal size of target . Taking the present results at face value for instance , a very small target ( 0 . 01 m2 ) had the highest catch density index , and since an E-net without any target caught some tsetse it has an infinitely high catch density . Concluding that no target will be most cost-effective is clearly nonsense ! It is likely that since Palpalis-group tsetse are very sensitive to small targets , the structures associated with electric grids ( transformer , 12 V battery , supporting frame of the grid ) attract some tsetse , despite our efforts to make these items as inconspicuous as possible . The 0 . 01 m2 target did not catch significantly more tsetse than no target and hence it seems that tsetse are not responding to targets of 0 . 1×0 . 1 m or smaller . The 0 . 25×0 . 25 m target did catch significantly more tsetse than no target and this probably represents the smallest target that might be considered . The catch density declines steadily as size increases and there is no evidence that more tsetse were attracted to a 1 m2 target than a 0 . 5 m2 one . Hence a target in the region of 0 . 25×0 . 25 to 0 . 5×0 . 5 m seems likely to be optimal . The performance of these small targets is crucially dependent on the presence of a flanking net: while Palpalis-group tsetse are attracted to small objects , few land on them and hence a flanking net treated with a insecticide is essential for killing flies that visit but do not land . Recent results [12]–[14] suggest that a flanking net equal in size to the target is optimal . The present results suggest that while there are marked differences in the responses of G . f . quanzensis and G . p . palpalis to oblongs , squares were as attractive as oblongs providing each had an equivalent surface area . Hence , square targets are likely to be effective to a wider range of species rather than , say , having vertical oblong targets for G . p . palpalis and horizontal ones for G . f . quanzensis . The present results along with those of Rayaisse et al . [14] are the first demonstration of a tsetse species ( G . p . palpalis ) being attracted to a vertical oblong in preference to a horizontal one . For all other species , vertical and horizontal oblongs are either equally attractive ( G . m . morsitans and G . pallidipes , [12]; G . f . fuscipes , [21] ) or horizontal oblongs are more attractive ( G . m . morsitans and G . pallidipes , [32]; G . f . quanzensis , present study ) . Previously , the preference for horizontal oblongs has been assumed to be related to the general shape of the mammalian hosts of tsetse [33] . It is therefore remarkable that just one species should not display this response . It is tempting to speculate that this is related to its anthropophilic feeding habit [22]; responding to an upright form may be adaptive for day-active Diptera that feed on humans . The present study found that while G . p . palpalis was attracted to vertical oblongs , horizontal oblongs elicited a stronger landing response . Studies of the responses of Morsitans-group tsetse have also found marked differences in the orientation and landing responses of tsetse to shape: for G . m . morsitans and G . pallidipes , horizontal and vertical oblongs are equally attractive but the former elicits a stronger landing response . For G . f . quanzensis too , the horizontal oblong E-targets caught 7x more tsetse than the vertical ones when they were not accompanied by flanking E-nets , compared to a two-fold difference when the E-nets were present . This suggests that the horizontal targets are more attractive and elicit a stronger landing response . There are clearly many subtle inter-specific differences in the responses of tsetse to target shape .
|
While the numbers of cases of human African trypanosomiasis ( HAT ) is now less than 10 , 000 reported cases per year , progress against the tsetse species that spread the disease is poor , with ∼10 million square kilometres of sub-Saharan Africa still being infested . This widespread persistence of vectors and reservoir hosts threatens the long-term sustainability of recent gains against HAT . Better progress against the vector would be achieved by developing cheap , effective and practical methods of tsetse control . Toward this end , we are improving the design of insecticide-treated targets to attract and kill tsetse . Here we show that for two important vectors of HAT , Glossina palpalis palpalis in Côte d'Ivoire and Glossina fuscipes quanzensis in the Democratic Republic of Congo , small ( between 0 . 25 m and 0 . 5 m square ) targets of black cloth with equally sized panel of fine black netting are ∼10x more cost-effective than the larger ( ∼1 m square ) targets or traps commonly in use .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"african",
"trypanosomiasis",
"parasitic",
"diseases"
] |
2011
|
How Do Tsetse Recognise Their Hosts? The Role of Shape in the Responses of Tsetse (Glossina fuscipes and G. palpalis) to Artificial Hosts
|
Anopheles aquasalis is a major malaria vector in coastal areas of South and Central America where it breeds preferentially in brackish water . This species is very susceptible to Plasmodium vivax and it has been already incriminated as responsible vector in malaria outbreaks . There has been no high-throughput investigation into the sequencing of An . aquasalis genes , transcripts and proteins despite its epidemiological relevance . Here we describe the sequencing , assembly and annotation of the An . aquasalis transcriptome . A total of 419 thousand cDNA sequence reads , encompassing 164 million nucleotides , were assembled in 7544 contigs of ≥2 sequences , and 1999 singletons . The majority of the An . aquasalis transcripts encode proteins with their closest counterparts in another neotropical malaria vector , An . darlingi . Several analyses in different protein databases were used to annotate and predict the putative functions of the deduced An . aquasalis proteins . Larval and adult-specific transcripts were represented by 121 and 424 contig sequences , respectively . Fifty-one transcripts were only detected in blood-fed females . The data also reveal a list of transcripts up- or down-regulated in adult females after a blood meal . Transcripts associated with immunity , signaling networks and blood feeding and digestion are discussed . This study represents the first large-scale effort to sequence the transcriptome of An . aquasalis . It provides valuable information that will facilitate studies on the biology of this species and may lead to novel strategies to reduce malaria transmission on the South American continent . The An . aquasalis transcriptome is accessible at http://exon . niaid . nih . gov/transcriptome/An_aquasalis/Anaquexcel . xlsx .
Anopheles aquasalis is a neotropical malaria vector , found along the northern coast of South America . Its geographical distribution extends from Brazil to Panama on the Atlantic shore and from Panama to Ecuador along the Pacific coast [1]–[6] . Their larvae develop preferentially in brackish water such as in mangrove swamps and coastal ground pools , but they also are capable of living in fresh water and often occur several kilometers from the coast [7] . The species has been reported as malaria vector in Venezuela , Brazil , Trinidad and The Caribbean [8]–[12] . Plasmodium falciparum and P . vivax , the two main human malaria parasites , are transmitted by An . aquasalis [12] , [13] . Its epidemiological importance was confirmed during a P . vivax malarial outbreak in 1991–1992 occurred in Trinidad which was linked to An . aquasalis [14] . Despite its importance as a malaria vector in Central and South America , regions responsible for 22% of the global area at risk of P . vivax transmission [15] , little is known about its genome , transcriptome and proteome . Efforts to colonize this species [12] , [16] provided the basis for studies assessing development , gene expression and immune responses to Plasmodium infection [12] , [17]–[24] . The role of reactive oxygen species and JAK-STAT pathway in the control of P . vivax infection were characterized in An . aquasalis . However , it was also demonstrated that this anopheline promotes an apparent weak response against P . vivax infection [18] which is supposed to be related to its susceptibility to this parasite species . Corroborating with this hypothesis , a recent work showed that colonized An . aquasalis presents higher infection rates and oocyst numbers when compared to other Neotropical anophelines [25] . Here we describe the transcriptomes and deduced proteomes of An . aquasalis larvae and adults fed on sugar and on blood . This data set provides indispensable information for the systematic and comprehensive analysis of molecules that may play an active role in mosquito biology and malaria transmission .
The An . aquasalis colony ( gift from Dr . Paulo Filemon Paolucci Pimenta/Fiocruz ) was maintained in the insectary at the Departamento de Parasitologia , ICB-USP ( São Paulo , Brazil ) at 27±1°C , 75–80% relative humidity and 12 h light∶12 h dark photoperiod . Larvae were kept in 0 . 2% marine salt ( w/v ) , and were fed with powdered fish food ( Tetramin , Blacksburg , VA , USA ) . Adult males and females were kept together in a cage with access to a 10% sucrose solution ( w/v ) ad libitum . Female mosquitoes aged 5–7 days after emerging from the pupal stage were allowed to feed on anaesthetized mice for 30 minutes . Eggs were collected 2–3 days post blood meal and hatched in 0 . 2% marine salt ( w/v ) . Larvae RNA was extracted from a pool of third and fourth instar larvae ( 20 each ) . RNA also was extracted from one pool of twenty 5–7 day old adults females fed with sucrose and two additional samples , each composed of twenty 5–7 day old adult females at 24 h after blood meal . Frozen animals were used for mRNA extraction using magnetic beads covalently bound to oligo ( dT ) tags ( Dynabeads mRNA DIRECT , Invitrogen , Grand Island , NY , USA ) , accordingly to the manufacturer's instructions . Aliquots of the purified mRNA samples were quantified using Quant-iTRiboGreen RNA Reagent ( Invitrogen ) and their integrity was checked in a microfluidics-based platform ( Agilent 2100 Bioanalyzer , Santa Clara , CA , USA ) . Approximately 400 ng of polyA+ RNA from each sample were used as template for sequencing . mRNA was fragmented with zinc chloride , resulting in molecules with a size distribution range from 300 to 800 bases ( assessed by the Bioanalyzer ) , and used as a template for cDNA synthesis . Adaptors were linked to the fragment ends . Beads coated with oligonucleotides complementary to the adaptor sequences were incubated with the cDNA fragments , and a water-in-oil emulsion was produced , followed by emulsion PCR . Washed beads were deposited in picotiter plate wells , and other sequencing reagents were loaded on the 454 GS-Junior sequencer ( Roche , Branford , CT , USA ) . Two hundred sequencing cycles were performed . Base-calling was performed by the 454 GS-Junior data processing software GS Run Processor , version 2 . 7 . The blastn tool ( performed locally from executables obtained at the NCBI FTP site ftp://ftp . ncbi . nih . gov/blast/executables/ ) [26] and CAP3 assembler [27] were used for sequence clustering , by a decreasing word size inclusion strategy as described in detail previously [28] . Coding sequences ( CDS ) were extracted as described before [28] based on matches to public databases or longer open reading frames with a signal peptide indicative of secretion . Contigs are named Anoaqua-XXX or megaclu_asbSigP-XXX reflecting the two modes of data extraction , where XXX represents the number of the full length assembled contig . Reference to specific contigs in this will use an abbreviated notation , contigXXX , instead of the full CDS name . The data was organized in a hyperlinked spreadsheet ( Anaquexcel ) as described in [29] . The blastx [30] tool was used to compare the translated nucleotide sequences to the NR protein database of the NCBI and to the Gene Ontology ( GO ) database [31] . The tool , reverse position-specific BLAST ( rpsblast ) [30] , was used to search for conserved protein domains in the Pfam [32] , SMART [33] , KOG [34] and conserved domains databases ( CDD ) [35] . Predicted protein segments starting with a methionine were submitted to the SignalP server [36] to identify translation products that could be secreted . Glycosylation sites on the proteins were predicted with the program NetOGlyc [37] . Functional annotation of the transcripts was based on all of the comparisons above . Transcripts and their encoded proteins were classified based on function and/or protein families . To compare gene expression between libraries , paired comparisons of the number of reads hitting each contig were calculated by Χ2 tests to detect significant differences between samples when the minimum expected value was larger than 5 and P<0 . 05 . A 2-fold change ( up or down ) was considered of interest when statistically significant . Normalized fold-ratios of the library reads were computed by adjusting the numerator by a factor based on the ratio of the total number of reads in each library , and adding one to the denominator to avoid division by zero . The complete Anaquexcel dataset ( including links ) may be downloaded from http://exon . niaid . nih . gov/transcriptome/An_aquasalis/Anaquexcel . xlsx and searched as an Excel spreadsheet . The raw data were deposited to the Sequence Read Archives ( SRA ) of the National Center for Biotechnology Information ( NCBI ) under bioproject number PRJNA210899 , biosamples SRS455914 ( adults ) and SRS455922 ( larvae ) and runs SRR927455 ( female sucrose ) , SRR927456 ( female blood fed ) and SRR927458 ( L3+L4 larvae ) . CDS representing >90% of known proteins or larger than 250 amino acids were deposited to the Transcriptome Shotgun Annotation ( TSA ) portal of the NCBI and received the accession numbers from GAMD01000001 to GAMD01003464 . To confirm the expression profile generated by the transcriptome sequencing , we validated the expression levels of twelve contigs identified in RNA-seq analysis using a qRT-PCR method . Contigs classified as enhanced or specific for larva ( Anoaqua-397 , Anoaqua-1598 , Anoaqua-4095 , Anoaqua-17360 and Anoaqua-1222 ) , specific for adult ( Anoaqua-436 and Anoaqua-457 ) and blood meal regulated ( megaclu_asbSigP-9948 , Anoaqua-3237 , megaclu_asbSigP-2537 , Anoaqua-5059 and Anoaqua-24500 ) were analyzed . To perform these quantifications , TRIZOL reagent ( Invitrogen ) was used to extract total RNA from 3 independent biological pools of third and fourth instar larvae ( 10 each ) , ten 5–7 day old adults females fed with sucrose and ten 5–7 day old adult females at 24 h after blood meal . For each extraction , total RNA was quantified and 4 . 0 µg were treated with DNAse I ( Invitrogen ) and was reverse transcribed using superscript II ( Invitrogen ) and oligoDT ( Invitrogen ) in a 40 µL final reaction volume . qRT-PCR assay was performed in Mastercycler Realplex 2 thermocycler ( Eppendorf ) with Maxima SYBR green Master Mix ( Thermo Scientific ) . Reactions were performed in a 20 µL final volume containing 2 µL of cDNA template and 0 . 5 µM of each primer ( Table S3 ) . Primers pair amplification efficiency was estimated using original cDNA in seven-fold serial dilutions to generate a standard curve . All primers pair showed efficiency greater than 90% ( Table S3 ) and only one specific peak was observed in the melting curve for each analyzed transcript . Each sample was measured in triplicate and three biological replicates were quantified . Expression levels of An . aquasalis Rp49 constitutive gene [18] , [38] ( Tables S3 and S4 ) was used to normalize variation in total cDNA concentration as an endogenous control . Fold-changes in gene expression were estimated by delta-delta CT method [39] and sample with lower expression levels for each gene was defined as calibrators . Statistical significance was evaluated using Graph Pad Prism5 software . Data was checked in relation to normality using D'Agostino and Pearson omnibus normality test . One way ANOVA followed by Tukey's Multiple Comparison posttest were applied when the data adequate to parametric model . Non-parametric data was analyzed by Kruskal-Wallis test followed by Dunn's Multiple Comparison posttest . Confidence intervals of 95% were defined .
Sequencing returned 1 . 1–1 . 7×105 reads among the samples classes , with averages sizes ranging from 350–420 nucleotides in length and 48–60×106 total bases sequenced ( Table 1 ) . Approximately 7% of these were ribosomal RNAs and therefore were excluded in the subsequent analyses . Assembled and annotated sequences are available in Anaquexcel database at http://exon . niaid . nih . gov/transcriptome/An_aquasalis/Anaquexcel . xlsx ( a condensed table with basic information was also provided at http://exon . niaid . nih . gov/transcriptome/An_aquasalis/Anoaqua-Summarized . xlsx ) . A summary of the assembly compared to the raw reads is shown in Figure S1 . The Anaquexcel database contains 7544 contigs assembled from ≥2 sequences and 1999 singletons . The number of sequences that compose each contig varies widely ( from 2 to 5 , 207 , average of 35 sequences per contig ) ; 43% of the assembled contigs contained 10 or more sequences ( Figure 1 ) . Blastp analyses of the deduced An . aquasalis protein sequences indicated that 70% of them have their closest counterpart in another insect ( 39% An . darlingi; 22% An . gambiae; 3% Ae . aegypti; 2% Cu . quinquefasciatus; and 4% other insects ) ( Figure 2 ) . The percentage coverage was at least 50% for 4 , 671 proteins and 2 , 057 had coverage of at least 90% . Furthermore , 6 , 495 of the blastx searches resulted in best hits with at least 50% identity . These results support the proposed functional annotation of the majority of the deduced An . aquasalis proteins ( Figure 3 ) . The translated products of 4 , 821 contigs or singletons were not assigned a putative function ( classified as unknown ) either because efforts failed to identify similar products in all the searched databases or because similar proteins identified in other organisms have no assigned biological role or activity . Among those with unknown functions , 1 , 434 encode products similar to proteins found in other organisms ( conserved hypothetical proteins ) . The remaining 2 , 387 transcripts classified as having unknown functions either encode novel proteins or alternatively correspond to fragments from mRNAs untranslated regions ( UTRs ) or non-protein encoding RNAs . The predicted presence of amino terminal secretory signal peptide-like sequences supports the conclusion that 15% of the translation products are putatively secreted proteins . These data complement and extend the analyses of EST databases derived from An . gambiae mosquitoes in similar physiological conditions [40]–[44] . To better detail the data generated by RNA-seq , we elaborated 7 sections below to discuss results related to larva and adult enhanced and specific transcripts , the adult female An . aquasalis sialome , blood meal regulated transcripts , immunity-related transcripts , signaling networks in An . aquasalis and conservation of gene regulation between An . aquasalis and An . gambiae . In order to support our data , we validated the expression levels of 12 contigs by qRT-PCR experiments , being 5 contigs chosen from blood meal regulated transcripts , 5 chosen from larva enhanced and specific transcripts and 2 chosen from adult enhanced and specific transcripts ( Supplementary data , Figure S2 , S3 and S4 ) . One hundred twenty one transcripts were represented by at least 10 reads in the larval sample and were not detected in adults . Transcripts encoding hexamerins were among those accumulated most in larvae ( Table S1 and S2 ) . This finding is consistent with previous descriptions of hexamerins , also referred to as larval serum proteins ( LSP ) or insect storage proteins , as abundant proteins at the late larval stages of holometabolous insects [45] , [46] . Hexamerins are synthesized in the larval fat body , secreted into the hemolymph and taken up by fat body shortly before pupation . These proteins degrade during metamorphosis providing a source of amino acids for energy production and adult protein synthesis . Both LSP-1 like ( contigs 1020 , 1221 , 1222 , 1226 , 3211 and 23555 ) and LSP-2 like ( contigs 637 , 2009 and 7437 ) hexamerins were found . Hexamerin-encoding transcripts corresponding to contig 1221 were found highly accumulated in both larvae and adults . Hexamerins expressed in adult mosquitoes have been reported [47] but their function during this developmental stage is unknown . Abundant larval mRNAs encoding ribosomal proteins , translation initiation factors and elongation factors represent the active protein synthesis machinery , consistent with the rapid growth rate during the third and fourth instars of mosquito larvae . Several transcripts encoding cuticle proteins have >50 reads in larvae and were not detected in adults ( contigs 397 , 983 , 1598 , 4095 and 17360 ) . Developmental stage specific-cuticular protein transcripts were reported in An . gambiae [48] , and those and our data support the hypothesis that the stage-specific expressed proteins are components of the different cuticular structures that characterize each metamorphic stage . A total of 424 transcripts represented by at least 10 reads in the adult dataset were not found in larvae . Transcripts enhanced in non-blood fed adults include components of visual sensory organs ( Table S1 and S2 ) . Opsins and arrestins ( contigs 618 , 1471 and 1843 ) are abundant components of the adult insect compound eyes and expressed highly in adult insects . Arrestins are important components for desensitization of G protein-coupled receptor cascades that mediate neurotransmission as well as olfactory and visual sensory reception [49] , [50] . Other components of the olfactory system with enhanced or specific expression in adults include16 identified contigs encoding odorant binding proteins , 10 of which were not represented in the sequenced larval RNA sample . These latter are indicative of new functions in the olfactory system that are specific to adult mosquitoes , such as host finding and breeding site selection . Structural and enzymatic components of the digestive system also were among the adult specific transcripts . An adult-specific peritrophin ( contig 1702 ) component of the peritrophic matrix ( PM ) was identified . A number of functions have been attributed to the PM , including protection against pathogens and abrasion , and compartmentalization of digestion [51] , [52] . The PM may delay digestion in adult mosquitoes [53] and modulate malaria parasites development [54] . Digestive trypsins with an expression pattern similar to those of An . gambiae trypsins 3 , 4 and 7 were detected . These trypsins ( contigs 457 , 1977 and 8935 ) are expressed exclusively in adults and are down-regulated following a blood meal [55] . These trypsin-like enzymes are probably necessary at the initial steps of digestion , but are dispensable later . Alternatively , their functions may be unrelated to digestion and they could associate with other processes regulated by limited proteolysis of precursors . Precursor proteins often require processing at specific sites in order to release their bioactive products [56] , [57] . Contig 383 corresponds to a transcript encoding an adult specific cuticle protein further supporting the hypothesis that the stage-specific cuticular proteins make up the different cuticular structures of larvae and adult insects [48] . Although the An . aquasalis transcriptome presented here was performed with whole body-extracted RNA and the salivary glands are only a small percentage of the total tissue , possibly less than 0 . 1% , ( generally containing 1–3 µg protein ) , several putative salivary proteins of the adult female An . aquasalis were identified , based on their similarity to a database of salivary proteins from blood feeding Nematocera [58] . We considered only transcripts that are significantly up-regulated in the adult libraries as compared to the larval library as indicated in the methods section . Among putative salivary enzymes , contigs 5562 , 17508 and 8234 encode members of the 5′nucleotidase/apyrase families , which are inhibitors of platelet aggregation . These contigs were assembled from 22–62 reads from adult libraries and 0–1 from larvae; similarly , contig 9665 codes for an alkaline phosphatase 66% identical to the Aedes aegypti salivary enzyme and was assembled from 60 reads from adults but zero from larvae . Although many serine peptidases were found in the An . aquasalis transcriptome , those encoded by contigs 2013 and 20556 are most similar to previously-described mosquito salivary enzymes that may play a role in blood feeding , such as destroying fibrin clots . Three peroxiredoxins similar to previously-described salivary proteins are also enriched in the adult libraries ( contigs 4115 , 19752 and 2971 ) . Maltases have been described in mosquito salivary glands and are found in both male and female glands and assist sugar feeding . Contig 10431 with 45 reads has no larval reads and is a candidate for encoding a salivary enzyme . Mosquito sialomes also contain antimicrobial and immune components found in both male and females including pathogen-recognition proteins and classical antimicrobial peptides . The antimicrobial peptide gambicin ( contig 120 , 70 reads from adults , 2 from larvae , 8 . 4 relative fold enrichment in adult library ) , three chitinase-like proteins ( contigs 568 , 3554 and 3923 ) , a protein with an ML domain involved in pathogen lipid recognition ( contig 4200 ) and a GGY family peptide ( contig 10227 ) are possible salivary enriched gene products . The antigen-5 family of proteins is found ubiquitously in animal and plants , and specific family members are expressed in virtually all sialomes studied so far . Contig 436 ( 76 reads from adults and zero from larvae ) is most similar to previously-annotated salivary members of this family . A more specific set of proteins found only in mosquito or Nematoceran sialomes also were discovered . This group includes four members of the Aegyptin family of inhibitors of collagen-induced platelet aggregation ( contigs 2621 , 5929 , 5148 and 1583 ) assembled from 843 adult reads but only three from the larval library . Members of the D7 family , involved in binding of host biogenic amines and inflammatory prostanoids also were assembled solely from adult-derived reads ( contigs 2060 , 950 , 4474 and 19124 ) . The Anopheles-specific antithrombin , anophelin , was matched in contig 801 , assembled from 139 and 5 adult and larval reads , respectively . The uniquely anopheline salivary protein family , SG1/Trio , with unknown function , is represented by contigs 148 , 3403 , 21751 , 2539 and 1630 , assembled from 410 and 21 adult and larval reads , respectively . Further studies demonstrating the salivary specificity of these transcripts are needed . Fifty one transcripts not detected in larvae and sugar-fed mosquito samples were represented by ≥10 reads in the RNA samples of blood-fed females ( Table S1 and S2 ) . Blood Digestion - The up-regulation of enzymes and proteins involved in digestion following a blood meal is well-documented in mosquitoes [55] , [59]–[61] , including An . aquasalis [22] , [23] , [62] . Accordingly , the transcription products of several serine peptidases ( contigs 299 and 305 , AnaqTryp-1 and -2 , respectively; contigs 2013 and 10625 , Anachy1 and Anachy2 respectively ) , aminopeptidases and carboxypeptidases ( contigs 2537 and 31670 ) were identified in our study as increasing in abundance following blood feeding . Mucins , peritrophins ( contigs 5059 and 9948 ) and G12 microvillar proteins ( contig 6859 ) are up-regulated following blood meal in An . aquasalis and other insects . The accumulation of these proteins in the mosquito midgut lumen at a time when malaria parasites are traversing the midgut epithelium has been linked to a possible role in modulation of malaria parasites development [54] , [63]–[65] . Oogenesis - Genes encoding vitellogenins , other yolk components and eggshell-related proteins ( vitellogenins , contigs 407 and 408; vitellogenic cathepsin , contig 1783 ) were identified as having large increases in transcript abundance after a blood meal . Our data confirm previously-described increase in vitellogenic protein expression in blood-fed mosquitoes [66] . Contig 24500 encodes lipophorin , a lipid transporter crucial for oogenesis , up-regulated following blood meal in An . aquasalis as well as other mosquitoes [43] . In addition to their indispensable function in mosquito reproduction , lipophorin and vitellogenin reduce parasite-killing by the antiparasitic factor TEP1 [67] . In the absence of either one , TEP1 binding to the ookinete surface becomes more efficient . Eggshell proteins are expressed abundantly in the ovaries of vitellogenic insects . These proteins and related gene products have been studied extensively in model organisms such as Drosophila melanogaster and Bombyx mori [68] , and more recently in An . gambiae [69] . We identified up-regulated transcripts encoding structural ( vitelline membrane proteins , contigs 296 , 2546 and 2709 ) as well enzymatic components of the mosquito eggshell ( chorion peroxidase , contig 3237 ) . Several other transcripts associated with egg formation and embryo development also were included in the group of blood meal induced transcripts ( for example , Maternal protein exuperantia , contig 1219; Tyrosine-protein kinase receptor torso , contig 19837 ) . Transgenic mosquito strains are being developed to contribute to the control of malaria transmission [70] . The genetics and resulting phenotypes of a female-specific RIDL strategy , previously developed for dengue vector mosquitoes [71] has been adapted to a vector of human malaria , An . stephensi [72] , indicating that the approach is applicable to other anopheline species . Moreover , production of malaria parasites-resistant transgenic mosquitoes has also been achieved in several laboratories [73] . The translation of these new , genetics-based technologies to new world anopheline species is likely feasible and the successful colonization of An . aquasalis places these mosquitoes in the list of target species for transgenesis . Therefore , regulatory sequences that drive transgenes expression in the appropriate developmental stage and organ , and produce an optimum amount of product are required [74] , [75] and the blood meal-activated genes identified in our work provide candidates for isolation and characterization of An . aquasalis functional promoters . One hundred and ninety transcripts decrease significantly in abundance after the blood meal , most of which encode proteins with unknown functions . These likely represent genes expressed in tissues that are not involved in digestive and oogenesis functions . Alternatively , these genes could encode products such as early trypsins ( contig 989 ) necessary at the initial steps of the digestion , but dispensable later [76] , [77] . The mosquito immune system plays a critical role in limiting the spread of malaria and other vector-borne diseases . We identified a series of components of the innate immune system of An . aquasalis , including the antimicrobial peptides defensin ( contigs 30436 , 21901 and 1968 ) , attacin ( contig 20438 ) , cecropin ( contig 120 ) , gambicin ( contig 3403 ) lysozyme-c ( contigs 4061 , 1957 , 14486 , 16360 and 13712 ) and lysozyme-i ( contigs 28407 and 27701 ) . Additionally , members of the Toll pathway ( Toll , contig 15063 , cactus , contig 1543 , dorsal , contig 17521 , Kenny , contig 11522 ) , members of the IMD pathway ( IMD ) ( DIAP2 , contig 6046 , IKKbeta , contig 10934 ) and thioester proteins ( TEPs ) ( contigs 21929 , 29052 and 32589 ) , the three major immune response systems in dipterans insects were identified . Recent research supported differences between the responses of An . aquasalis to P . vivax infection when compared to immune response of An . gambiae to P . falciparum [18] , [19] , [78] . The immunity-related transcripts identified in this study will allow a more detailed study of the immune response of this neotropical vector to both P . vivax and P . falciparum infection . The dynamics of tyrosine phosphorylation-dephosphorylation constitutes a master biochemical regulator of cell biology [79] . It is mediated by a set of three major components: protein tyrosine kinases ( PTKs ) , protein tyrosine phosphatases ( PTPs ) , and Src Homology 2 ( SH2 ) domains . It has been demonstrated previously the role of such a mechanism during tick embryogenesis [80] , mosquito early adult development [81] and parasite infection [82] . The analysis of the An . aquasalis tyrosine phosphorylation-dephosphorylation regulatory enzymes revealed two contigs encoding PTPs ( contigs 10198 and 21444 ) . The first , contig 10198 , encodes the classical non-receptor PTP ( PTPn9 ) and the number of its reads increases in the larval/adult transition but decreases after blood feeding . This is a soluble tyrosine phosphatase that down-regulates prolactin- and EGF-mediated STAT5 activation [83] . STAT5 regulates expression of genes that promote cell survival and proliferation in breast cancer cells . The levels of phosphorylated EGF also are increased upon the suppression of PTPn9 mediated by MicroRNA miR-24 [84] . The Anopheles family of STAT transcription factors ( Ag-STAT ) was reported to be activated by bacterial challenge which then results in their nuclear translocation . This pathway is activated by inhibitors of PTPs [85] . We speculate that when bacterial loads increase following a blood meal , the suppression of PTPn9 expression allows the establishment of a STAT-mediated immunity and the induction of cell growth by incoming nutrients derived from blood digestion . The second transcript , contig 21444 , encodes for a dual-specificity phosphatase ( DUSP ) , a PTP that also increases during the larval to adult transition and is strongly suppressed after blood feeding . These enzymes dephosphorylate both phosphotyrosine and phosphothreonine residues in target proteins and act as deactivators of mitogen-activated PK ( MAPK ) cascades . The complete set of genes in An . gambiae encoding for MAPKs and their activation profiles were described [86] . The level of phosphorylation of MAPKs in Anopheles was demonstrated to be responsive through treatment with insulin , TGF-B1 , and LPS [86] . Curiously , p38 phosphorylation also is affected by hydrogen peroxide treatment , a common inhibitor of PTPs . So it is likely that contig 21444 is the enzyme that ultimately down-regulates the level of MAPK activation after cell treatment with the above-mentioned agents . The induction of MAPK activation following both metabolic and immune challenges after blood feeding coincides with the down-regulation of contig 21444 transcripts . An overall analysis of signaling molecules involved in direct phosphorylation-dephosphorylation circuits whose transcripts are up-regulated during different stages of mosquito development revealed one contig ( 1237 ) encoding a CBL-interacting serine/threonine-protein kinase 10 present in larvae ( Table S2 ) . Calcineurin B-like proteins ( CBLs ) are calcium binding proteins that interact in the presence of calcium with a group of serine/threonine kinases designated as CBL-interacting protein kinases ( CIPKs ) . This signaling network in plants allows the coupling of several different types of stress to a specific response . The most common is the regulation of salt stress [87] . Since An . aquasalis larvae are highly tolerant to salt stress , the CIPKs could be part of a similar response in which overexpression of these genes promotes salt tolerance [88] , [89] . The DUSP ( contig 21444 ) mentioned above and a serine/threonine-protein phosphatase 4 regulatory subunit 1 ( PP4 ) ( contig 20076 ) are among the most down-regulated transcripts following a blood meal ( Table S2 ) . Serine/threonine phosphatases are divided in two main families , PPP and PPM . PPP are divided in five subtypes ( PP1 , PP2A , PP3 , PP5 and PP7 ) . PP4 protein belongs to the PP2A subfamily and like this enzyme is modulated by R regulatory subunits [90] , [91] . PP4 is likely involved in the repair of DNA double-strand breaks but it was recently demonstrated that it also acts as a negative regulator of negative regulator of NF-κB activity in T lymphocytes [92] , [93] . PP4R1 provides the interaction between the IκB kinase ( IKK ) complex and the phosphatase PP4c , thereby dephosphorylating and inactivating the IKK complex . The inactivation of IKK complex blocks NF-kB activation once its inhibitors , called IκBs ( Inhibitor of κB ) , remain bound to the NF-kB complex . Deficiency of PP4R1 caused sustained and increased IKK activity and thus the permanent inhibition of immune responses [93] . Mosquito Rel1 and Rel2 members of the NF-kB transcription factors are activated after a blood meal and their silencing blocks the establishment of an immune response against malaria parasite . This occurs due to the inhibition of the basal expression of the anti-plasmodium genes TEP1 and LRIM , 1 which are involved in the mosquito resistance to malaria parasite [94] . Thus PP4 might represent a long term down regulator of NF-kB activation and its 9-fold down regulation after a blood meal supports the hypothesis that it is required to enhance mosquito refractoriness to eventual pathogen infection . Future molecular analysis of such pathways together with the precise identification of the phosphorylation sites affected may reveal novel targets to overcome disease transmission by anophelines . The changes in transcript abundance between larvae and adults and between sugar fed and blood fed females observed in our study were compared with those previously described for An . gambiae in similar developmental stages . A total of 8 , 355 contigs presented here had a homolog An . gambiae transcript ( best Blast match ) represented in the GeneChip Plasmodium/Anopheles Genome Array [42] , [95] . The pairwise comparisons including all An . aquasalis/An . gambiae homologous pairs demonstrated a lack of conservation of developmental changes in gene expression between the two mosquito species . Approximately half of the genes showed consistent up or down regulation in both species while the remaining showed up regulation in one mosquito and down in the other ( Figure 4 ) . A more stringent analysis was performed by restricting the transcript list to putative 1∶1 orthologous pairs defined by reciprocal blast . Additionally , the list included only those regulated significantly in An . aquasalis , and with ≥3-fold change between two compared samples ( larvae/sugar fed adult females or sugar fed females/blood fed females ) . Applying these more stringent parameters , 75% of the transcripts regulated by blood feeding were consistently up- or down-regulated in both species . The same was not observed for the transcripts accumulated differentially between larvae and adults where only 49% were consistently up- or down-regulated in both species ( Figure 4 ) . This study represents the first effort to sequence the transcriptome of the New World malaria vector , An . aquasalis . We have explored the transcriptomes of larva and adult An . aquasalis , providing valuable information about protein-coding transcripts involved in biological processes relevant to mosquito development , blood feeding , blood digestion , reproduction , and the Plasmodium life cycle . This study , together with other recently published and ongoing efforts to sequence the genomes and transcriptomes of malaria vectors ( vectorbase . org ) [96]–[98] , will provide a needed and more complete understanding of malaria vector biology . Our findings on gene functionalities shed light on the essential physiology of An . aquasalis and thus may help one to develop new control strategies . Moreover , present data may act as shortcuts to investigate genes of other congeneric pathogen-vectors . Data may also be used as taxonomic molecular markers or in future phylogenetic inferences ( of genes or species ) based on exons which are not under differential between-taxa natural selection . Finally , one limitation of the sequencing project reported here is that transcripts present only at developmental stages not included in this study ( embryos , pupae , adult males ) could not be detected . It also is important to be aware that accumulation levels and variations in transcript abundance may not correlate with a similar variation in the amount of the encoded protein . Furthermore , enzyme activity may be subject to regulation by feedback inhibition by the corresponding pathway product , allosteric interactions , reversible covalent modifications or programmed proteolytic cleavage . Differently from other Anophelines , the complete genome sequence of the An . aquasalis was not obtained until now . This fact imposes a limitation to estimate how complete this transcriptome is , and the size of coding genome as well as orthology comparison between related species needs to be adopted with this assumption . Further studies to generate a comprehensive picture of gene expression , protein synthesis and function throughout the mosquito development are needed to uncover biological processes in mosquitoes and to help in the efforts to control malaria transmission .
|
The mosquito Anopheles aquasalis is responsible for transmitting malaria parasites to humans in South America coastal areas . An . aquasalis females transmit Plasmodium vivax and Plasmodium falciparum , the two major malaria etiological agents in these endemic sites . Although the vectorial importance of this mosquito has been demonstrated , molecular aspects of its biology have been poorly explored . In this study , we present the transcriptome of An . aquasalis using 454 sequencing followed by automated bioinformatic analyses . Our study identified and annotated more than 9 , 000 putative proteins based on homology , gene ontology , and/or biochemical pathways , including putative secretory proteins . The comparison of RNAs present in samples extracted from larvae , sugar fed adult females , or blood fed adult females , reveal gene expression regulation during mosquito development . The present dataset provides a useful resource and adds greatly to our understanding of a human malaria vector from developing countries .
|
[
"Abstract",
"Introduction",
"Methods",
"Results/Discussion"
] |
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"genome",
"expression",
"analysis",
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"functional",
"genomics",
"parasitic",
"diseases",
"animals",
"genome",
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"infectious",
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"arthropoda",
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] |
2014
|
Transcriptome Sequencing and Developmental Regulation of Gene Expression in Anopheles aquasalis
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In many animal species the meiosis I spindle in oocytes is anastral and lacks centrosomes . Previous studies of Drosophila oocytes failed to detect the native form of the germline-specific γ-tubulin ( γTub37C ) in meiosis I spindles , and genetic studies have yielded conflicting data regarding the role of γTub37C in the formation of bipolar spindles at meiosis I . Our examination of living and fixed oocytes carrying either a null allele or strong missense mutation in the γtub37C gene demonstrates a role for γTub37C in the positioning of the oocyte nucleus during late prophase , as well as in the formation and maintenance of bipolar spindles in Drosophila oocytes . Prometaphase I spindles in γtub37C mutant oocytes showed wide , non-tapered spindle poles and disrupted positioning . Additionally , chromosomes failed to align properly on the spindle and showed morphological defects . The kinetochores failed to properly co-orient and often lacked proper attachments to the microtubule bundles , suggesting that γTub37C is required to stabilize kinetochore microtubule attachments in anastral spindles . Although spindle bipolarity was sometimes achieved by metaphase I in both γtub37C mutants , the resulting chromosome masses displayed highly disrupted chromosome alignment . Therefore , our data conclusively demonstrate a role for γTub37C in both the formation of the anastral meiosis I spindle and in the proper attachment of kinetochore microtubules . Finally , multispectral imaging demonstrates the presences of native γTub37C along the length of wild-type meiosis I spindles .
In mitosis and male meiosis in animals , the establishment of spindle bipolarity is mediated by centrosomes that act as microtubule organizing centers ( MTOCs ) . These structures serve to organize and focus the growing microtubules to form a bipolar spindle . γ-Tubulin is a primary component of MTOCs and is required for mitotic spindle assembly in many organisms ( reviewed in [1] ) . However , in most animal species , including Drosophila melanogaster , female meiosis is acentrosomal and the mechanisms by which a bipolar spindle is formed during meiosis I have not been fully elucidated . Despite the absence of centrosomes , a role for γ-tubulin in female meiosis has been implicated in many organisms . Schuh and Ellenberg [2] have presented strong evidence that the spindle in mouse oocytes is formed by the action of a large number of γ-tubulin-containing MTOCs that are self-organized from a cytoplasmic microtubule network . These authors propose that the progressive clustering of MTOCs , along with the action of a kinesin-5 motor protein , facilitates the formation of a bipolar spindle . This mechanism of acentrosomal spindle assembly is fully consistent with mammalian studies of γ-tubulin during meiosis I that show localization of γ-tubulin throughout the meiosis I spindle [3] and with work by Burbank et al . [4] demonstrating the existence of the minus ends of the microtubules throughout the meiosis I spindle . These observations lead to a model of spindle assembly in which microtubules are initially nucleated in the region around the chromosomes ( possibly by γ-tubulin ) and then moved poleward . However , despite the evidence in other female meiotic systems for a role of γ-tubulin in meiosis I spindle assembly and function , the role ( if any ) of γ-tubulin in the formation of the meiosis I spindle in Drosophila oocytes has remained highly controversial [3] , [5] . Drosophila has two genes encoding γ-tubulin: γtub37C and γtub23C [6] . γTub23C is expressed in all somatic tissues once embryos become cellularized [6] , [7] . However , in ovaries γTub23C is only expressed in the mitotically dividing germ cells [6] . After meiosis is initiated , γTub37C accumulates rapidly in the oocyte and nurse cells for use during the rapid embryonic cell divisions [6] . In embryos , γTub37C localizes primarily to the centrosomes , but does show some localization over the length of the mitotic spindle and at the midbody [6] , [8] . Although γTub37C is present in the female germline , whether it plays a role in spindle formation during meiosis I has been controversial . Using similar sets of γtub37C mutants , different investigators have obtained highly divergent results with respect to the role of γTub37C in the assembly and function of the first meiotic spindle [8] , [9] . Wilson and Borisy [9] examined the effects of a number of γtub37C mutants ( including a null allele ) on female meiosis I and observed some normal-looking bipolar spindles , leading them to conclude that γTub37C was not essential for either microtubule nucleation or the assembly of the female meiotic spindle . Endow and Hallen [10] reached similar conclusions using a weak loss-of-function allele of γtub37C . However , Tavosanis et al . [8] observed significant defects in both spindle morphology and chromosome arrangement during meiosis I in Drosophila oocytes . Indeed , in the Tavosanis et al . [8] study , ∼80% of oocytes from mothers hemizygous for two null mutations of γtub37C showed abnormal meiotic figures , including chromosomes randomly arranged across the spindle , spindles that were less dense and less uniform then those observed in wild-type oocytes , and spindles that were not focused at the poles [8] . We will show below that these divergent conclusions with respect to the role of γTub37C in spindle assembly were the result of methodological differences in the manner in which oocytes were collected . The data presented here show that γTub37C is indeed required for spindle assembly and function during prometaphase I ( the stage primarily studied by Tavosanis et al . [8] ) and that the spindle defects are often ameliorated by metaphase I ( the stage primarily studied by Wilson and Borisy [9] ) . Even the presence of γTub37C in the meiosis I spindle has been highly contentious . Wilson and Borisy [6] , Tavosanis et al . [8] and Matthies et al . [11] all failed to detect γTub37C on the meiosis I spindle by indirect immunofluorescence microscopy . The inability to detect γTub37C on the meiosis I spindle leant support to the genetic data suggesting that γTub37C was not required for spindle formation in meiosis I . However , Endow and Hallen [10] have recently demonstrated the localization of an overexpressed and green fluorescent protein ( GFP ) -tagged version of γTub37C to the microtubules and poles of the meiosis I spindle . Although this observation shows that γTub37C is capable of localizing to the meiosis I spindle when overexpressed , there are numerous examples of proteins that mislocalize when overexpressed [12] , [13] . Thus , it remained to be determined whether endogenous γTub37C is a native component of the meiosis I spindle . To both resolve these controversies and to explore the role ( s ) of γTub37C in the acentrosomal spindle , we have characterized defects caused by both a novel point mutation in γtub37C , γtub37CP162L , and a null mutation of γtub37C , γtub37C3 , during prometaphase I and metaphase I using both fixed and live oocyte methods . We find that both mutations cause spindle and chromosome defects , demonstrating that γTub37C plays important roles in spindle formation , maintenance , and positioning , as well as chromosome alignment and morphology during prometaphase I . Indeed , mutations in γtub37C lead to loss of kinetochore biorientation and altered kinetochore microtubule attachments . Finally , using multispectral imaging we detect endogenously expressed γTub37C on the microtubules of the meiosis I spindle , suggesting that γTub37C acts within the meiotic spindle to execute these essential functions .
We examined Drosophila oocytes carrying either of two alleles of γtub37C ( Genbank AY070558 . 1 ) to understand its function during meiosis I . The first mutant , γtub37CP162L , was isolated in the course of a screen for EMS-induced recessive female sterile mutants . This mutation resulted in a C to T transition at position 834 that results in a P to L change at amino acid 162 in exon 3 of the γtub37C gene ( data not shown ) . We also examined oocytes carrying a previously characterized early-stop mutation ( γtub37C3 ) , that removes the C-terminal 106 amino acids of the 457 amino acid γTub37C protein , over a deletion that removes the entire γtub37C gene ( γtub37C3/Df ) [9] . The γtub37C3 mutation is a presumed null allele since the truncated protein could not be detected by Western blot [9] . Drosophila females were maintained under two different sets of conditions which yield preparations enriched for either prometaphase I or metaphase I oocytes that were established by Gilliland et al . [14] . For prometaphase I-enriched preparations , mated females were held two to three days post-eclosion on wet yeast paste . Metaphase I-enriched preparations females were collected as virgins and held four to five days on yeast paste in the absence of males . During prometaphase I , in the majority of wild-type oocytes the autosomal bivalents and X chromosomes are aligned together at the midzone of the tapered , bipolar spindle with the achiasmate 4th chromosomes either out on the spindle ( Figure 1A ) or associated with the chiasmate chromosomes ( Table 1 ) [15] . However , upon examination of prometaphase I oocytes from γtub37CP162L and γtub37C3/Df females we observed a wide array of aberrant chromosome configurations ( Figure 1B–1E , Figure S1A–S1B , Table 1 ) . As exemplified in Figure 1B , in γtub37CP162L mutant oocytes the chiasmate chromosomes often failed to form a single chromosome mass at the spindle midzone , but rather were stretched across the length of the spindle . The most notable feature of Figure 1B is the physical separation of the chiasmate autosomes on the spindle ( see arrowheads ) , a phenomenon that we refer to as autosomal slippage . Autosomal slippage that results in a near total disruption of the overlap between the autosomes on the spindle is not commonly observed in wild-type oocytes [15] . Slippage of the autosomes was observed in 42% of γtub37CP162L mutant oocytes and in 23% of γtub37C3/Df mutant oocytes ( Figure 1B , 1D , Table 1 ) . The lower rate of slippage in the γtub37C3/Df mutant oocytes is likely due to the higher rate of severely abnormal chromosome masses in γtub37C3/Df mutant oocytes that are described below ( Table 1 ) . We also observed severe defects in chromosome morphology in mutant prometaphase I oocytes . As exemplified in Figure 1C , the chromosomes from the γtub37C mutant oocytes sometimes appeared rounded and misshapen . Additionally , these chromosome masses did not appear to be properly condensed based on the DAPI staining ( Figure 1C ) . The chromosome morphology defects observed in γtub37C mutant oocytes was rarely observed in wild-type oocytes . As exemplified in Figure 1E and Figure S1A–S1B , the chromosomes of some mutant prometaphase I oocytes failed to align in any direction and displayed both abnormal invaginations and projections away from the main chromosome mass ( Figure 1E , Figure S1A–S1B ) . This lack of obvious alignment was observed in 27% of the γtub37CP162L and 41% of γtub37C3/Df mutant oocytes ( Table 1 ) . Chromosome configurations that appeared more similar to wild-type configurations were observed for a few γtub37CP162L and γtub37C3/Df mutant oocytes ( Figure 1F , Figure S1C–S1D , Table 1 ) . Finally , in 18% of γtub37C3/Df oocytes the chromosomes had separated far enough apart not to be contained within the same set of microtubules ( Table 1 ) . Thus , as was originally noted by Tavasonis et al . [8] , chromosomes fail to align properly on the prometaphase I spindle in γtub37C mutant oocytes . These phenotypes suggest that γTub37C is involved in properly aligning and orienting the chromosomes on the prometaphase I spindle . Additionally , γtub37C mutant oocytes show defects in chromosome morphology , which had not been described previously . Based on these defects , γTub37C appears to function in regulating chromosome alignment and morphology during meiosis I . Our ability to recognize chromosomes on the prometaphase I spindle was greatly enhanced by the use of an antibody recognizing histone H3 phosphorylated on serine 10 ( phH3S10 ) . Histone H3 serine 10 phosphorylation increases during prophase and peaks at metaphase of mitosis and meiosis ( reviewed in [16] ) . The phH3S10 antibody allowed for unambiguous identification of oocyte nuclei that have progressed to at least prometaphase I even in the absence of normal-looking chromosomes ( Figure 1B–1E , Figure S1A–S1B ) . While using the phH3S10 antibody in control oocytes , we discovered that this antibody also robustly highlighted the DNA threads that connect achiasmate chromosomes [15] . These threads are hypothesized to be involved in the mechanism by which achiasmate chromosomes can reassociate during their dynamic prometaphase I movements on the meiotic spindle and the subsequent congression of the chromosomes to the metaphase plate during metaphase I [15] . Analyzing DNA threads using DAPI alone is difficult . Often chromosomes will show evidence that threads are present , such as spurs on the ends of the chromosomes , but the full-length thread will be below the level of detection [15] . The phH3S10 antibody allowed for the visualization of complete threads connecting achiasmate chromosomes , such as the thread projecting from the achiasmate 4th chromosomes in Figure 1A in a wild-type oocyte and from both X and 4th chromosomes in the FM7/X oocytes shown in Figure S2 . Using the phH3S10 antibody to examine thread number and morphology in wild-type oocytes , we primarily observed phH3S10 threads connecting well-separated 4th chromosomes or the absence of threads when achiasmate chromosomes were part of the main chromosome mass . phH3S10-staining threads that failed to project toward another achiasmate chromosome were seen in only 11% of wild-type oocytes , and in these cases only one or two very short threads were observed ( Table 1 ) . However , in many γtub37C mutant oocytes multiple abnormal threads were observed projecting away from the chromosomes ( Figure 1E and Figure S1A–S1B ) . These aberrant thread-like structures frequently co-localized with α-tubulin ( Figure 1E and Figure S1A–S1B ) . Such aberrant threads were observed in 70% of γtub37C3/Df and 42% of γtub37CP162L mutant oocytes ( Figure 1E , Figure S1A–S1B , Table 1 ) indicating that functional γTub37C is required for normal DNA thread morphology . In wild-type oocytes at prometaphase I the meiotic spindle has two tapered poles and the spindle is approximately the width of the autosomes on the spindle midzone at its widest point ( Figure 1A ) . Tapered , bipolar spindles were observed in 89% of wild-type oocytes ( Table 2 ) . However , when spindle structure was examined in prometaphase I γtub37C mutant oocytes , we observed either abnormal spindles or the absence of a recognizable spindle in 70% of γtub37CP162L mutant oocytes and 86% of γtub37C3/Df mutant oocytes ( Table 2 ) . Less than a third of the spindles in γtub37CP162L mutant oocytes , and even fewer in γtub37C3/Df mutant oocytes , could be classified as tapered and bipolar ( Figure 1F , Figure S1C , Table 2 ) , though some of these bipolar spindles showed minor defects in width and microtubule density . Monopolar spindles were observed in 22% of prometaphase I γtub37CP162L mutant oocytes ( Figure 1B , Table 2 ) and 21% of spindles in γtub37C3/Df mutant oocytes ( Table 2 ) . Barrel-like spindles that lacked tapered poles , but still displayed bidirectionality , were observed in 23% of γtub37CP162L mutant oocytes and 25% of γtub37C3/Df oocytes ( Figure 1C–1D , Table 2 ) . The width of the spindle was not constant in these barrel-like spindles . In the image of the γtub37CP162L mutant oocyte that is shown in Figure 1C the spindle is approximately the width of the chromosomes , but in some oocytes the barrel-like spindles were narrower than the chromosomes ( Figure S1D ) . Finally , in 15% of γtub37CP162L and 32% of γtub37C3/Df mutant oocytes the microtubules displayed no clear directionality and simply projected in all directions ( Figure 1E , Figure S1A–S1B , Table 2 ) . Typically this microtubule morphology was also associated with the most abnormal chromosome morphologies . Three γtub37CP162L mutant oocytes failed to show any microtubule association with the chromosomes despite robust antibody penetration . The multitude of aberrant spindle structures suggests that γTub37C is required for both formation of a proper prometaphase I spindle and for maintenance of a tapered bipolar spindle . To investigate the defects in chromosome alignment in γtub37C mutant oocytes , we examined the position of kinetochores using an antibody to Centromere Identifier ( CID ) , the Drosophila CENP-A homolog [17] . In 93% of wild-type oocytes CID localizes to eight foci—four foci on each end of the chromosome mass oriented in opposite directions ( Figure 2A , Table S1 ) . One oocyte appeared to be in spindle assembly before kinetochores had bioriented , and one oocyte had five CID foci on a single side of the chromosome mass , most likely indicating an achiasmate chromosome was caught in the process of moving dynamically on the spindle as observed by Hughes et al . [15] ( Table S1 ) . In γtub37C3/Df mutant oocytes only 17% showed eight CID foci in the wild-type configuration ( Figure 2B , Table S1 ) . In 51% of oocytes CID foci were maloriented with more than four foci pointing towards a single direction or with foci pointing in more than two directions , indicating a frequent failure to properly biorient kinetochores during prometaphase I ( Figure 2C , Table S1 ) . We also observed CID foci that appeared to be oriented towards a single pole ( Figure 2D , Table S1 ) or clustered together in the middle of the chromosome mass suggesting a complete failure of chromosome biorientation ( Figure 2E , Table S1 ) . Finally , in 14% of γtub37C3/Df mutant oocytes more than eight CID foci were observed suggesting precocious sister chromatid separation ( Figure 2F , Table S1 ) . Kinetochores were bioriented in only 38% of γtub37CP162L mutant oocytes . In the majority of γtub37CP162L mutant oocytes the CID foci were maloriented , while more than eight CID foci were detected in the remaining oocytes ( Table S1 ) . These results clearly illustrate that chromosome kinetochores fail to correctly biorient in γtub37C mutant oocytes and in a few cases sister chromatid cohesion may be lost . To better understand the defects in kinetochore orientation , we examined the location of CID foci in comparison to α-tubulin . In wild-type oocytes CID foci appear to interact directly with the kinetochore microtubules as shown in Figure 3A . In γtub37C mutants we observed that many CID foci lacked these direct connections to microtubules , despite careful analysis of all sections of the imaged Z stacks ( Figure 3B–3F ) . While we observed some CID foci that appeared to lack clear interaction with any microtubules ( Figure 3C–3F ) , it appeared that other CID foci may potentially have lateral interactions with microtubules ( Figure 3B , 3E ) . These data suggest that γTub37C plays an important role in allowing microtubules to attach properly to kinetochores . The disruption of microtubule attachments to some kinetochores in γtub37C mutants may result in kinetochores failing to undergo proper biorientation [18] , as well as contributing to the observed spindle and chromosome alignment defects . To better understand the defects observed in the γtub37CP162L mutant oocytes , we examined chromosome movement and spindle dynamics in live oocytes as described in Hughes et al . [15] . Video S1 shows a prometaphase I spindle from a wild-type oocyte . The chromosomes are aligned on the spindle midzone and the spindle is bipolar , consistent with fixed images and previous live-imaging studies [15] . The spindle and chromosomes remain stable for over an hour of observation . We were able to image nuclear envelope breakdown and successful formation of a spindle-like structure in five γtub37CP162L mutant oocytes . In all five the resulting spindles were either barrel-like or failed to maintain a tapered bipolar shape throughout the period of imaging ( Video S2 ) . The ends of the spindles were frequently observed to wave back and forth , and the entire spindle often moved in different directions . The chromosomes also moved quickly into different configurations , at times showing no alignment on the spindle midzone . For an additional 16 γtub37CP162L mutant oocytes the prometaphase I spindle was already present at the start of live-imaging . These spindles displayed many of the same phenotypes that were observed for the spindles that formed after observation of nuclear envelope breakdown . Video S3 shows one such prometaphase I spindle and Figure 4 shows selected still images from this video . At the beginning of Video S3 the DNA is associated with the spindle midzone but the spindle is abnormally wide and barrel-like ( Figure 4A ) . In Figure 4B and 4C the chromosomes stay associated but change in shape , suggesting movement of the chromosomes with respect to one another . The spindle rotates almost 60° clockwise and individual microtubules move rapidly . By 37 . 8 minutes after imaging began the microtubules appear to be shed into the cytoplasm and the bivalents begin to separate on the spindle ( Figure 4D ) . At the end of imaging ( 45 . 7 minutes ) , the chromosomes have split into two distinct masses ( Figure 4E ) . The spindle has turned another 30° clockwise and starts to drift out of the field of view . The dynamic movement of individual microtubules and the shedding of multiple microtubules into the cytoplasm is not commonly observed in wild-type oocytes after the completion of nuclear envelope breakdown [15] . These microtubule movements imply that individual microtubule bundles are not maintained in an organized bipolar spindle in γtub37CP162L mutant oocytes , suggesting that γTub37C is required for stabilizing microtubules relative to one another within the spindle . Spindles in wild-type oocytes typically remain fairly stationary for long periods of imaging . However , spindles in γtub37CP162L mutant oocytes frequently shifted position and orientation quickly which hampered imaging for long time periods . As shown in Video S4 , movement of the ends of the thin and long barrel-like spindle eventually leads to the spindle moving toward the top edge of the focal area . After re-centering the spindle within the focal plane , the spindle and the chromosomes within the spindle continue to move rapidly . The spindle becomes progressively thinner during these movements until it eventually dissolves and the moving chromosomes disperse . These changes in spindle position may be due to the rapid movement of the spindle microtubules or the possible role of γTub37C in maintaining the cortical microtubules that anchor the meiosis I spindle . When examining single time points from live-imaging of γtub37C mutant oocytes most , if not all , of the spindle and chromosome configurations observed in fixed images could be identified . The reason we observe both normal and aberrant spindles in fixed images is that they represent transient intermediates in dynamically changing but structurally flawed spindles . For example , Video S2 shows a spindle that gains and loses tapered bipolarity during the course of imaging . Shortly after nuclear envelope breakdown a tapered bipolar spindle is formed . Around 40 . 3 minutes the chromosomes begin to spread out across the spindle midzone , followed by the spindle forming into a wide barrel . Despite continued reshuffling of the chromosomes across the spindle midzone the spindle regains tapered bipolarity by 50 . 9 minutes . The bottom pole of the spindle then widens and refocuses a second time before a bipolar spindle reforms and is maintained for the remainder of imaging . Due to problems inherent in finding the meiotic spindle , imaging of γtub37C3/Df mutant oocytes proved very difficult . We were unable to acquire any quality recordings of γtub37C3/Df mutant oocytes despite numerous efforts . The results from live-imaging suggest that γTub37C is involved in bipolar spindle assembly and maintenance as well as chromosome and spindle positioning during prometaphase I . Additionally , live-imaging demonstrates that mutants can both lose and regain wild-type spindle and chromosome morphology . This suggests that the array of phenotypes observed in fixed preparations are not due to the spindle progressively deteriorating during the progression to metaphase I arrest but rather represents different stages of the dynamic spindle recovery and dissolution process . Fixed preparations of virgin females four to five days post-eclosion are highly enriched for oocytes arrested at metaphase I ( Table 3 ) [14] . In wild-type oocytes the achiasmate chromosomes congress back to the metaphase plate and the chromosomes form a lemon-shaped configuration for metaphase I arrest at stage 14 ( Figure 5A ) [14] . Chromosomes that had congressed into a lemon-like shape on the metaphase plate were observed in 96% of wild-type oocytes ( Table 3 ) . Despite the abnormal chromosome morphologies observed during prometaphase I , the chromosomes in 62% of γtub37CP162L mutant oocytes successfully congressed to the metaphase plate to form lemon-like DNA structures ( Figure 5B , Table 3 ) . However , 8% of γtub37CP162L mutant oocytes displayed chromosome masses that had split into 2 or more parts ( discussed below ) . In 18% of γtub37CP162L mutant oocytes chromosomes were associated but formed irregular , non-lemon-shaped configurations ( Table 3 ) . Chromosome morphology at metaphase I arrest was also examined in oocytes from virgin γtub37C3/Df mutant females . Metaphase I DNA configurations were observed in 35% of γtub37C3/Df mutant oocytes , demonstrating that chromosomes can successfully congress to the metaphase plate in some oocytes lacking γTub37C ( Figure 5E , Table 3 ) . For γtub37C3/Df mutant oocytes , we noted a propensity for the chromosome mass to split into multiple pieces . This phenotype was observed for 56% of oocytes ( Figure 5C–5D , Table 3 ) . In some oocytes the chromosome masses were near each other ( Figure 5D ) but in many others the chromosome masses were dispersed throughout the cytoplasm ( Figure 5C ) . The phH3S10 antibody facilitated the identification of all the chromosomes , as some chromosomes were not associated with clear spindles ( See Figure 5C for an example ) . Although chromosomes successfully congressed to the metaphase plate in most γtub37CP162L mutant oocytes , they failed to properly orient in preparation for segregation at anaphase I . Using Fluorescent In-Situ Hybridization ( FISH ) probes that recognize the X and 4th chromosomal heterochromatin , we observed in wild-type oocytes that the X and 4th chromosomes were properly positioned on each half of the chromosome mass in 100% of oocytes ( Figure 6A ) , which is consistent with previous studies [14] . This configuration was observed in only 28% of γtub37CP162L mutant oocytes ( Figure 6B ) . In 40% of γtub37CP162L mutant oocytes the 4th chromosomes were associated on the same side of the chromosome mass while the Xs were properly segregated ( Figure 6C ) . In 28% of oocytes the X chromosomes were associated on the same side of the chromosome mass while the 4th chromosomes were segregated correctly ( Figure 6D ) . Finally , in 4% of γtub37CP162L mutant oocytes the X and 4th chromosomes were on opposite ends of the chromosome mass ( Figure 6E ) . These results show that while γTub37C is not involved in chromosome congression , it does play a role in ensuring that the chromosomes are packaged correctly when they do congress . The aberrant threads observed during prometaphase I were mostly absent during metaphase I in γtub37C mutant oocytes ( 15% for γtub37CP162L and 6% for γtub37C3/Df , Table 3 ) . Whether the aberrant threads were resolved or simply packaged into the chromosome mass during congression is unknown . In wild-type oocytes the spindle becomes shorter as the chromosomes congress , which results in a small , bipolar , tapered spindle , similar to the spindle in mouse oocytes [14] , [19] . Bipolar spindles were observed in 87% of wild-type oocytes from four to five day-old virgin wild-type females ( Table 4 ) . While the γtub37CP162L mutation caused spindle abnormalities during prometaphase I in 70% of oocytes ( Table 2 ) , many mutant oocytes were able to recover to form spindles resembling wild-type metaphase I-arrested spindles . Short , tapered , bipolar spindles were observed in 54% of γtub37CP162L and 8% of γtub37C3/Df mutant oocytes ( Table 4 ) . In 35% of γtub37C3/Df oocytes the spindle was reduced to a large microtubule bundle projecting from each end of the chromosome mass ( Table 4 ) . This spindle phenotype was also observed in 21% of γtub37CP162L mutant oocytes ( Figure 5B , Table 4 ) . In 33% of γtub37C3/Df oocytes and 5% of γtub37CP162L mutant oocytes , no apparent microtubules were associated with the chromosomes . This phenotype was often associated with chromosome masses that had split apart . The remaining γtub37CP162L and γtub37C3/Df mutant oocytes displayed spindles that were abnormal in ways similar to those from prometaphase I-enriched preparations , such as barrel-like spindles , monopolar spindles , and microtubule aggregations lacking directionality ( Table 4 ) . These results suggest that γTub37C plays a less pivotal role in spindle maintenance and chromosome positioning during metaphase I . The ability of chromosomes to congress to the metaphase plate in many γtub37C mutant oocytes suggests that chromosome congression during metaphase I is mediated by a mechanism that is different from chromosome positioning during prometaphase I and that does not require a robust prometaphase I spindle . In Drosophila oocytes the spindle pole protein D-TACC is required for maintaining the bipolarity of the meiosis I spindle [20] . Spindles fail to maintain bipolarity in d-tacc mutant oocytes [20] . In Caenorabditis elegans embryos , γ-tubulin plays a role in localizing TACC ( called TAC-1 in C . elegans ) to the spindle poles [21] . Drosophila oocytes with a mutation in the Cdc2 subunit , Cks/Suc1 , display defects in chromosome alignment and spindle morphology as well as mislocalization of D-TACC [22] . These results suggest that D-TACC mislocalization can also cause spindle defects . We examined whether D-TACC localization was disrupted in γtub37CP162L mutant oocytes . Using an antibody to D-TACC , Cullen and Ohkura [20] reported that D-TACC primarily localizes to the poles of the meiosis I spindle with diffuse staining of spindle microtubules in a few oocytes . In contrast , we observed this additional diffuse localization in the majority of wild-type prometaphase I and metaphase I spindles we examined ( Figure 7A and 7B ) . Moderate to strong diffuse spindle and polar staining was observed in 23/36 ( 64% ) prometaphase I and 34/45 ( 76% ) metaphase I wild-type oocytes . Weak diffuse spindle and polar staining was observed in an additional 7 ( 19% ) and 4 ( 9% ) prometaphase I and metaphase I spindles , respectively . In the remaining 6 ( 17% ) prometaphase I and 5 ( 11% ) metaphase I wild-type oocytes D-TACC could either not be detected on the spindle or the background staining was too high to assign a definitive localization pattern . One metaphase I oocyte showed staining primarily to the poles and one showed patchy D-TACC staining ( described below ) . In γtub37CP162L mutant oocytes , D-TACC localization was frequently abnormal . In 38/63 ( 60% ) prometaphase I oocytes , the D-TACC localization was patchy with large , bright foci in some regions and no staining in other regions of the spindle ( Figure 7C ) . In 8/63 ( 13% ) prometaphase I γtub37CP162L mutant oocytes , small , punctate spots of staining were observed along only part of the spindle , often close to the chromosomes ( Figure 7D ) . In 12/63 ( 19% ) of prometaphase I γtub37CP162L mutant oocytes , D-TACC was absent or highly reduced on the spindle ( Figure 7E ) . Only 5/63 ( 8% ) prometaphase I oocytes displayed the diffuse microtubule and polar D-TACC localization observed in wild-type oocytes . For the 49 γtub37CP162L mutant oocytes examined from metaphase I-enriched preparations , 12 ( 24% ) displayed patchy D-TACC localization , 6 ( 12% ) displayed punctuate localization , 29 ( 59% ) showed reduced or absent staining ( Figure 7F ) , and only 2 ( 4% ) displayed D-TACC localization similar to wild-type . Since D-TACC is required for the proper formation of spindle poles , mislocalization of this spindle assembly factor could contribute to the lack of defined spindle poles in γtub37C mutant oocytes . During our attempts to examine living γtub37C mutant oocytes , we noticed that the oocyte nucleus was mislocalized within the cytoplasm with respect to the dorsal appendages . The mislocalization of the oocyte nucleus was not surprising since cortical microtubules are required for nuclear positioning [23] . Additionally , mutations in γ-tubulin ring components ( γTURCs ) have been reported to affect bicoid RNA localization to the anterior cortex of Drosophila oocytes , which is a process dependent on microtubules [24] . In DAPI-only fixed preparations , 98% of wild-type oocyte nuclei were localized to the anterior third of the oocyte near the nurse cells during stages 11 and 12 ( Table S2 ) . In contrast , oocyte nuclei were mislocalized in 22% of γtub37CP162L mutant oocytes during stages 11 and 12 ( Table S2 ) . In γtub37C3/Df mutant oocytes , only 65% of oocyte nuclei were in the anterior third of the oocyte while 26% were located in the middle of the oocyte and 9% of oocyte nuclei were located in the posterior third of the oocyte ( Table S2 ) . In wild-type stage 13 and 14 oocytes , 100% of the oocyte nuclei were located near the dorsal appendages in the anterior one-third of the oocyte ( Table S2 ) . Meanwhile , in γtub37CP162L mutant oocytes , 60% of the oocyte nuclei were located in the anterior portion of the oocyte and 40% were located in the middle or posterior of the oocyte ( Table S2 ) . In γtub37C3/Df mutant oocytes , only 37% of the oocyte nuclei were in the anterior third of the oocyte at stages 13 and 14 ( Table S2 ) . In 43% of γtub37C3/Df mutant oocytes , the nucleus was in the middle third of the oocyte and in 20% the oocyte nucleus was in the posterior portion of the oocyte ( Table S2 ) . Our data suggest that γTub37C plays a role in nuclear positioning , most likely by affecting the cortical microtubules required to stabilize the oocyte nuclei in the anterior part of the oocyte during prophase I and after spindle formation . As noted above , multiple laboratories have failed to detect endogenous γTub37C on the meiosis I spindle [6] , [8] , [11] , [25] . More recently , Endow and Hallen [10] reported that an overexpressed transgenic GFP-tagged γTub37C protein localized to a subset of meiosis I spindles . This experiment had the caveat that the γTub37C-GFP construct was driven not by the genomic γtub37C promoter but rather by the ncd promoter , and endogenous γTub37C was still present in these oocytes . Thus , the γTub37C-GFP construct was likely not present at endogenous levels , raising the possibility that the localization of γTub37C-GFP does not represent the localization of endogenous γTub37C . In order to address these issues and demonstrate the presence of endogenous γTub37C on the meiosis I spindle , we re-examined γTub37C localization using multi-spectral imaging . We examined the localization of endogenous γTub37C using an antibody raised against the C-terminal 17 amino acids of γTub37C ( DrosC ) [26] . Processing and acquisition of the images was optimized for the highest possible detection efficiency of γTub37C staining and the maximum possible rejection of signal from other fluorescent labels and autofluorescence . The method of choice for these applications is multispectral imaging [27] , [28] , [29] . This method provides both high signal-to-noise imaging of multiple fluorophores , as well as residuals-based verification of fitting quality . This allows for detection and removal of even small amounts of bleedthrough or autofluorescence in confocal images . In this case , it permits accurate determination of the presence or absence of γTub37C in the company of α-tubulin , which is in much higher abundance . We observed γTub37C antibody localizing to all the wild-type meiosis I spindles examined while Endow and Hallen [10] failed to detect γTub37C-GFP on the meiosis I spindle in some oocytes ( Figure 8A–8C ) . Rather than staining predominantly at spindle poles with weaker staining on the remaining spindle as is observed in embryos [6] , [30] , a uniform signal was observed over the entire spindle for both prometaphase I ( Figure 8A–8B ) and metaphase I spindles ( Figure 8C ) . This staining is similar to γ–tubulin staining in mitotic and meiotic cells in other systems [31] . Indeed , γTub37C co-localized with the α-tubulin antibody signal , although γTub37C localization was sometimes seen to extend slightly past the α-tubulin signal to form more defined spindle poles ( Figure 8B ) . To ensure that the DrosC anti-γTub37C antibody is specific for γTub37C we examined γtub37C3/Df mutant oocytes . We focused our analysis on those oocytes that formed a chromatin-associated microtubule structure to rule out the possibility of failing to detect γTub37C simply due to lack of a “spindle . ” All γtub37C3/Df mutant oocytes examined failed to show detectable DrosC antibody staining on the spindle ( Figure 8D–8E ) . This result suggests that the DrosC antibody is specific for the C-terminus of γTub37C since the γtub37C3 allele results in a 160 amino-acid C-terminal truncation of the γTub37C protein . These data clearly demonstrate , contrary to previous studies using other γTub37C antibodies , that endogenous γTub37C is present on the meiosis I spindle of Drosophila oocytes . We also examined the localization of γTub37C in γtub37CP162L mutant oocytes and failed to detect γTub37C on the meiosis I spindle in all γtub37CP162L mutant oocytes examined ( Figure 8F ) . Based on results from Western blot analysis , γTub37C does appear to be expressed in γtub37CP162L mutant ovaries ( Figure S3 ) . While the γtub37CP162L mutation appears to be a strong loss-of-function allele , it causes phenotypes that are slightly weaker at prometaphase I and noticeably weaker in terms of the split chromosome mass phenotype at metaphase I compared to the null allele ( γtub37C3 ) . For these reasons , we expected to see only a reduced level of γTub37C on the meiotic spindle . A small amount of γTub37C may be present on the meiosis I spindle in γtub37CP162L mutant oocytes , but it may be below our level of detection . Another possibility is that γTub37C has a function not directly associated with the spindle microtubules to regulate meiosis I , and the γtub37CP162L mutation does not completely abrogate this function .
Our analysis both confirms the conclusion of Tavosanis et al . [8] that γTub37C is required for the organization of the female meiotic spindle and extends that conclusion in several very important ways . First , γtub37C mutant oocytes show strong defects in kinetochore biorientation during prometaphase I . Indeed , in γtub37C mutant oocytes many kinetochores appear to lack typical kinetochore microtubule attachments , suggesting that γTub37C plays an essential role in initiating or maintaining the kinetochore microtubule attachments that are required for properly positioning the chromosomes on the meiotic spindle . Our demonstration of a role for γTub37C in mediating kinetochore microtubule interactions in meiosis is consistent with the observation by others that in γ-tubulin depleted S2 cells subjected to cold-induced microtubule depolymerization , kinetochore-driven microtubule regrowth is delayed [32] . Additionally , in HeLa cells the γ-tubulin Ring Complex is recruited to unattached kinetochores and is required for nucleation of kinetochore microtubules [33] . These studies suggest that γ-tubulin may play a role in kinetochore microtubule nucleation during acentriolar meiosis similar to that in mitosis . Second , although the defects in spindle assembly exhibited by γtub37C mutants are first observed soon after nuclear envelope breakdown and remain severe throughout prometaphase I , normal spindle morphology and chromosome alignment can be lost and regained throughout prometaphase I . Indeed , spindle microtubules in living γtub37CP162L mutant oocytes often appeared to be rapidly moving and microtubule bundles were frequently seen being shed into the cytoplasm . These observations suggest that γTub37C plays an important role in stabilizing existing microtubules within the spindle . The visualization of spindles splitting apart and the subsequent dissociation of the chromosomes from the spindle during prometaphase I by live imaging suggests a mechanism for the split chromosome mass phenotype that is often observed in fixed images . Third , and quite surprisingly , many metaphase I arrested oocytes appear to partially or even fully recover normal spindle and chromosome morphologies , but the chromosomes fail to orient correctly in the metaphase I chromosome mass . The fact that the highly morphologically abnormal prometaphase I spindles in γtub37C mutant oocytes can nonetheless progress to form metaphase I spindles that are relatively normal in appearance may reflect the existence of redundant mechanisms that can facilitate spindle assembly . The existence of such redundant mechanisms of spindle assembly is supported by the observation that when γ-tubulin is decreased in Drosophila mitotic cells , spindle assembly is delayed but a mitotic spindle does eventually form [34] , [35] . Fourth , we also uncovered a previously unidentified role for γTub37C in nuclear positioning . The spindles in living γtub37CP162L mutant oocytes rapidly changed position and orientation within the cytoplasm . Additionally , fixed preparations showed that the oocyte nucleus was sometimes mislocalized to the middle and posterior portion of the oocyte . Cortical microtubules likely play a role in maintaining spindle position within the cytoplasm and a role for γTub37C in regulating these microtubules seems probable [23] . A similar defect in spindle positioning was observed in live oocytes carrying a mutation in ncd , a gene encoding a kinesin motor protein required for bundling the microtubules of the meiotic spindle [11] , [23] . Finally , we observed two previously undescribed defects in chromatin morphology in γtub37C mutant oocytes at prometaphase I . Chromosomes at this stage were often obviously morphologically abnormal and thread-like chromatin projections emanating along microtubules were frequently observed . The observed disruption in chromatin morphology , as well as the potential disruption of the chromatin threads that normally connect achiasmate chromosomes [15] , may well underlie the failure of chromosomes to properly package into the chromosome mass at metaphase I in γtub37CP162L mutant oocytes . Whether the unusual threads and chromosome morphology defects are an indirect effect of the abnormal spindles or whether γTub37C plays a more direct role in mediating chromosome morphology remains to be elucidated . Abnormal movement of the chromosomes due to a lack of kinetochore microtubules could potentially result in homologs moving so far apart that the chromatin threads sever or that the threads from different chromosomes become entangled and pulled out when chromosomes move around aberrantly . While consistent with the observations of Tavosanis et al . [8] and Jang et al . [36] , our conclusion that γTub37C is required for the assembly and function of the meiosis I spindle conflicts with those of Wilson and Borisy [9] and Endow and Hallen [10] . Both groups concluded that γTub37C did not play an essential role in spindle assembly and maintenance during meiosis I . We argue that there are two major causes for this discrepancy . First , Wilson and Borisy [9] and Endow and Hallen [10] based their conclusions on the observation of apparently normal spindles in some mutant oocytes . The formation of even a few bipolar spindles in γtub37C mutant oocytes , even for those homozygous for the weak allele of γtub37C used by Endow and Hallen [10] , led these authors to conclude that γTub37C must not be essential for spindle formation . However , we have shown above that such apparently normal spindles are transient intermediates in a process of highly dysfunctional spindle assembly . Second , while we examined prometaphase I and metaphase I oocytes separately , Wilson and Borisy [9] mainly observed newly laid eggs or activated oocytes , often from virgin females . These preparations would be highly enriched in metaphase I-arrested oocytes and later stages of meiosis . Since many of the spindle defects observed in prometaphase I are partially or fully rectified by metaphase I , the failure to observe spindle defects in a population of oocytes enriched for metaphase I figures is not surprising . Moreover , although metaphase I appeared relatively wild-type in γtub37C mutant oocytes based on DAPI and α-tubulin staining , FISH revealed that chromosome alignment was still defective within the chromosome mass . Based on the data presented above , we suggest a speculative model for the role of γTub37C in meiosis I . We propose that during meiosis I in oocytes , γTub37C may be controlling spindle assembly and maintenance through several different mechanisms . As outlined in Figure 9 , γTub37C localizes to the microtubules in wild-type oocytes . Loss of γTub37C would result in changes in microtubule nucleation and stability . The kinetochore microtubules required for aligning and orienting the kinetochores would be especially affected by these changes . According to our model , these defects would cause spindles to have abnormal morphology , which explains our frequent observation of extremely morphologically abnormal spindles at prometaphase I and metaphase I spindles that appear to be two thick microtubule bundles ( see Figure 5B ) . The mislocalization of the spindle pole component D-TACC in γtub37C mutant oocytes would likely further disrupt spindle morphology . Decreased kinetochore microtubules in γtub37C mutant oocytes would result in a failure of chromosomes to attach to opposite spindle poles and properly biorient , explaining the chromosome alignment and co-orientation defects that we have observed . These defects in spindle assembly would likely also inhibit the ability of other proteins , such as the Ncd kinesin , to bundle microtubules together , which would further impair spindle assembly and chromosome alignment [37] , [38] . As predicted by such a hypothesis , we observed clear second-site noncomplementation between an allele of ncd and a small deficiency uncovering γtub37C in terms of defects in spindle structure , such as non-tapered spindle poles . Such defects were observed in 56% of these doubly heterozygous oocytes . Spindle defects were also observed in 38% of oocytes heterozygous for both γtub37CP162L and ncd mutations ( data not shown ) . Spindle defects were observed in only 11% of ncd heterozygotes , 5% of γtub37CP162L heterozygotes , and 10% of oocytes heterozygous for the deficiency uncovering γtub37C . These results suggest that some of the defects we observe in γtub37C mutant oocytes could be partially mediated by an impairment ( perhaps secondarily ) in the ability of Ncd to properly function on the microtubules . Our model also proposes that the defects created by the loss of γTub37C would also impede the functioning of Nod , a chromokinesin-like protein whose plus-end polymerization function is required for the polar ejection force [39] , [40] , [41] . Indeed , we can imagine that the impairment of Nod function might lead to the defects in chromatin morphology . Such a proposal is consistent with our observation that chromosomes on the most aberrant spindles typically displayed the most severe morphology defects . Abnormal microtubule bundles , impaired attachment of Nod to the chromosomes arms , and a lack of proper kinetochore microtubule attachments would lead to a cascade of defects that affect various aspects of chromosome and spindle structure . In summary , our analysis of staged mutant oocytes and sophisticated microscopy demonstrate that γTub37C is present in the meiosis I spindle of Drosophila oocytes and plays important roles in spindle assembly , maintenance and positioning , as well as in chromosome positioning , orientation and morphology . Multispectral imaging allowed for detection of endogenously expressed γTub37C in the meiosis I spindle . Furthermore , a point mutation that disrupted localization caused severe spindle defects , strongly suggesting that correct localization of γTub37C to the spindle is necessary for this role . γ-Tubulin also appears to play an important role during meiosis in mammalian oocytes and knock down of γ-tubulin by siRNA in mouse oocytes leads to chromosome misalignment and changes in spindle structure [3] , [5] . Our work shows that Drosophila can be used as a model for understanding the function of γ-tubulin in acentriolar bipolar spindle assembly .
Flies were maintained on standard food at 25°C . Wild-type flies were yw; pol . γtub37CP162L flies were yw; γtub37CP162L and the mutation was generated by EMS mutagenesis in the Hawley laboratory ( see below ) . γtub37C3/CyO and w1118; Df ( 2L ) Exel6043 , P{XP-U}Exel6043/CyO ( deficiency uncovering γtub37C ) were obtained from the Bloomington Stock Center and γtub37C3/Df flies were created by crossing the two stocks . The γtub37CP162L mutation was isolated in the course of a screen for EMS-induced recessive female sterile mutants . Although females homozygous for the γtub37CP162L mutation exhibited complete sterility , homozygous males were unaffected . γtub37CP162L homozygous females laid externally wild-type looking eggs that failed to hatch . Embryos from homozygous γtub37CP162L mothers arrested with one or a few spindle-like structures ( data not shown ) . The spindles were often large , wide , and had chromosomes or chromosome fragments distributed across the spindle structure rather then being aligned on the metaphase plate ( data not shown ) . Deficiency mapping using the sterility phenotype narrowed the location of the γtub37CP162L mutation to region 37C-D , which includes γtub37C . By sequencing the γtub37C gene from γtub37CP162L homozygous flies we identified a C to T transition at position 834 that results in a P to L change at amino acid 162 in exon 3 . A complementation test using the γtub37C3 null mutation confirmed the sterility of γtub37CP162L was due to the mutation in γtub37C . For sequencing the γtub37C gene , genomic DNA was prepared from single flies by standard protocol [42] . The following gene primers were used for amplification of the γtub37C gene and for sequencing: 5′ CCTACCTCGTTCAGAGTTATTT , 5′ TAATGACTTCCACTTCCATC , 5′ TGGTCTTTCGAACGCTTGTC , 5′ CCACCGCCGTGCTTGGAGAG , 5′ GACAAGCGTTCGAAAGACCA , and 5′ CTCTCCAAGCACGGCGGTGG . Oocytes were fixed by one of two methods . For all samples except one replicate of the D-TACC prometaphase I experiments , ovaries were dissected from yeasted females in 1× Robb's media ( 55 mM sodium acetate , 8 mM potassium acetate , 20 mM sucrose , 2 mM glucose , 0 . 44 mM MgCl2 , 0 . 1 mM CaCl2 and 20 mM HEPES , pH 7 . 4 ) containing 1% Bovine Serum Albumin ( BSA ) . For prometaphase I-enriched preparations , females were yeasted for 2–3 days with males [14] ( see Table 1 for level of enrichment ) . For metaphase I-enriched preparations , virgin females were yeasted for 4–5 days post-eclosion [14] ( see Table 3 for level of enrichment ) . Ovaries were fixed using a 1× fix buffer ( 100 mM potassium cacodylate , 100 mM sucrose , 40 mM sodium acetate and 10 mM EGTA ) and 8% formaldehyde ( Ted Pella ) for 4–5 minutes . After fixation oocytes were washed three times in PBS plus 0 . 1% triton-X-100 ( PBST ) and vitelline membranes were removed manually using the rough end of two frosted slides . After further washing with PBST , oocytes were blocked with 5% Normal Goat Serum ( NGS ) for at least one hour . Oocytes were incubated overnight in primary antibodies in PBST and 5% NGS at 4°C . After several washes with PBST , oocytes were incubated at room temperature for 4–5 hours or 4°C overnight with secondary antibodies in PBST and 5% NGS . 1 . 0 µg/mL 4′6-diamididino-2-phenylindole ( DAPI ) or 2 . 5 µg/mL Hoechst 34580 ( Invitrogen ) DNA dye was added during the last 10–20 minutes of incubation . Oocytes were washed three times in PBST and then mounted in ProLong Gold ( Invitrogen ) . For fixed preparation using only DAPI , ovaries were fixed under the same conditions as above and washed three times in PBST . Ovaries were teased using gentle pipetting and 2 . 0 µg/mL DAPI was added for 20 minutes . After three washes in PBST oocytes were mounted in ProLong Gold ( Invitrogen ) . To ensure the mislocalization of D-TACC was repeatable under different fixation conditions , the prometaphase I experiments with the anti-D-TACC antibody were replicated using the Buffer A protocol described in McKim et al . [43] . Females were dissected in 1× Robb's media plus 1% BSA and fixed for 10 minutes in 1× Buffer A ( 15 mM PIPES , pH 7 . 4 , 80 mM KCl , 20 mM NaCl , 2 mM EDTA , 0 . 5 mM EGTA ) , 1 mM DTT , 0 . 5 mM spermidine , 0 . 15 mM spermine and 4% paraformaldehyde . Samples were washed three times in a Buffer A solution lacking formaldehyde but containing triton-X-100 . Vitelline membranes were removed manually using the rough end of two frosted slides and washed three more times . Oocytes were blocked for 30 minutes in a Buffer A solution containing 10% NGS . Antibodies were spun for 10 minutes at 4°C in the same solution and then added to oocytes overnight at 4°C . Oocytes were washed in a Buffer A solution containing 0 . 2% BSA and then incubated with secondary antibodies in a Buffer A solution with 10% NGS for 3–5 hours . DAPI was added for 10–15 minutes and samples were washed in a Buffer A solution before mounting in ProLong Gold . The primary antibodies were used at the following concentrations: rat anti-α-tubulin ( AbD Serotec , NC 1∶250 ) , mouse anti-α-tubulin DM1a ( Sigma-Aldrich 1∶100 ) , rabbit anti-γtub37C ( [26] 1∶100 ) , rabbit anti-D-TACC ( [44] 1∶250 ) , rat anti-CID ( [45] 1∶1000 ) and rabbit anti-phosphorylated-histone H3 at serine 10 ( Millipore 1∶500 or 1∶250 ) . Secondary Alexa-488 or Alexa-555 conjugated antibodies ( Molecular Probes ) were used at a dilution of 1∶400 . FISH was performed as described by Xiang and Hawley [46] , with the following modifications . Incubation and hybridization temperature was 30°C and annealing temperature was 91°C . Part of the 359-bp repeat on the X chromosome conjugated to Alexa Fluor 488 and the AATAT repeat primarily on the 4th chromosome conjugated to Cy3 were chosen as probes as previously described [47] , [48] . For fixed experiments not requiring spectral unmixing , the DeltaVision microscopy system was used ( Applied Precision , Issaquah , WA ) . The system is equipped with an Olympus 1×70 inverted microscope and high-resolution CCD camera . The images were deconvolved using the SoftWoRx v . 25 software ( Applied Precision ) . For spectral unmixing experiments images were acquired with an LSM-710 confocal laser scanning microscope ( Carl Zeiss Microimaging , Inc . , Jena , Germany ) using either a 40× 1 . 3 NA Plan-Neofluar or a 40× 1 . 3 NA Plan-Apochromat oil objective . Images were collected using a pinhole of one airy unit and a pixel dwell time of 1 . 6 µs . Each line was averaged eight times in the acquisition . Z stacks were obtained at 0 . 5 µm intervals . All antibody imaging was performed using the spectral detection channel of the microscope with the MBS 488/561 excitation dichroic with 9 . 8 nm resolution and collecting from 494 to 660 nm . All focusing and zooming operations were performed with excitation at 561 nm only ( for visualization of the Alexa Fluor 555 α-tubulin staining ) to avoid photobleaching of the weak Alexa Fluor 488 ( AF488 ) staining of γ-tubulin . All confocal imaging was performed with 488 nm excitation only to maximize the AF488 signal relative to AF555 , as we found that 488 nm excitation was sufficient to observe the α-tubulin-AF555 signal . Reference spectra for secondary antibodies were obtained every day that imaging was performed under identical imaging conditions except with small gain and laser power changes . Variation in reference spectra between the two objectives used from day to day was negligible . The reference spectra were obtained using the secondary antibodies mounted in ProLong Gold ( identical to biological sample mounting ) . Hoechst or DAPI imaging was accomplished with 405 nm excitation and a MBS 405 excitation dichroic and a 415–480 bandwidth collection channel . Hoechst or DAPI imaging was done under identical zoom and Z stack settings to the visible light imaging for each sample . All image processing was accomplished using ImageJ functionality as well as custom-written plugins for binning and spectral unmixing . Spectral images were linear unmixed using standard linear least squares algorithms . Residuals images were generated for each wavelength at each Z position . These residuals were carefully inspected , with close attention paid to channels containing AF488 to ensure that signal from those channels conformed to the expected spectrum for AF488 . In all images , these residuals were completely random in the AF488 channels and showed minimal variation in AF555 channels . Maximum projections of selected slices containing spindles were performed for presentation purposes for both the unmixed images as well as the Hoechst or DAPI images . Maximum projection of the entire collected Z stack was avoided due to non-specific staining above and below the spindle . Live-imaging was performed as describes in Hughes et al . [15] . Briefly: approximately stage 13 oocytes were dissected from ovaries of 2–3 day-old , well-fed adult females and the oocytes were aligned in halocarbon oil 700 ( Sigma ) in a well made on a no . 1 ½ coverslip . Oocytes were injected using standard micro-injection procedures with an approximately 1∶1 ratio of bovine or porcine rhodamine-conjugated tubulin minus glycerol ( Cytoskeleton ) and Quant-iT OliGreen ssDNA Reagent ( Invitrogen ) diluted 0 . 7 fold with water . After injection , oocytes were covered with a piece of YSI membrane . The well slides were placed on a temperature-controlled bionomic controller ( Technology , Inc ) set at 23 . 5°C . Oocytes were imaged using an LSM-510 META confocal microscope ( Zeiss ) with a Plan-APO 40× objective ( 1 . 3 NA ) with a zoom of 2–2 . 5 or an alpha plan-fluar 100× ( 1 . 4 NA ) with a 1 . 5 zoom . Images were acquired using the AIM software v4 . 2 by taking a 10 series Z stack at 1 micron intervals with 20 seconds between acquisitions which resulted in a set of images approximately every 45 seconds . Images were transformed into 2D projections and concatenated into videos using the AIM software v4 . 2 . For each genotype , 50 pairs of ovaries from 2–3 day-old , yeast-fed females were dissected in 1× PBS and homogenized in 50 µL of cold lysis buffer containing 150 mM NaCl , 50 mM Tris ( pH 6 . 8 ) , 2 . 5 mM EDTA , 2 . 5 mM EGTA , 0 . 1% Triton-X , and protease inhibitor cocktail ( Sigma-Aldrich ) . Ovary lysates were cleared by centrifugation twice at 14 , 000 rpm for 15 minutes at 4°C . Equivalent volumes of ovary lysates per genotype were combined with 2× SDS sample buffer , boiled for five minutes , and the solubilized proteins were analyzed by Western blot using standard techniques . The primary antibody used for Western blot was rabbit anti-DrosC γTub37C at a dilution of 1∶500 . Immunoreactivity was detected using an alkaline phosphatase-conjugated rabbit secondary antibody ( Jackson ImmunoResearch ) and the nitroblue tetrazolium and 5-bromo-4-chloro-3-indolyl phosphatase ( NBT/BCIP , Invitrogen ) reagents .
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Proper chromosome segregation during cell division is essential . Missegregation of mitotic chromosomes leads to cell death or cancer , and chromosome missegregation during meiosis leads to miscarriage and birth defects . Cells utilize a bipolar microtubule-based structure known as the meiotic or mitotic spindle to segregate chromosomes . Because proper bipolar spindle formation is critically important for chromosome segregation , cells have many redundant mechanisms to ensure that this structure is properly formed . In most animal cells , centrosomes containing γ-tubulin protein complexes help organize and shape the bipolar spindle . Since meiosis I spindles in oocytes lack centrosomes , the mechanisms by which a meiotic bipolar spindle is assembled are not fully understood . In Drosophila oocytes it was not clear whether γ-tubulin played a role in bipolar spindle assembly or if it was even present on the meiotic spindle . We demonstrate that γ-tubulin plays vital roles in bipolar spindle formation and maintenance , as well as in aligning the chromosomes on the oocyte spindle . Additionally , we show that γ-tubulin is present on the bipolar spindle in Drosophila oocytes . More importantly , we demonstrate that γ-tubulin plays a critical role in the formation of the kinetochore microtubules that are required to properly orient chromosomes on the meiotic spindle .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"meiosis",
"cell",
"division",
"cell",
"biology",
"chromosome",
"biology",
"centromeres",
"biology",
"molecular",
"cell",
"biology"
] |
2011
|
Gamma-Tubulin Is Required for Bipolar Spindle Assembly and for Proper Kinetochore Microtubule Attachments during Prometaphase I in Drosophila Oocytes
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As a para-retrovirus , hepatitis B virus ( HBV ) is an enveloped virus with a double-stranded ( DS ) DNA genome that is replicated by reverse transcription of an RNA intermediate , the pregenomic RNA or pgRNA . HBV assembly begins with the formation of an “immature” nucleocapsid ( NC ) incorporating pgRNA , which is converted via reverse transcription within the maturing NC to the DS DNA genome . Only the mature , DS DNA-containing NCs are enveloped and secreted as virions whereas immature NCs containing RNA or single-stranded ( SS ) DNA are not enveloped . The current model for selective virion morphogenesis postulates that accumulation of DS DNA within the NC induces a “maturation signal” that , in turn , triggers its envelopment and secretion . However , we have found , by careful quantification of viral DNA and NCs in HBV virions secreted in vitro and in vivo , that the vast majority of HBV virions ( over 90% ) contained no DNA at all , indicating that NCs with no genome were enveloped and secreted as empty virions ( i . e . , enveloped NCs with no DNA ) . Furthermore , viral mutants bearing mutations precluding any DNA synthesis secreted exclusively empty virions . Thus , viral DNA synthesis is not required for HBV virion morphogenesis . On the other hand , NCs containing RNA or SS DNA were excluded from virion formation . The secretion of DS DNA-containing as well as empty virions on one hand , and the lack of secretion of virions containing single-stranded ( SS ) DNA or RNA on the other , prompted us to propose an alternative , “Single Strand Blocking” model to explain selective HBV morphogenesis whereby SS nucleic acid within the NC negatively regulates NC envelopment , which is relieved upon second strand DNA synthesis .
The hepatitis B virus ( HBV ) is a global human pathogen that chronically infects hundreds of millions and causes a million fatalities yearly . It belongs to the Hepadnaviridae family , which also includes several related animal viruses such as the duck hepatitis B virus ( DHBV ) [1] . Hepadnaviruses contain a small ( ca 3 kb ) , partially double-stranded ( DS ) DNA genome enclosed within an icosahedral capsid that is formed by 240 ( or 180 in a minority population ) copies of the same viral protein , the core or capsid protein ( HBc ) , and is , in turn , coated with an outer envelope . As pararetroviruses , hepadnaviruses assemble initially as immature nucleocapsids ( NCs ) , packaging an RNA pregenome ( pgRNA ) . These immature NCs undergo a process of maturation first to NCs containing a single-stranded ( SS ) DNA ( still considered immature ) and subsequently to mature NCs containing the DS DNA genome , via reverse transcription of pgRNA inside the maturing NCs . Only the mature NCs are then enveloped by the viral envelope or surface ( HBs ) proteins and secreted extracellularly [2] , [3] . How genome maturation , within NCs , is coupled to envelopment , from without , remains poorly understood . In particular , the exact nature of the viral genome that is ultimately responsible for regulating virion secretion is not yet clear . As SS RNA or DNA is not secreted in virions but DS DNA , in either the major , relaxed circular ( RC ) or minor , double-stranded linear ( DSL ) form , or RNA-DNA hybrid , is [3]–[10] , the prevailing model posits that the accumulation of DS DNA as a result of second strand elongation during reverse transcription triggers a structural change in the maturing NC that , in turn , signals envelopment and secretion [2] , [3] , [11] , [12] . Thus , this so-called maturation signal would emerge on the mature NC only as reverse transcription approaches completion and positively regulate virion secretion . On the other hand , it has been suggested that HBV may secrete virions containing no DNA at all . Two populations of HBV virion particles were found to circulate in the blood of infected patients decades ago , with one having a lighter buoyant density than the other [13]–[15] . These so-called “light” virion particles contained HBV envelope and core proteins but in contrast to the “heavy” particles , displayed no endogenous polymerase activity , which reflects DNA synthesis by the virion reverse transcriptase ( RT ) using the endogenous DNA template . These light particles also appeared empty under electron microscopy ( EM ) and were assumed to be devoid of viral DNA . However , these early reports did not directly determine the levels of viral DNA in the light virions or whether they contained viral RNA or host nucleic acid . A more recent study suggested that the light virion particles might actually contain , instead of the normal capsid protein , an aberrantly processed precore protein [16] . Another recent report found that small amounts of enveloped HBV capsids devoid of viral genome were secreted in transfected cell cultures but those were deemed aberrant [9] , [16] . Thus , it has remained unclear if HBV does secrete DNA-free virions and if so , whether it is part of the normal virion morphogenesis process . In our efforts to further define the nature of the viral genome that underlies selective NC envelopment and virion secretion , we have found that genome packaging or DNA synthesis , per se , was actually not required at all for virion secretion as HBV mutants able to form normal capsids but incapable of viral RNA packaging or DNA synthesis , due to defects in RT , were secreted readily as enveloped , empty ( nucleic acid-free ) virions . Furthermore , quantitative analyses of virion-associated DNA and capsids revealed that the vast majority of virions secreted by the wild type ( WT ) HBV in cell culture as well as in infected chimpanzees contained no viral DNA . We propose a new model to explain the selective HBV virion morphogenesis that can reconcile the seemingly contradictory observations that virion formation selects stringently for DS , and against SS , nucleic acid genome and yet empty HBV virions devoid of any nucleic acid are secreted .
We obtained the first hint that HBV virion secretion may not necessarily require viral DNA synthesis during our recent studies on the anti-HBV effect of the cellular antiviral protein , Apobec3G ( A3G ) [17] . As shown in Figure 1 and reported earlier [18] , over-expression of A3G led to a dramatic reduction of HBV DNA associated with non-enveloped ( naked ) NCs as well as virions secreted into the culture medium of transfected human hepatoma ( HepG-2 ) cells , which were resolved by native agarose gel electrophoresis [19] . However , we found , unexpectedly , that the amount of enveloped capsids secreted into the culture medium was not decreased at all by A3G . The identity of the virion signal was further verified by its association with , and dependence on , the viral envelope proteins ( Figures 1 & 2 ) and its authentic buoyant density ( see below ) . Naked capsids , which are routinely released into cell culture medium via an unknown mechanism unrelated to virion secretion and are of uncertain significance [20] , were not affected by A3G though their DNA content was reduced as reported before [18] . HBsAg particles , which contain only the viral envelope proteins but no capsids nor genome [1] , were not affected by A3G either ( Figure 1 ) . As HBsAg particles are normally released in large excess ( 100-fold or more ) over virions [1] , the surface protein signals detected in Figures 1 and 2 were virtually all from the HBsAg but the virion and HBsAg comigrated on the gel . This was also evidenced by the surface protein signal migrating at the virion position from a mutant defective in core and polymerase protein expression ( C-P- ) and thus could only secrete HBsAg particles but no virions ( neither empty nor DNA-filled ) at all ( Figure 2B ) . These results indicated that viral DNA synthesis might not be required for NC envelopment and virion formation , in apparent contradiction to the prevailing maturation signal hypothesis . To further examine the relationship between genome content and virion secretion in HBV , we determined the virion secretion capacity of two mutants , Pol- and Y63D; Pol- is defective in polymerase expression and unable to package viral RNA [21] whereas Y63D is defective in DNA synthesis but fully functional in RNA packaging [22] ( Figure 2 ) . Native agarose gel analyses of HBV particles concentrated from the culture medium of transiently transfected HepG-2 cells showed that as reported before [5] , pgRNA was not found in enveloped virions though readily detectable in naked capsids of either WT or the Y63D mutant ( Figure 2 ) . As anticipated , no viral RNA was detected in either NC or virion particles from the Pol- mutant , and only the WT , but not the Y63D or Pol- mutant , contained viral DNA in virions or naked capsids . However , capsid protein signal was clearly detected not only with the naked capsids but also with the virion particles from the Y63D and Pol- mutants , at levels at least as high as those of the WT . As a negative control , the Env- mutant did not show any capsid or DNA signal migrating at the virion position . The co-migration of the putative Y63D and Pol- virions with the WT virions as well as with the envelope signals supported their identification as authentic virions , which was further verified by density gradient centrifugation ( Figure 3; Figure S1 ) . Native agarose gel electrophoresis analyses of viral particles fractionated on the CsCl gradient confirmed that enveloped capsids , i . e . , virions , judged both by their density profile and mobility on the agarose gel , were indeed secreted by the Y63D and Pol- mutants even though they contained no viral DNA . Consistent with the notion that these enveloped capsids contained no nucleic acid ( viral or cellular; also see below ) at all , the empty virions ( marked as peak #2 ) produced by the Y63D or Pol- mutant had a slightly lower density ( as predicted by their lack of nucleic acid ) than the authentic , DNA-containing virions secreted by the WT HBV ( peak #1 ) . Furthermore , the capsid signal peak ( marked as #2 ) from the WT virion fractions also had a lower density than the DNA peak . This later result suggested that most of the WT HBV virions might also be empty such that the bulk virions ( empty ) had a density lighter than the minor amounts of DNA-containing virions . Although small amounts of naked NCs migrated near the position of the virions after the CsCl gradient fractionation step , presumably in aggregated forms as a result of exposure to the high salt concentration of the gradient as suggested earlier [9] , the naked NCs were clearly separated from the virions by the CsCl density gradient fractionation such that there was no naked NC contamination in the virion fractions ( Figure 3 ) . In addition , consistent with numerous previous reports ( see Introduction ) , immature SS DNA was found in naked NCs but not in the virion fractions ( Figure S2; and also Figure 4 below ) , again indicating clear separation of virions from naked NCs . On the other hand , no naked capsid signal was detected near the virion position on the agarose gel without the CsCl gradient centrifugation step as indicated by the absence of any capsid protein or viral DNA signal at the virion position from an HBV mutant defective in envelope protein expression ( Figure 2A , lanes 4 , 12; Figure 2B , lane 5 ) . Furthermore , abolishing viral envelope protein expression eliminated any capsid or viral DNA signal in the virion fractions on the CsCl gradient ( Figure S3 ) , again indicating the secretion of the empty virions , just like the DNA-containing virions , was absolutely dependent on the viral envelope proteins and no naked capsids contaminated the virion fractions following the density gradient fractionation . To follow up on the above suggestion , we decided to quantify the amount of HBV DNA and the capsid protein signals within the virion fractions secreted by WT HBV transfected HepG-2 cells . This revealed that most secreted HBV virions ( from 92 . 5% in the lighter fraction to 67% in the heavier fraction ) from transfected cells ( Figure 4 , lanes 15–18 ) were indeed devoid of any viral DNA . These estimates were based on quantifications of the levels of virion-associated capsids ( based on 240 copies of core protein per capsid ) vs . the virion DNA . Although it is theoretically possible that the DNA-free virion capsids may have reacted differently than the DNA-containing virion capsids with the anti-HBc antibody used for the western blotting , this was made unlikely by the fact that the relative signals of the capsid proteins remained constant across the lighter ( virtually DNA-free ) and heavier ( with more DNA-containing virions ) fractions ( Figure S4 ) , whether the capsid protein levels were estimated as native particles resolved on an agarose gel or as denatured subunits resolved by sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) and detected by either a polyclonal antibody or a monoclonal directed at a linear epitope at the N-terminus of the capsid protein [23] . As reported earlier [13] , [14] using HBV positive human sera , two kinds of WT HBV virions were observed by EM after negative staining , with either a filled or empty looking inner capsid under EM ( Figure 5A ) . However , for most virions , the stain did not penetrate the envelope thus making it difficult to discern if the capsids within the virions were filled or empty . To visualize better the capsids contained in the virions , the virion envelope was removed by detergent lysis [12] , [14] and the released capsids were observed under EM . Upon removal of the virion envelope by the detergent treatment , we found that the majority of the released capsids ( ca 70% ) had an empty appearance consistent with their containing no nucleic acid while the remaining showed a filled appearance presumably containing the DS DNA ( Figure 5B ) . We also released the capsids from the Pol- mutant virions using the same method and found that all released Pol- capsids showed the empty appearance , consistent with their lower density and containing no nucleic acid ( Figure 5C ) . As higher concentrations of capsids were obtainable from intracellular sources , more detailed analyses of the empty and filled capsids , including 3-D image reconstruction , were carried out on those capsids and are described below ( Figures 6 & 7; Figures S8 & S9 ) . Intrigued by these results , we decided to quantify the levels of viral DNA and capsid protein in the sera of HBV infected chimpanzees [24] . As shown in Figure 4 ( lanes 1–7 ) and Figure S5 , the vast majority ( 97 . 8–99 . 7% ) of HBV virions found in the sera from all four infected chimpanzees harvested at two different times post-infection contained no viral DNA . HBV virions in one of the chimpanzee sera were also fractionated by CsCl density gradient centrifugation . As with the virions released by transfected cells in culture , the core protein peak ( Figure S5B , lane 2 ) in the serum virions had a lighter density than the DNA peak ( Figure S5B , lane 4 ) , indicating again the majority of the WT virions in the serum were DNA-free and had a lighter density . The core protein signal in the DNA peak fraction ( lane 4 ) was below the limit of detection since our core protein Western blotting had a sensitivity of ca 1 ng/lane while the Southern blotting had a sensitivity of less than 2 . 5 pg/lane ( Figure 4 ) . The virion DNA secreted by the infected chimpanzees in vivo migrated mostly just below the 3 kb DS DNA standard ( Figure 4 , bottom image , lanes 1–7 ) , typical for the incompletely DS HBV virion RC DNA isolated from infected patients [10] , [25] . For reasons yet to be understood , HBV virion DNA secreted by the transfected hepatoma cells in vitro appeared more fully DS [26] , migrating above the 3 kb linear DNA ( Figure 4 , bottom , lanes 15–18 ) . As the HBsAg particles are present in large excess ( 100–1 , 000 fold or more ) over virions [1] and we detected also a large excess of empty virions over DNA-containing ones ( with total virion core proteins reaching 1–3 . 75 µg/ml or 1 . 6–4 . 4×1011 virions/ml ) , it was of interest to estimate the levels of HBsAg in the sera from the infected chimpanzees . To this end , purified recombinant HBsAg ( eEnzyme ) was used a quantitative standard for western blotting . Due to the undefined oligomeric states ( a mixture of monomers , dimers and higher order oligomers; eEnzyme ) of the HBsAg standard , it was resolved by SDS-PAGE along with different dilutions of two serum samples from the same chimpanzee , one before and one after HBV infection . The surface proteins were detected using a rabbit polyclonal antibody that can recognize the denatured HBsAg ( Figure S6A ) and the HBsAg concentration in the HBV-positive serum was estimated based on comparison with the standard . The HBsAg concentrations in the other chimpanzees were then estimated by comparison to this serum after they were resolved together on an agarose gel ( Figure S6B ) . Our estimates of the serum HBsAg levels in all four infected chimpanzees ranged from 124–1 , 899 µg/ml , or ca 3×1013–4 . 4×1014/ml assuming 100 copies of the small surface protein per HBsAg sphere [1] , [27] . Thus , the HBsAg levels were ca 167- to 1 , 688-fold above the virions ( both DNA-containing and empty ) . These estimates were in good agreement with earlier estimates of HBsAg concentrations in the serum of HBV infected human patients of 1013/ml or 40 µg/ml [28] to more recent quantifications of up to 1015 or 4 mg/ml [29]–[31] . The secretion of HBV virions containing no nucleic acid at all implied that empty HBV capsids , containing no viral or cellular nucleic acid , were assembled in host cells . In support of this , the naked capsids harvested from the WT and Y63D transfections peaked at a lower density ( Figure 3 & Figure S1 , peak #4; ) than the viral RNA or DNA signals ( i . e . , filled NCs ) that peaked at a higher density ( peak #3 ) . Although this capsid protein peak contained some viral RNA or DNA , it likely represented the overlap from the heavier , nucleic acid-containing NCs; earlier ( still lighter ) capsid fractions contained little to no viral RNA or DNA . The naked capsids of the polymerase-null mutant , being devoid of any viral RNA or DNA ( Figure 2 ) or cellular nucleic acid ( see Figure 6 below ) , peaked at the same light density as the capsid protein peak ( #4 ) from the WT and the Y63D mutant , lighter than the RNA or DNA-containing NCs peak ( #3 ) . To further verify that empty HBV capsids , containing no viral or cellular nucleic acid , were indeed made in cells , we purified intracellular capsids from transfected HepG-2 cells by sucrose gradient centrifugation , resolved them on agarose gels along with HBV capsids purified from E . Coli that are known to be filled with nonspecific RNA [32]–[34] , and detected the capsids ( the protein signal ) or their associated nucleic acid with the highly sensitive general protein ( SYPRO Ruby ) or nucleic acid ( SYBR Gold ) stain . The detection sensitivity of the capsid-associated RNA ( approximately 8 ng capsids , or 1 . 6 ng RNA assuming each capsid packages 3 kb RNA; Figure 6 ) was about 5-fold less than that of purified RNA ( approximately 0 . 3 ng RNA; Figure S7 ) , presumably due to the sequestration of RNA inside the capsids . Therefore , instead of attempting to quantify the absolute amounts of nucleic acid associated with the HepG-2 cell-derived capsids , we used the E . Coli-derived capsids , not purified RNA or DNA , for relative comparison . The HBV Pol- capsids did not show any detectable nucleic acid signal ( Figure 6 , top , lane 10 ) , as anticipated . Given that 244 ng of Pol- capsids ( Figure 6 , bottom , lane 10 ) , estimated based on comparison to the E . Coli-derived capsid standard ( lanes 2–9 ) , was loaded and the RNA associated with 7 . 8 ng bacterially derived capsids was detectable , the amount of RNA in the pol- capsids , if any , was thus less than 3% of that in the E . Coli-derived capsids . The capsids harvested from WT HBV transfected HepG-2 cells ( Figure 6 , lane 1 ) were found to contain detectable but much less ( ca 35% ) nucleic acid when compared to the E . Coli-derived capsids . Thus , the amount of WT HBV capsids purified from HepG-2 cells loaded was 707 ng ( Figure 6 , bottom , lane 1 ) but its nucleic acid content was equivalent only to 269 ng E . Coli-derived capsid ( Figure 6 , top , lane 1 ) . Titration of the WT HBV capsids harvested from HepG-2 cells showed that the nucleic acid content could be detected by the staining method when ca 100 ng of capsid was loaded per lane ( Figure S8 ) . As the nucleic acid signal detected in the WT HBV NCs was a mixture of capsid-associated viral DNA as well as RNA , and the SYBR Gold stain had a higher ( by ca 7 fold ) detection sensitivity for DNA than RNA ( Figure S7 ) , the amount of nucleic acid associated with the WT capsids from HepG-2 cells , relative to that associated with the bacterially-derived capsids that contain only RNA , were likely overestimated . Thus , these results clearly indicated that the majority of WT HBV capsids assembled in hepatoma cells were also empty . The purified capsids were further subjected to negative staining and EM ( Figure 7 ) , which showed that most ( ca 80–90% ) ( Table S1 ) HBV capsids harvested from the WT HBV transfected HepG-2 cells , and virtually all capsids from the HBV Pol- transfected cells , displayed the thin-walled or empty appearance characteristic of nucleic acid-free capsids such as the empty , C-terminally truncated HBV capsids purified from E . Coli [15] , [35] , [36] . The rest ( ca 10–20% ) of the WT HBV capsids from HepG-2 cells showed the thick-walled or filled appearance consistent with their containing RNA or DNA , similar to the full-length capsids derived from E . Coli that are known to contain non-specific RNA . To obtain further information about the interior contents of the various HBV capsid populations , 3-D capsid maps were reconstructed from selected negatively stained particles from the EM images ( Table S1 ) . To check how the 3-D reconstruction program translated the EM images into a 3-D model , the WT HBV capsids from the HepG-2 cells were separated into two groups , those that appeared to be empty and those that appeared to be filled , and reconstructions were performed separately for each group . The empty group resulted in a map with a hollow core , and the filled group depicted electron densities inside of the capsid ( Figure S9 ) . In comparison , the 3-D map of the HBV Pol- capsids also had a hollow core , in agreement with the results above showing that these capsids contained no detectable nucleic acid ( Figures 2 & 6; Figure S8 ) . Therefore , the results indicated that a 3-D reconstruction from negatively stained EM images could help distinguish between empty and filled viral particles . Capsids assembled from the full-length and C-terminally truncated capsid proteins expressed in E . Coli were also examined . The 3-D map of the full-length capsids from bacteria showed electron densities protruding inwards from the shell again consistent with the fact they contained non-specific RNA . In contrast , the C-terminally truncated capsids that appeared empty in the EM image also showed a hollow core in the corresponding 3-D model . These results agreed well with previously published cryo-EM structures that revealed that the amount of the RNA content in the E . Coli-derived HBV capsids depends on the length of the C-terminal nucleic acid binding domain of the core proteins [37] . A previous report suggested that an aberrant precore protein , lacking the C-terminal nucleic acid binding region of the core protein but retains the precore region preceding the core protein including the signal peptide , could assemble into abnormal capsids that were enveloped and secreted [16] . The constructs we used here to express the HBV core protein lacks the coding sequence for part of the precore region including the precore initiation codon and thus is not expected to produce any precore protein [38] . Also , the core protein in the purified capsids or virions migrated on SDS-PAGE as the full-length core protein standard ( Figure 4; Figures S4 & S5 ) , suggesting that no truncation or degradation occurred . To confirm directly that the HBV core protein made in transfected cells indeed contained the C-terminal region , we employed an antibody specific for the last 14 residues of the core protein [39] . As anticipated , this antibody failed to detect a C-terminally truncated core protein but could detect the core protein in both the WT and Pol- capsids; an antibody specific for the N-terminal sequence of the core protein detected the full-length as well as the truncated core proteins ( Figure S10 ) . These results thus verified that the HBV core protein expressed in our system was full-length and no aberrantly processed core or precore proteins were made .
Hepadnaviruses select only mature , DS DNA-containing , but not immature , RNA- or SS DNA-containing NCs for envelopment and extracellular secretion as virions . The molecular mechanism underlying this selective virion morphogenesis , which is a defining characteristic of all hepadnaviruses as pararetroviruses , remains to be elucidated . We have provided here multiple , complementary lines of evidence to demonstrate the secretion of empty HBV virions with no viral or cellular nucleic acid , i . e . , enveloped capsids with no RNA or DNA . First , the secretion of empty virions , like the DNA-containing ones , depended on the expression of the viral envelope proteins as viral mutants defective in envelope expression did not secrete any virions , empty or DNA-filled . Second , the co-migration of the capsid and envelope proteins on the agarose gel and their co-fractionation on the density gradient indicated the capsids detected at the virion position were enveloped , as confirmed by EM observation . Third , the buoyant density of the empty virions were very close to the DNA-containing virions and much lower than the naked NCs , as expected for enveloped capsids , Fourth , the absence of viral DNA or RNA in the empty virions was shown by the secretion of virions by the Pol- mutant that is incapable of packaging viral RNA or synthesizing DNA and by the Y63D mutant that can package pgRNA but can't synthesize any DNA , and by the lack of detection of any viral RNA or DNA by Southern blotting in these virions . Fifth , the absence of any nucleic acid , viral or cellular , in the empty virions was indicated by their slightly but reproducibly lower density than the DNA-containing virions and the EM observation of virions containing empty capsids as well as empty ( mostly for the WT and entirely for the Pol- mutant ) capsids released from the virions . Sixth , the naked ( non-enveloped ) capsids from the WT were shown to be mostly , and those from the Pol- HBV entirely , empty , with no viral or cellular nucleic acid . This was demonstrated by the absence of viral RNA or DNA by Southern blot analyses , by EM observation and 3-D image reconstruction , by their expected lighter density on the CsCl gradient , and by direct measurement of total nucleic acid in the capsids using sensitive nucleic acid staining methods . Thus , in the case of the Pol- virions , it would have been very difficult to imagine that the secreted virions would have contained any RNA or DNA since the intracellular Pol- capsids , which were the substrates for envelopment and virion formation , contained no detectable nucleic acid . Also , given that ( a ) most of the WT intracellular capsids contained no viral or cellular nucleic acid and the remaining WT capsids contained either viral RNA or DNA , ( b ) the intracellular capsids were the precursors to the enveloped virions , and ( c ) viral RNA-containing capsids were excluded from envelopment , it would have been difficult too to explain how the DNA-negative WT virions would have contained any nucleic acid . The secretion of enveloped capsids , without any nucleic acid inside , is incompatible with the prevailing model of hepadnavirus morphogenesis , whereby virion formation requires DS DNA synthesis , i . e . , NC maturation . Instead , we propose here that the paradox , i . e . , the seemingly stringent selection of DS DNA over RNA or SS DNA in virion formation on one hand and yet secretion of empty HBV virions devoid of any nucleic acid on the other , can be resolved by invoking a new model , which we call “single strand ( SS ) blocking . ” In this , negative signal model , the presence of SS DNA or pgRNA in the immature NCs actively prevents their envelopment by triggering a signal that negatively regulates NC envelopment ( Figure 8A ) . The requirement of DS DNA for efficient virion secretion would NOT be due to a need to accumulate a threshold amount of total nucleic acid with the resulting increase in internal pressure , negative charge , genome rigidity , or some other unknown effect , which in turn would trigger a structural change ( i . e . , the maturation signal ) on the maturing NC leading to envelopment and virion secretion , as envisioned by the classical maturation signal model ( Figure 8B ) [2] , [3] , [11] , [12] . Rather , the apparent requirement for DS DNA synthesis in virion secretion reflects the need to remove the pgRNA or SS DNA , the trigger of the blocking signal , so as to relieve its inhibition on NC envelopment . Those capsids that do not package pgRNA ( nor any other RNA , see below ) would not display this negative signal and are thus competent for envelopment and secretion ( as empty virions ) ( Figure 8A ) . Although the secretion of empty virions could be explained , in theory , by invoking a second envelopment signal for the empty capsids , independent from that emerging on the DS DNA-containing capsids , the simplest explanation is that the empty capsids and the DS DNA-containing capsids share the same envelopment signal . The SS blocking model also predicts that decreasing the DS DNA ( thus the total amount of nucleic acid inside the NC ) may not block virion secretion . In support of this model , internally deleted short ( ca 2 kb or shorter ) DNAs , derived from packaging and reverse transcription of spliced HBV pgRNA have been detected commonly in the blood of HBV-infected patients [40] , [41] and in transfected cell culture medium [42]–[44] . In contrast , the secretion of these shortened genomes in virions would be more difficult to explain with the classical maturation signal model . The secretion of empty HBV virions requires assembly of empty capsids inside cells ( Figure 8A ) . Three lines of evidence support the existence of these empty capsids: ( 1 ) their lower buoyant density than the nucleic acid-containing NCs , ( 2 ) lack of detection of nucleic acid with highly sensitive , general nucleic acid stain , and ( 3 ) their empty appearance under EM observation and 3-D capsid image reconstruction . HBV capsids assembled in insect cells did not package any cellular RNA either [45] , [46] and presumably empty HBV capsids ( so-called light cores ) were also detected in the liver of HBV infected patients [14] , [15] , [47] . Thus , in contrast to capsids assembled in bacteria which package non-specific RNA [32]–[34] , HBV capsids assembled in insect and mammalian cells do not package non-specific RNAs and are empty if they fail to package the viral pgRNA . The underlying mechanism for this difference remains to be elucidated but may be at least partly due to capsid phosphorylation in mammalian and insect cells that decreases its RNA binding activity [46] , [48] , [49] . We previously reported that the capsid protein associated with the DHBV virions , as well as intracellular mature NCs containing DS DNA , is unphosphorylated , in contrast to immature NCs that consist of heterogeneously phosphorylated core protein [11] . If DHBV , like HBV , secretes empty virions , it would suggest that not all unphosphorylated DHBV capsids are mature and some unphosphorylated DHBV capsids are in fact empty . If all unphosphorylated DHBV capsids do contain DS DNA , it would suggest that DHBV does not secrete empty virions , in contrast to HBV . For the single strand blocking model to accommodate the absence of empty DHBV virions , we suggest that DHBV capsids may package viral pgRNA more efficiently or could package non-specific RNA in host cells , unlike HBV . Consequently , empty DHBV capsids may not be assembled in host cells and thus unavailable for virion formation . Studies are currently underway to determine the relationship between genome content and virion secretion in DHBV . How SS DNA or RNA triggers the proposed blocking signal is not yet understood . One NC maturation model suggests that the mature NC , by containing DS DNA and thus twice the amount of negative charges as the immature NC containing either the SS DNA or pgRNA , is destabilized as a result of the charge imbalance , triggering an NC conformational change that may be part of the maturation/envelopment signal [50] , [51] . In the context of the single strand blocking model , it can be suggested that NC destabilization due to the charge imbalance can not only result from DS DNA accumulation within the maturing NC but also from the lack of any nucleic acid within the empty capsids . Indeed , empty HBV capsids were shown to be unstable especially under low salt conditions [51] . This structural instability may preclude the generation of the blocking signal , thus allowing the secretion of both empty and DS DNA-containing virions . The secretion of SS DNA-containing virions by certain HBV core mutants ( the so-called immature secretion mutants ) [9] , [52] , [53] and an avian hepadnavirus ( the snowgoose HBV or SGHBV ) [54] indicates that the blocking signal may be absent from , or sequestered by , these immature NCs . In addition , low level secretion of SS DNA-containing virions by the WT HBV has also been observed [52] , [53] although we saw little , if any , SS DNA in WT HBV virions in our experiments here . It is possible that the stringency with which the single strand blocking signal controls virion morphogenesis may vary , to some extent , with the viral strains , the host cells , and the exact experimental conditions . In conclusion , we propose that SS DNA or RNA within immature hepadnavirus NCs triggers a blocking signal that negatively regulates their envelopment and secretion , thus ensuring the secretion of only DS DNA ( or RNA-DNA hybrid ) in virions . However , empty capsids assembled in host cells that are devoid of any viral or cellular nucleic acid , thus lacking the negative signal , can be enveloped and secreted as “empty” virions containing capsids but no nucleic acid . Therefore , the long-sought-for maturation signal , a positive signal for envelopment , may in fact represent the removal of this negative signal , as the maturing NC retains ever-shorter SS DNA due to second ( plus- ) strand DNA elongation . In common with the maturation signal hypothesis , the SS blocking hypothesis also entails an as yet ill-defined structural change accompanying plus-strand DNA elongation ( i . e . , loss of the blocking signal ) . However , the blocking hypothesis predicts that mature , DS DNA-containing NCs share a structural characteristic with empty capsids ( both lacking the blocking signal ) and furthermore , this characteristic is absent from the pgRNA or SS DNA-containing NCs . The potential pathophysiological significance , if any , of the empty HBV virions remains to be determined . Among other possibilities , these empty virions may function as immune modulators , defective interfering particles , and markers of viral production or tissue damage [13] , [55] . As the decision to secrete DNA-filled vs . empty virions is actually made at an earlier step during HBV assembly , i . e . , during NC formation and pgRNA packaging ( Figure 8A ) , the relative abundance of these two different virion populations reflects the efficiency of pgRNA packaging and may be a convenient marker for monitoring this complex step in the HBV life cycle that requires not only the viral RT and core proteins and pgRNA but also cellular factors [56] , [57] .
pCMV-HBV contains the HBV ( ayw ) 1 . 1-mer over-length genomic sequence driven by the cytomegalovirus ( CMV ) immediate-early promoter [38] . pCMV-HBV-Pol- and pCMV-HBV-Env- were derived from pCMV-HBV and are defective in polymerase and envelope protein expression , respectively [26] . pCMV-HBV-Core-Pol- was derived from pCMV-HBV-Pol- by introducing a 4 nt ( GATC ) insertion at position 1986 creating a frameshift mutation after codon 30 in the core gene . pCMV-HBV-Y63D bears a Y63D substitution in the RT gene eliminating DNA synthesis [22] . All site-specific mutations were confirmed by automated DNA sequencing . pA3G-Flag expresses the Flag-tagged human Apobec3G protein [18] . The human hepatoma cell line HepG-2 were transfected by FuGene 6 ( Roche ) [18] , [58] . The HepAD38 cell line was derived from HepG-2 cell and replicates HBV in a tetracycline ( Tet ) -repressible manner [59] . Core protein expression levels in the cytoplasmic lysate were analyzed by SDS-PAGE and western blotting using the anti-HBV core antibody ( Dako , a rabbit polyclonal antibody; or a mouse monoclonal specific for the N-terminal end of the core protein [23] ) as described previously [18] , [58] . Briefly , primary antibodies were diluted at 1∶1 , 000 and incubated with proteins bound to Immobilon-P membrane ( Millipore ) overnight . Secondary anti-mouse or anti-rabbit peroxidase labeled antibodies were used at 1∶20 , 000 dilution . Chemilluminescence was used for detection of the bound antibody . A rabbit polyclonal antibody specific for the last 14 residues of the HBV core protein [39] was used where indicated to specifically detect the C-terminal sequence of the core protein . Virion- or naked capsid-associated DNA was isolated and analyzed by Southern blotting as described previously [18] , [58] . Native HBV NCs were purified from transfected HepG-2 cells by sucrose gradient centrifugation [11] , [60] . Recombinant HBV capsids purified from bacteria were obtained from Virogen . Purified NCs and capsids were resolved by native agarose gel electrophoresis [18] , [58] , [61] , [62] and the gel was stained by SYBR Gold ( Invitrogen ) to detect RNA and DNA; after destaining with 10% methanol and 7% glacial acetic acid for 2 hr , the same gel was restained with SYPRO Ruby ( Sigma ) to detect proteins . The protein and nucleic acid signals were detected and quantified by using a Molecular Imager ( BioRad FX-PRO Plus ) . Culture medium containing HBV virions and naked NCs was concentrated by polyethylene glycol precipitation and digested with DNase I ( 1 mg/ml at 37°C for 1 h ) to eliminate residual plasmid DNA and fractionated by isopycnic CsCl gradient ultracentrifugation [7] , [11] , [61] to remove naked ( non-enveloped ) NCs . Purified virion fractions or DNase digested concentrated medium samples were analyzed by native agarose gel electrophoresis as described [63] . Encapsidated RNA or DNA in viral particles was detected using 32P-labeled RNA or DNA probe as indicated , followed by detection of core proteins associated with virions or naked NCs on the same membrane using the anti-core antibody . Goat polyclonal anti-HBV surface protein ( Dako ) was then used to detect the viral envelope proteins after stripping the membrane . The nature of virion DNA was determined by Southern blotting after extraction from purified virions using SDS/proteinase K digestion [7] , [11] . The core protein associated with virions was also analyzed by SDS-PAGE and western blot analysis as described above . Sera from HBV infected chimpanzees have been described before [24] . DNA levels were quantified using phosphoimaging following Southern blot analysis and protein levels using densitometry following western blot analysis . Viral DNA and core protein standards were used to generate standard curves from which sample virion DNA and core protein levels were quantified . To estimate the levels of HBsAg , purified recombinant HBsAg ( eEnzyme ) was used as a quantitative standard . Following SDS-PAGE and western transfer , the HBV envelope proteins were detected by a polyclonal rabbit anti-HBs antibody ( Virostat ) . To prepare virion-derived capsids for EM , the virion fractions were treated with 1% NP40-10 mM dithiothreitol ( DTT ) for 30 minutes on ice to remove the viral envelope and release the capsids [12] , [14] . The released virion capsids , along with complete virions were observed under EM after neagative staining as described below . For each sample , an aliquot of 3 µl was placed on a freshly glow-discharged continuous carbon coated copper grid . Phosphotungstic acid negative stain was applied by standard drop method and the sample was examined in JEOL 1400 TEM at 120 kV . For the 3-D reconstruction , 10–20 micrographs were collected with a Gatan Orius SC1000 CCD camera with Digital Micrograph at a calibrated magnification of 22 , 510X and 27 , 845X . Viral particles were selected and processed using Robem [64] . The reconstruction processes were performed without CTF correction using icosahedral averaging with the program Auto3dem , which generated a random model directly from the raw data as the initial starting structure [64] . The final resolution was determined where the Fourier shell correlation fell below 0 . 5 as reported in the summary output file of Auto3dem . The number of viral particles selected , the final resolution and diameters of the 3-D reconstructions are listed in Table S1 . The final pixel sizes were 1 . 26 for the WT HBV capsids and 2 . 52 for all other constructs . The final reconstructions were colored radially using the program Chimera [65] .
|
Hepatitis B virus ( HBV ) , an important global human pathogen and the main cause of liver cancer worldwide , is classified as a para-retrovirus , as it replicates by reverse transcription , i . e . , copying of RNA to DNA , like retroviruses . However , different from retroviruses that are RNA viruses replicating via a DNA intermediate , HBV is a DNA virus that replicates through an RNA intermediate . Like retroviruses , HBV initially packages an RNA copy of its genome into intracellular subviral particles . However , complete HBV virions contain only a double-stranded ( DS ) DNA . The long-standing model to explain this selective presence of DS DNA in HBV virions postulates that DS DNA synthesis is required to trigger virion secretion . We have found , however , that virion secretion does not require any DNA synthesis . Rather , the presence of the single-stranded RNA ( or the single-stranded DNA intermediate of reverse transcription ) negatively regulates virion formation . These results thus change the prevailing paradigm in understanding HBV morphogenesis and also have important implications for virus assembly in general . Furthermore , they raise the important question regarding the role of empty HBV virions identified here in viral replication and pathogenesis .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"virology",
"biology",
"microbiology"
] |
2011
|
Secretion of Genome-Free Hepatitis B Virus – Single Strand Blocking Model for Virion Morphogenesis of Para-retrovirus
|
Despite being phylogenetically very close to Anopheles gambiae , the major mosquito vector of human malaria in Africa , Anopheles quadriannulatus is thought to be a non-vector . Understanding the difference between vector and non-vector mosquitoes can facilitate development of novel malaria control strategies . We demonstrate that An . quadriannulatus is largely resistant to infections by the human parasite Plasmodium falciparum , as well as by the rodent parasite Plasmodium berghei . By using genetics and reverse genetics , we show that resistance is controlled by quantitative heritable traits and manifested by lysis or melanization of ookinetes in the mosquito midgut , as well as by killing of parasites at subsequent stages of their development in the mosquito . Genes encoding two leucine-rich repeat proteins , LRIM1 and LRIM2 , and the thioester-containing protein , TEP1 , are identified as essential in these immune reactions . Their silencing completely abolishes P . berghei melanization and dramatically increases the number of oocysts , thus transforming An . quadriannulatus into a highly permissive parasite host . We hypothesize that the mosquito immune system is an important cause of natural refractoriness to malaria and that utilization of this innate capacity of mosquitoes could lead to new methods to control transmission of the disease .
The Anopheles gambiae Giles complex comprises seven mosquito species and several incipient species [1]–[3] . Sibling species are closely related to each other , are morphologically indistinguishable , and can crossbreed in captivity; however , they vary greatly in their capacity to transmit human malaria [3]–[5] . An . gambiae sensu stricto ( henceforth An . gambiae ) and Anopheles arabiensis are highly efficient vectors in sub-Saharan Africa and surrounding islands . Other species of this complex are only locally important vectors: Anopheles melas in western Africa , Anopheles merus in eastern Africa , and Anopheles bwambae in Uganda . Finally , An . quadriannulatus Theobald species A and B , found in southern Africa and Ethiopia , respectively , are exceptional in that they are considered medically unimportant: human malaria parasites have never been detected in wild caught An . quadriannulatus females [4] . Both species display characteristics that are believed to have existed in ancestral forms of the complex , i . e . standard chromosomal arrangements , disjointed distribution and adaptation to temperate climates [1] . Furthermore , they have been considered strictly zoophilic although recent laboratory and field studies report equal feeding preference for human and cattle [6]–[8] . Although laboratory reared An . quadriannulatus species A can be infected with cultured P . falciparum , the infection prevalence is significantly lower than in An . gambiae and Anopheles stephensi [9] . Plasmodium undergoes a complex developmental lifecycle in the mosquito . As shown for the rodent malaria parasite P . berghei , a standard laboratory model system , the parasite suffers substantial losses during its passage through the mosquito . The greatest reduction in parasite numbers occurs at the ookinete-to-oocyst transition stage [10] . Ookinetes , an invasive parasitic form , are often eliminated by lysis ( and clearance ) or melanization in the mosquito midgut epithelium , which are controlled by reactions of the mosquito innate immune system [11] . However , the few parasites that survive to reach the oocyst stage , a sessile parasitic form developing on the basal side of the midgut epithelium , multiply and produce thousands of sporozoites . When the oocysts burst , sporozoites are released to the haemolymph , invade the salivary glands and , upon subsequent mosquito bites , infect human hosts . Here , we investigate the mechanisms of refractoriness to Plasmodium in the malaria non-vector mosquito An . quadriannulatus . We show that refractoriness is controlled by partially dominant genetic traits and is manifest by clearance and melanization of ookinetes in the mosquito midgut as well as by killing of other parasitic stages developing later in the mosquito . The mosquito immune system appears to play a fundamental role in these reactions: inactivation of genes known to contribute to parasite killing in the malaria vector An . gambiae renders An . quadriannulatus a highly efficient vector of the rodent parasite P . berghei . We speculate that the same resistance traits may be present in wild vector populations at lower frequencies , since genetic selection for refractoriness apparently generates An . gambiae lines with phenotypes that are similar to that of An . quadriannulatus [12] , [13]; these phenotypes can be reversed after silencing specific immunity genes [14] . Our data suggest that resistance to malaria may be an ancestral state of mosquitoes and prompt us to hypothesize that co-evolution and co-adaptation between the parasite and its insect host have lead to less refractory populations ( and species ) and successful malaria transmission .
We tested the ability of An . quadriannulatus species A , strain SKUQUA ( henceforth An . quadriannulatus ) , to support development of P . falciparum . An . gambiae mosquitoes of the Yaoundé strain [15] were used as a reference . Three to four-day-old female mosquitoes were fed via a membrane with cultured P . falciparum gametocytes , and 10 days later their midguts were dissected and examined for oocysts . The results from two independent feeding experiments showed 0 of 18 ( 0/18 ) and 6/39 An . quadriannulatus midguts infected ( 0% and 15 . 4% infection prevalence , respectively ) ; the corresponding median oocyst densities were 0 . 0 in both experiments ( Table 1 ) . In the paired feedings of An . gambiae , a known host for P . falciparum , 13/38 and 13/30 midguts had at least one viable oocyst ( 34 . 2% and 43 . 3% infection prevalence , respectively ) with corresponding median oocyst densities of 5 . 0 and 12 . 0 . Two subsequent An . quadriannulatus infections showed no live oocysts , but melanized ookinetes were occasionally observed in the mosquito midguts ( Figure 1A ) ; however , control An . gambiae mosquitoes were not used in these experiments , and thus comparisons cannot be made . Clearance of pre-oocyst parasitic stages and melanization of ookinetes are established important immune reactions of mosquitoes against Plasmodium . Thus , these data suggested that mosquito immunity could contribute to the reduced susceptibility of An . quadriannulatus . To investigate further this possibility , we utilized the convenient laboratory parasite , P . berghei , against which an extensive repertoire of mosquito immune responses has been previously documented . In these experiments , An . quadriannulatus and control An . gambiae mosquitoes were infected with a transgenic P . berghei parasite line that constitutively expresses green fluorescent protein ( GFP ) throughout its lifecycle [16] . Data from four independent infection experiments showed that An . quadriannulatus mosquitoes are highly refractory to P . berghei , in terms of oocyst prevalence , parasite density and ookinete melanization . A representative picture of an infected An . quadriannulatus midgut , with melanized P . berghei ookinetes , is shown in Figure 1B . The four experiments were analyzed by the Residual Maximum Likelihood ( REML ) variance components analysis , which revealed that the outcomes of these experiments were homogeneous and unlikely to be the result of random effects; thus justifying pooling of the data . Compared to An . gambiae ( n = 118 , where n is the number of midguts in the pooled data ) , An . quadriannulatus ( n = 167 ) exhibited both reduced oocyst prevalence ( 67% vs . 100%; P<0 . 001 ) and increased ookinete melanization prevalence ( 93% vs . 13%; P<0 . 001 ) in their midguts 7–10 days post infection . As shown in Figure 1C , the density of melanized parasites per midgut of An . quadriannulatus was markedly greater than in An . gambiae ( P<0 . 001 ) where melanization was only sporadically observed . In contrast , the live oocyst density was much lower ( P<0 . 001 ) in An . quadriannulatus than in An . gambiae . The distributions of oocyst densities varied significantly ( P<0 . 01 ) between the two mosquito species , as one-third of An . quadriannulatus had no oocysts whereas almost every An . gambiae midgut had one or more ( Figure S1 ) . We assessed mosquito salivary gland infection by sporozoites to determine whether the losses of midgut parasitic stages in An . quadriannulatus can ultimately affect the transmission capacity of these mosquitoes . Importantly , we observed that parasite losses continue at later stages of the parasite lifecycle . In pooled data from three infection experiments ( different from the above ) , the prevalence of P . berghei salivary gland sporozoites at day 21–22 post infection was much lower ( 20%; n = 115 ) compared to the prevalence of oocysts ( 57%; n = 104 ) at day 10 in the corresponding infection ( P<0 . 001; Figure 1D and Table S1 ) . No significant difference in prevalence between midgut ( n = 105 ) oocysts and salivary gland ( n = 85 ) sporozoites was detected in the paired infections of An . gambiae ( 76% vs . 79% , respectively ) . Moreover , salivary gland sporozoites in An . quadriannulatus appeared to be less infective compared to those in An . gambiae . From four bite-back experiments , using equal numbers of An . quadriannulatus or An . gambiae females ( ranging from 10 to 15 ) , which were infected 21–22 days earlier with P . berghei and then allowed to feed on naïve TO mice ( one per experiment per mosquito species ) , only one resulted in mouse infection . In contrast , all four An . gambiae control bite-back experiments were infectious . A great variability in the degree of refractoriness to P . berghei was observed between An . quadriannulatus individuals , indicating genetic polymorphism within the mosquito population . The majority of mosquitoes in the four infection experiments described above displayed an intermediate phenotype with high numbers of both live oocysts and melanized parasites; others were fully resistant exhibiting strong ookinete melanization and no live oocysts , and yet others were highly susceptible , displaying many oocysts and few or no melanized ookinetes . No correlation ( R2 = 0 . 001 ) was detected between oocyst and melanized ookinete counts ( Figure S2 ) . This variability within the population suggested that these phenotypes are determined by quantitative genetic traits . Two independent crossing experiments were carried out to determine whether the refractory traits of An . quadriannulatus are heritable . In these experiments , F1 females were generated by mass mating of An . quadriannulatus males and An . gambiae females ( the reciprocal cross is uninformative because it predominantly yields males [17] ) . The resulting F1 females were then backcrossed to An . quadriannulatus males to obtain backcrossed F2 females . First generation F1 and F2 progenies , and the parental An . quadriannulatus and An . gambiae populations were compared for their ability to support parasite development ( see Materials and Methods ) . The data resulting from the two crossing and infection experiments were pooled and analyzed with Kruskal-Wallis non-parametric ANOVA . In terms of infection , the F1 hybrids were phenotypically similar to the An . quadriannulatus but different from the An . gambiae parental populations in both prevalence of infection ( P<0 . 001 ) and oocyst density ( P = 0 . 03; Figure 2 ) . They displayed 80% oocyst prevalence with a median density of 5 . 5 per midgut ( n = 66 ) compared to 71% and 5 . 0 in An . quadriannulatus ( n = 49 ) and 98% and 14 . 0 in An . gambiae ( n = 53 ) , respectively . F1 and parental An . quadriannulatus mosquitoes exhibited marked similarity in their pattern of ookinete melanization . The prevalence of melanization was 77% in F1 hybrids and 80% in parental An . quadriannulatus; both of these were very different from the prevalence of melanization in An . gambiae ( 23%; P<0 . 001 ) . A more striking similarity of refractoriness with the parental An . quadriannulatus was detected in the F2 backcrossed mosquitoes ( n = 86; 69% oocyst prevalence and 4 . 0 oocyst density ) . Melanized ookinetes were detected in 75 of the 86 F2 females ( 87% prevalence ) with median density 12 . 0 per midgut . All these measurements were significantly different from those reported above for An . gambiae ( all at P<0 . 001 but the oocyst density which was at P<0 . 01 ) . Together these data suggested that the refractoriness exhibited by An . quadriannulatus is heritable and that both traits contributing to this phenotype , reduction in the number of oocysts and increase in the number of melanized ookinetes , are dominant or partially dominant . Given that the refractory mechanisms of An . quadriannulatus are under genetic control , we sought to determine if these are due to reactions of the mosquito innate immune system . Several An . gambiae genes have been implicated in lysis , clearance and melanization of P . berghei ookinetes in the mosquito midgut . In this initial study , we examined three genes , LRIM1 , LRIM2 ( previously called APL1 [13]; synonym is suggested here for systematization ) and TEP1 , all of which exhibit potent antagonistic effects against P . berghei . LRIM1 and LRIM2 encode leucine-rich repeat proteins , and silencing of either gene by RNA interference ( RNAi ) remarkably increases live oocyst densities in An . gambiae [13] , [18] . LRIM1 also mediates melanization of ookinetes in mosquitoes that are deficient for the melanization inhibitor C-type lectin 4 , CTL4 . TEP1 is the founder member of a thioester-containing protein family; it binds ookinetes promoting their lysis or melanization [14] . We used An . gambiae-specific oligonucleotide primers to amplify exon sequences of these genes from a cDNA pool constructed from An . quadriannulatus adult females . Sequencing the amplified fragments revealed a high degree of sequence similarity between An . gambiae and An . quadriannulatus for all the three genes: 98 . 9% for LRIM1 , 96 . 2% for LRIM2 and 98 . 7% for TEP1 ( Figure S3 ) . This was not surprising as the two species are very closely related in the evolutionary scale and genetic introgression has likely taken place for some time after their separation . Using these gene fragments as templates , we produced double stranded RNA ( dsRNA ) sequences for each of these genes , which were microinjected separately in the body cavity of freshly emerged An . quadriannulatus females , as described for An . gambiae [19] . Mosquitoes injected with dsRNA of the LacZ gene were used as a control . Quantitative RT-PCR ( qRT-PCR ) revealed robust and specific silencing of cognate gene expression 4 days later , which ranged from 98% for LRIM1 to 89% for LRIM2 . As shown in Figure 3 , silencing of LRIM1 , LRIM2 or TEP1 in An . quadriannulatus resulted in a striking increase in P . berghei oocyst density ( P<0 . 001 ) and complete inhibition of ookinete melanization compared to the control ( P<0 . 001 ) . While the oocyst density was 2 . 0 per midgut in control mosquitoes , this number increased to 102 . 5 in LRIM1 and 141 . 0 in LRIM2 knockdown ( kd ) mosquitoes . A similar increase was observed by silencing TEP1 compared to the control: 116 . 5 vs . 1 . 1 , respectively . These results suggest an effect for these genes in both ookinete melanization and clearance of parasites ( possibly by lysis ) , as the number of oocysts in kd mosquitoes is much higher than the sum of oocysts and melanized ookinetes in the controls . The very high sequence similarity between An . gambiae and An . quadriannulatus , which is expected for most genes in the two genomes , pointed to an intriguing possibility: that An . gambiae-specific dsRNA can be directly used to silence genes in An . quadriannulatus . Indeed , An . gambiae-specific dsRNA for LRIM1 fully rescued the susceptibility phenotype when injected into An . quadriannulatus females , causing an approximately 4-fold increase in the oocyst density , 71% increase in oocyst prevalence ( from 29% to 100% ) and complete inhibition of ookinete melanization ( data not shown ) . This result would constitute an important breakthrough if it pertains to additional genes: this non-vector species could then be utilized in conjunction with the An . gambiae vector as a model system , to further understand differences contributing to its reduced vectorial capacity . Our results clearly indicate that a mosquito innate immune response accounts for most of the resistance of An . quadriannulatus to P berghei . LRIM1 , LRIM2 and TEP1 are essential elements of this response and likely to operate in the same pathway , since their effects on the parasite are very similar . We examined with qRT-PCR whether the transcriptional profile of any of these three genes is different between the two mosquito species . In two independent experiments , mosquitoes were allowed to feed either on naive mice or on mice infected with P . berghei , and RNA samples from whole mosquitoes were prepared 24 hrs later . Sugar-fed mosquitoes of the same generation and age were also included . The results showed that the expression levels of LRIM1 and TEP1 were similar between the two species at all three conditions , with minor variations ( Figure S4 ) . However , the levels of LRIM2 in sugar-fed and naive blood-fed mosquitoes were consistently elevated in An . quadriannulatus compared to An . gambiae . It remains to be explored whether this difference in LRIM2 expression is directly related to the refractoriness phenotype of An . quadriannulatus . Furthermore , although only few nucleotide differences were identified in the sequenced gene fragments between An . gambiae and An . quadriannulatus , some of these differences in LRIM1 and LRIM2 lead to non-synonymous amino acid substitutions . Future research will aim to determine if any of these changes ( or others in the non-sequenced gene segments ) can enhance or otherwise alter the function of these genes , thus contributing to the refractoriness phenotype . The An . gambiae LRIM2 gene is located in a genomic region that was recently identified to control the density of mosquito infection with P . falciparum in a natural malaria transmission system in Mali , West Africa [13] . The same locus was responsible for almost 90% of parasite-free mosquitoes and 100% of mosquitoes with melanized parasites . These responses are highly similar to those we report here for An . quadriannulatus , making LRIM2 a strong candidate for regulating natural mosquito refractoriness to the human malaria parasite . On the other hand , LRIM1 was recently shown to have undergone strong positive selection in the other major African vector , An . arabiensis; this “arabiensis-like” allele has been introduced in An . gambiae populations at lower frequencies through multiple introgression events , but it is not present in other , less competent species of the complex including An . quadriannulatus [20] . These data in conjunction with the failure to demonstrate an apparent effect of An . gambiae LRIM1 on sympatric field isolates of P . falciparum in a laboratory transmission setting in Cameroon [21] , could suggest that LRIM1 is subject to evolutionary adaptation to the human parasite; however , the effect of LRIM1 on allopatric isolates or laboratory strains of P . falciparum is yet to be examined . Finally , An . gambiae TEP1 was shown to have a strong antagonistic effect against a laboratory P . falciparum line [22] . Furthermore , a refractory allele of this gene is found in a genetically selected mosquito strain which kills and melanizes all Plasmodium species or strains that have been tested , except sympatric isolates of P . falciparum [12] . The phenotype of this refractory An . gambiae strain is identical to that of An . quadriannulatus . Therefore , it is tempting to speculate that persistent interaction of An . gambiae ( and other major vectors ) with P . falciparum might have led to an evolutionary co-adaptation between the mosquito immune responses and this parasite , whereas the resistance phenotype of the mostly zoophilic An . quadriannulatus could represent the ancestral function of the mosquito immune system against the parasite . Malaria kills up to three million people every year and threatens the lives of almost half of the global population . Of the several hundreds of mosquito species only some anophelines can transmit human malaria . Even within the An . gambiae species complex , which includes some of the most important malaria vectors in Africa , the two An . quadriannulatus species are considered non-vectors . Researchers have proposed that understanding the differences between vector and non-vector mosquitoes could provide a new means for malaria control . Our research establishes for the first time a model laboratory system to study these differences at a genetic and molecular level . It demonstrates that mosquito immunity , which regulates the density of infection by the model rodent parasite , P . berghei , in the most competent vector of human malaria , An . gambiae , is the main cause of refractoriness to P . berghei in its non-vector sibling An . quadriannulatus species A . It remains to be revealed whether these findings also apply to infections with the human parasite P . falciparum .
The An . quadriannulatus SKUQUA strain was established from wild mosquitoes collected from an area near Skukuza , Kruger National Park , South Africa , in December 1995 . The An . gambiae Yaoundé strain was colonized from wild mosquitoes collected from the Yaoundé area in 1988 [15] . Both mosquito colonies were raised at 28°C , 65–70% relative humidity , under a 12 hr light/dark cycle; adult mosquitoes were maintained on a 10% ( w/v ) sucrose solution . Infections with P . berghei were performed using the PbGFPCON parasite line [16] , cultured using standard methods [23] . For infection , 50–70 female mosquitoes were randomly separated in paper caps , fed on anaesthetized mice infected with P . berghei ( parasitaemia >5% ) and kept at 20–21°C until the day of dissection . Midguts of mosquitoes were dissected 7–10 days post infectious blood meal , fixed in 4% para-formaldehyde and mounted on microscopy slides using VectaShield ( Vector Laboratories Inc ) before visualized with a light/fluorescence microscope . Killed parasite that appear as melanized ookinetes in the midguts and living oocyst that fluoresce green were separately quantified; melanized ookinetes were detected in bright field and oocysts were visualized with the fluorescein isothiocyanate filter . For P . falciparum infections , erythrocytic stages of the 3D7 clone of the NF54 isolate were cultured as described [24] , followed by induction of gametocytogenesis [25] . Cultures were then added to RBCs with HI AB serum at packed cell volume ( ca . 40% ) and introduced into membrane feeders . Mosquitoes were exposed to the membrane feeders for 25–30 min , and thereafter kept at standard insectary conditions until dissection . Mosquito midguts were dissected 7–9 days post infection , stained with 0 . 5% mercurochrome and examined for live oocysts and melanized ookinetes using a light microscope . Detection of the infectivity of salivary gland sporozoites was carried out by mosquito bite-back experiments as described [26] , with minor modifications . Female An . quadriannulatus and An . gambiae mosquitoes were infected with PbGFPCON P . berghei after feeding on an infected TO mouse . Non blood-fed mosquitoes were removed . The presence of oocysts on the mosquito midguts was confirmed at day 8–10 post infection . At day 21–22 post infection , 10–15 of these mosquitoes were allowed to feed on naïve 8–10 week-old TO mice . The mice were then screened for blood staged parasites on day 5 after the mosquito bite , and the screening was continued every other day until day 15 . The bite-back was considered non-infective if no blood-staged parasites were detected by day 15 . Four independent replicate experiments were performed . In each crossing experiment , male An . quadriannulatus ( 300–400 ) and female An . gambiae ( 100–150 ) adult mosquitoes were allowed to mass mate to produce the F1 progeny . 100–150 females from this F1 progeny were then backcrossed to 300–400 An . quadriannulatus males to obtain the F2 progeny . In parallel to this backcrossing , the initial crossing of the parental populations was repeated in order to obtain first generation F1 progeny which were of the same age as the F2 progeny . Females of the parental populations and the F1 and F2 progenies were allowed to feed on P . berghei-infected mice as described above . Because there were four groups of females for each infection experiment , we used two mice of similar parasitaemia , each of which was randomly allocated to two of these groups; after 10 min in feeding , mice were swapped between group pairs . The entire crossing and infection experiment was repeated twice . DsRNA production was performed as previously described , using gene specific oligonucleotide primers tailed with the short T7 promoter sequence TAATACGACTCACTATAGGG [27] . The sequences of these primers are: LRIM1 F , AATATCTATCTCGCGAACAATAA; LRIM1 R , TGGCACGGTACACTCTTCC; LRIM2 F , GCTTACGCGCACACTATTCA; LRIM2 R , GCTATTGTGCGATGCGTCTA; TEP1 F , TTTGTGGGCCTTAAAGCGCTG; TEP1 R , ACCACGTAACCGCTCGGTAAG; LACZ F , AGAATCCGACGGGTTGTTACT; LACZ R , CACCACGCTCATCGATAATTT . Injection of dsRNA in adult female mosquitoes was performed as described [19] . The aforementioned primers were also used to PCR amplify and determine the sequence of the An . quadriannulatus genes . Two additional primers were used for sequencing another fragment of the LRIM1 gene in An . quadriannulatus , which was not part of the dsRNA-targeted sequence: LRIM1 988 F , ATCGCGCTGAAGCGCAAAGAG; LRIM1 1530 R , TTATCCCAGCTGGCTCGCTAAATTCTG . qRT-PCR was performed as described previously [27] , with the following modifications . Total RNA was extracted from approximately 10 adult mosquitoes with 1 ml of TRIzol reagent ( Invitrogen ) and treated with Turbo DNAfree ( Ambion ) according the manufacturer's directions . 1 µg of total RNA was used for reverse transcriptions using Superscript II ( Invitrogen ) . Transcript abundance was measured with an Applied Biosystems 7700 Real-Time PCR system using the ribosomal S7 gene as an internal control . Reactions of 25 µl consisted of 1× SYBR green mix ( Applied Biosystems ) and cDNA , corresponding to 2 . 5 ng of total RNA . The primer sequences and concentrations in the final reaction are: LRIM1 1914 F ( 0 . 9 µM ) , CATCCGCGATTGGGATATGT; LRIM1 1983 R ( 0 . 9 µM ) , CTTCTTGAGCCGTGCATTTTC; LRIM2 825 F ( 0 . 9 µM ) , GCAAAGAAAGTGACAAGCCGTAT; LRIM2 884 R ( 0 . 3 µM ) , CGCTCGTCAGGGCAATGTA; TEP1 2676 F ( 0 . 9 µM ) , AAAGCTGTTGCGTCAGGG; TEP1 2750 R ( 0 . 3 µM ) , TTCTCCCACACACCAAACGAA; S7 F ( 0 . 3 µM ) , GTGCGCGAGTTGGAGAAGA; S7 R ( 0 . 3 µM ) : ATCGGTTTGGGCAGAATGC . For analysis of the data , the prevalence of infection and parasite density were treated as two independent infection variables , although they are likely to be partly connected . The prevalence data were analysed using the chi-square goodness-of-fit test , except for comparing the prevalence of P . falciparum infection between An . quadriannulatus and An . gambiae where Fisher exact test with Yates correction was used . For the analysis of density of oocysts and melanised ookinetes , mosquitoes showing no parasites ( neither live oocysts nor melanised ookinetes ) in their midguts were excluded , and the data were subjected to normality and homogeneity tests . As counts of both live and dead parasites ( x ) displayed right-skewed distributions , the geometric means were computed after data normalization by log10 ( x+1 ) transformation . The log-transformed data from all replicates within a study ( dataset ) were analyzed by REML ( Rresidual Maximum Likelihood ) variance components analysis by fitting the mixed effect model . In this model , we treated mosquito species or the control kd status as a fixed effect and introduced a random effect for the replicates . For each dataset a combined P-value is reported for the fixed effects . When normality of datasets could not be achieved by the above transformation method ( i . e . for the P . falciparum oocyst density and for the density of oocysts and melanised ookinetes in the crossing experiments ) the median value of untransformed data was computed and datasets were subjected to Kruskal-Wallis one-way ANOVA . Comparison of oocyst and melanised ookinete distributions between An . quadriannulatus and An . gambiae midguts was carried out using the Kolmogorov-Smirnov ( KS ) test . All these statistical analyses were performed using the GenStat® software .
|
Malaria is a mosquito-borne infectious disease that threatens almost half of the human population and kills 1 to 3 million people every year . In sub-Saharan Africa , where the vast majority of deaths occur , the capacity of mosquitoes to transmit malaria varies greatly even between closely related species . We compared the ability of malaria parasites to develop in two very closely related mosquitoes , one vector and one non-vector , and found that non-vector mosquitoes kill parasites at various stages , predominantly when they invade the mosquito midgut . This is achieved by parasite clearance , possibly by lysis in the midgut cells and by melanization , both of which are reactions of the mosquito immune system . This phenotype depends on heritable and dominant traits that can be passed on to vector/non-vector mosquito hybrids . We examined whether specific components of the mosquito immune system affect the resistance of these mosquitoes to infection . By silencing the activity of three immunity genes , we transformed mosquitoes of the resistant species into highly susceptible . Our results suggest that the mosquito immune system may affect refractoriness to malaria in non-vector mosquitoes . This innate capacity of mosquitoes to kill malaria parasites could be utilized in future integrated efforts to control and ultimately eradicate the disease .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"microbiology/innate",
"immunity",
"immunology/innate",
"immunity",
"genetics",
"and",
"genomics/disease",
"models",
"genetics",
"and",
"genomics/gene",
"function",
"public",
"health",
"and",
"epidemiology/infectious",
"diseases",
"infectious",
"diseases/protozoal",
"infections",
"infectious",
"diseases/tropical",
"and",
"travel-associated",
"diseases",
"immunology/immunity",
"to",
"infections"
] |
2008
|
Transmission Blocking Immunity in the Malaria Non-Vector Mosquito Anopheles quadriannulatus Species A
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Mycobacterium tuberculosis infection is associated with a spectrum of clinical outcomes , from long-term latent infection to different manifestations of progressive disease . Pro-inflammatory pathways , such as those controlled by IL-1β , have the contrasting potential both to prevent disease by restricting bacterial replication , and to promote disease by inflicting tissue damage . Thus , the ultimate contribution of individual inflammatory pathways to the outcome of M . tuberculosis infection remains ambiguous . In this study , we identified a naturally-occurring polymorphism in the human IL1B promoter region , which alters the association of the C/EBPβ and PU . 1 transcription factors and controls Mtb-induced IL-1β production . The high-IL-1β expressing genotype was associated with the development of active tuberculosis , the severity of pulmonary disease and poor treatment outcome in TB patients . Higher IL-1β expression did not suppress the activity of IFN-γ-producing T cells , but instead correlated with neutrophil accumulation in the lung . These observations support a specific role for IL-1β and granulocytic inflammation as a driver of TB disease progression in humans , and suggest novel strategies for the prevention and treatment of tuberculosis .
Tuberculosis ( TB ) , a chronic bacterial disease caused by Mycobacterium tuberculosis ( Mtb ) , remains a major global health problem that claims 1 . 4 million lives annually . Natural infection with Mtb is initiated by the deposition of Mtb-containing aerosol droplets onto lung alveolar surfaces , and infection at this site can produce a wide spectrum of clinical outcomes . The vast majority of immunocompetent individuals contain the pathogen and remain indefinitely asymptomatic , a status defined as “latent” TB [1] . A smaller proportion of Mtb-infected people develop active disease , most often characterized by a progressive inflammatory pathology of the lung . The hematogenous dissemination of Mtb can also result in disease at a variety of extra pulmonary sites , commonly including bone and the central nervous system [2] . The mechanisms controlling TB progression remain elusive . Both Mendelian genetic susceptibilities in the IL-12 and IFN-γ axis [3] , [4] , and the association of TB with HIV-mediated lymphocyte depletion indicate that cell-mediated adaptive immunity is critical for controlling mycobacterial growth and containing disease . However , genetic association studies also commonly identify single nucleotide polymorphisms ( SNPs ) in pro-inflammatory innate immune mediators that are associated with TB susceptibility and regulate cellular function , such as TLRs , LTA4H , IL22 , and IL6 [5] , [6] , [7] , [8] , [9] , [10] . These associations suggest that disease progression may be determined at multiple levels in human populations and that the inflammatory response could play a decisive role . As a potent proinflammatory cytokine , IL-1β plays an important role in many inflammation-related diseases as well as cancer . Accordingly , IL1B gene polymorphisms , especially functional SNPs -31 T>C ( rs1143627 ) and -511 G>A ( rs16944 ) are associated with susceptibility to a number of diseases [11] , [12] , [13] , [14] . IL-1β is also important in the pathogenesis of TB in mice [15] , and polymorphisms in the human IL1B gene have been suggested to have differing effects on TB susceptibility . Awomoyi reported the IL1B rs16944 , but not +3953 T>C ( rs1143634 ) SNP , was associated with susceptibility to TB in a Gambian population [16] . Kusuhara et al reported that three SNPs in IL1B ( rs1143629 , rs1143643 and rs3917368 ) are associated with TB susceptibility in a Japanese population [17] . Similarly , susceptibility to TB is associated with the polymorphic +3953 region in the IL1B gene [18] . Using an oligochip-based method , Sun et al genotyped two SNPs ( rs1143627 , rs16944 ) in a small cohort of the Chinese Han population ( 98 TB patients and 65 healthy controls ) , and found that the rs1143627T allele was associated with TB susceptibility [19] . On the contrary , a strong protection conferred by IL1B +3953 T-allele-carrying genotypes was observed in a Northwestern Colombian population [20] . While these studies implicated several SNPs in IL1B gene were associated with TB susceptibility , a conclusive role of IL1B SNPs has not yet been established due to the small sample sizes and different ethnic background in these studies . Furthermore , the mechanisms by which these polymorphisms might act remain unclear . Recent mechanistic studies in the mouse model have implicated IL-1β mediated granulocytic inflammation as a potential driver of TB progression [21] , [22] , [23] . IL-1β is a prototypical proinflammatory cytokine that stimulates both local and systemic responses [24] , and this cytokine plays a complex dual role in chronic infections . The production of IL-1β is important for the proper development of antimicrobial adaptive immunity during the initial stages of infection [25] , [26] . However , prolonged IL-1β production promotes the continual recruitment of granulocytes to the lung , induces the expression of additional inflammatory mediators such as prostaglandin E2 , and stimulates tissue-damaging metalloproteinases [27] , [28] . As a result , the production of this cytokine must be repressed in chronically Mtb-infected animals to avoid progressive pathology [21] , [29] . While , production of IL-1β correlates with the severity of human TB disease [30] , [31] , its paradoxical activities in promoting both antimycobacterial immunity and chronic tissue damage leave the ultimate contribution of this cytokine to TB progression in human populations unclear . In this study , we identified a genetic polymorphism ( rs1143627 ) in the promoter region of the IL1B gene that increases the C/EBPβ- and PU . 1-dependent expression of the cytokine . The high-IL-1β-expressing genotype is associated with increased risk of active tuberculosis and poor clinical outcome . The observed correlation between IL-1β production , neutrophil recruitment , and TB susceptibility indicates a causative role for IL-1β-mediated granulocytic inflammation in TB progression .
Genetic variants in the IL1B gene , especially those in the promoter region , can affect cytokine expression and have been associated with susceptibility to inflammatory disorders [24] , [32] and chronic infections [33] , [34] . Since TB is a disease of chronic inflammation , we hypothesized that similar genetic variants might alter susceptibility to this disease . To test our hypothesis , the genotype distribution of 4 IL1B SNPs with potential regulatory effects was first determined in healthy controls and TB patients from a cohort in Shenzhen and then replicated in a Shanghai cohort ( Table 1 ) . Among them , the following three SNPs were located within the known promoter region , -31 T>C ( rs1143627 ) , -511 G>A ( rs16944 ) , and -1473 G>C ( rs1143623 ) . The final SNP , +7326 T>C ( rs2853550 ) , is located in the 3-UTR of IL1B gene . The minor allele frequencies of all 4 SNPs were>10% and were in Hardy-Weinberg equilibrium in both control groups ( P>0 . 05 ) . However , only the frequency of rs1143627 was significantly different between patients with active TB ( TB , n = 1533 ) and healthy controls ( HC , n = 1445 ) in the Shenzhen cohort . Specifically , a significantly higher frequency of the T allele at rs1143627 was observed among patients with active TB , compared with controls , indicating that this allele is associated with an increased risk of tuberculosis ( odds ratio [OR] = 1 . 20; 95% confidence interval [CI] , 1 . 09–1 . 33; P = 0 . 0004 ) . At the genotype level , carriage of the IL1B rs1143627T allele increased the apparent risk of active TB ( OR = 1 . 35; 95% CI , 1 . 14–1 . 59; P = 0 . 0005 , dominant model ) . TT and TC genotypes also showed increased TB susceptibility compared to CC genotype using an additive model ( OR = 1 . 44; 95%CI , 1 . 18–1 . 76; P = 0 . 0004; and OR = 1 . 30; 95%CI , 1 . 09–1 . 55; P = 0 . 004 ) ( Table 2 ) . The association of rs1143627 with tuberculosis was replicated in an independent Shanghai cohort . In this group the T allele was associated with TB susceptibility using a multiplicative model , with an adjusted OR of 1 . 40 ( 95% CI , 1 . 10–1 . 77 , P = 0 . 007 ) . Using an additive model the T allele was estimated to impart a 97% increase in risk ( OR = 1 . 97; 95% CI , 1 . 20–3 . 23; P = 0 . 006 ) ( Table 3 ) . These observations from two large independent cohorts were similar and consistent with the reported distribution of rs1143627 and rs16944 alleles in separate small cohort of Chinese TB patients and healthy controls [35] . When we pooled the Shenzhen and shanghai data together ( 1799 cases and 1707 controls ) for statistical analysis , the effect of rs1143627T was even stronger in all genetic models ( multiplicative , additive , dominant and recessive , Table S1 ) than in either cohort alone . Thus , we conclude that the T allele of IL1B SNP rs1143627 is associated with susceptibility to active TB in the Chinese population . While TB is primarily a disease of the lung , dissemination of the bacterium can also result in pathology at a variety of different tissue sites . To understand if the rs1143627 SNP differentially affected pulmonary versus extrapulmonary disease , we further investigated whether the distribution of rs1143627 alleles differed in patients suffering from distinct manifestations of TB . As shown in Table 4 , the rs1143627T allele is significantly associated with both pulmonary TB ( PTB , OR = 1 . 17; 95% CI , 1 . 06–1 . 30; P = 0 . 003 ) as well as extrapulmonary disease ( ETB , OR = 1 . 78; 95% CI , 1 . 32–2 . 39; P<0 . 0001 ) using a multiplicative model . However , the frequency of the rs1143627T allele was significantly higher in patients with extrapulmonary TB than in those with pulmonary disease ( P = 0 . 005 ) . Thus , among TB patients , individuals carrying rs1143627T allele are more prone to develop extrapulmonary TB than those without ( OR = 1 . 53 , 95% CI , 1 . 14–2 . 05 , multiplicative model ) . The proinflammatory activity of IL-1β suggested that the rs1143627 polymorphism might affect the inflammatory response to Mtb , which could be exhibited at either systemic or local levels . No difference in erythrocyte sedimentation rate ( ESR ) or C-reactive protein ( CRP ) levels were apparent among pulmonary TB patients carrying different rs1143627 genotypes ( TT , TC , CC ) ( Fig . S1 ) . However , using high-resolution computed tomography ( HRCT ) , we were able to directly quantify lung damage in these patients using a score based on radiographic manifestations including the presence of nodules , cavities , and bronchial lesions [36] , [37] . A total of 453 out of 1432 patients with primary pulmonary TB received an HRCT score before anti-TB treatment . Among these individuals , the HRCT scores were significantly higher in patients carrying rs1143627TT genotype than those with rs1143627CC genotype ( Fig . 1A ) . Even 2 years after the completion of anti-TB treatment , patients ( n = 53 ) of the rs1143627TT genotype still displayed significantly higher HRCT scores than those carrying rs1143627CC , suggesting that the rs1143627 polymorphism is associated with long-term lung damage and disease outcome ( Fig . 1C ) . In contrast , no difference of HRCT score was found in patients carrying different genotypes of rs16944 ( Fig . 1B and D ) , an IL1B SNP that is associated with the risk of developing active TB in the Gambian population [16] , but not in our current study . Taken together , these results indicated that the rs1143627T allele is associated with more severe pulmonary TB and the expression of extrapulmonary disease . Located at position -31 from the transcriptional start site , SNP rs1143627 is in the promoter region of IL1B gene . Previous reports indicated that rs1143627 affects transcription of IL-1β in response to lipopolysaccharide [38] . To determine whether this polymorphism affects gene expression in response to Mtb stimulation , we assessed IL-1β expression in CD14+ monocytes isolated from healthy controls carrying different rs1143627 genotypes in response to heat-killed Mtb or the 19 kDa lipoprotein derived from this bacillus . In response to both stimuli , monocytes isolated from individuals carrying rs1143627TT and TC genotypes produced significantly higher amounts of IL1B protein ( Fig . 2A ) and mRNA ( Fig . 2C ) than those carrying the CC genotype . Since the bioactivity of IL-1β also involves the antagonistic effects of IL-1Ra , we assessed the concentration of IL-1Ra in the culture supernatant and IL1RN mRNA cell lysates . As shown in Fig . 2B and D , no significant differences were observed in IL-1Ra expression upon Mtb lysate stimulation among different rs1143627 genotypes . Together , these results suggest that the rs1143627T allele specifically increased the amount of bioactive IL-1β produced by Mtb stimulation . IL-1β is recognized to play an important role in shaping adaptive immunity , especially Th17 cell responses [39] , [40] . To understand if the rs1143627T allele could promote TB disease by inhibiting the expression of adaptive immunity , we investigated whether this polymorphism altered the production of Mtb antigen-specific IFN-γ or IL-17A , the hallmark cytokines of Th1 and Th17 cells , respectively . PBMC isolated from healthy controls carrying different rs1143627 genotypes were cultured in the presence of heat-killed Mtb and the production of IFN-γ and IL-17A were compared . Consistent with our previous results with purified monocytes , we found that PBMC from individuals carrying rs1143627 TT and TC genotypes produced more IL-1β than those from individuals carrying the CC genotype ( Fig . 3A ) . The rs1143627TT PBMC also produced slightly higher levels of IFN-γ and similar levels of IL-17A as rs1143627 CC cells ( Fig . 3 , B and C ) . The increased production of IFN-γ was a result of IL-1β secretion , as IFN-γ and IL-1β secretion were highly correlated in PBMC cultures and IFN-γ production could be inhibited by the addition of a blocking antibody to IL-1β ( Fig . S2 ) . The effect of rs1143627 on the production of IFN-γ-producing cells was further investigated in a large cohort of pulmonary TB patients . Mtb antigen-specific IFN-γ production by PBMCs was detected by using a previously established IFN-γ Elispot assay that employs either ESAT-6 protein or an ESAT-6/CFP-10 peptide pool as a stimulant [41] , [42] . Patients carrying the rs1143627TT genotype had significantly higher numbers of Mtb antigen-specific IFN-γ spot forming cells ( SFCs ) than those carrying the CC genotype ( Fig . 3 , D and E ) . In contrast , the numbers of IFN-γ SFCs were not different among patients carrying different rs16944 genotypes ( Fig . S3 ) . Thus , the TB-associated rs1143627T allele was associated with an augmentation of the canonical antimycobacterial response . We conclude that the susceptibility of individuals carrying the rs1143627T allele is unlikely to be the result of a failure to respond to mycobacterial antigen . Another primary effect of IL-1β is the recruitment of neutrophils , a process that can exacerbate TB pathogenesis in animal models [21] , [22] , [23] . To investigate whether this cytokine could play a similar role in human TB , we simultaneously measured the level of IL-1β and the number of neutrophils in broncheoalveolar lavage fluid ( BALF ) from patients with active pulmonary TB . A significant correlation between the level of IL-1β and the number of recruited neutrophils was found ( r = 0 . 828 , P<0 . 0001 ) ( Fig . 3F ) . Thus , the increased production of IL-1β associated with the rs1143627T allele could promote lung damage and TB progression by stimulating granulocytic inflammation . The rs1143627 defines a T>C mutation at the -31 position of the IL1Β promoter . We used electrophoretic mobility-shift analysis ( EMSA ) to determine if this polymorphism altered protein binding to this promoter region . Synthetic allele-specific oligonucleotides representing the polymorphic rs1143627 sites were incubated with nuclear protein extracts from human monocytic U937 cells stimulated without or with killed Mtb lysate . There was no difference in binding activity between rs1143627T oligonucleotide ( T Probe ) and rs1143627C oligonucleotide ( C Probe ) when U937 were not stimulated with Mtb lysate ( Fig . 4A ) . Previous Mtb stimulation of these cells induced the formation of a DNA binding complex on the rs1143627T oligonucleotide ( “complex 1” , Figure 4B ) . In contrast , Mtb exposure induced less complex 1 formation on the rs1143627C oligonucleotide ( Fig . 4B , lane 2 and 6; Fig . 4C ) . Furthermore , complex 1 formation on radiolabelled rs1143627 was specifically blocked by competition with the unlabelled oligonucleotide ( Fig . 4B , lanes 3 , 4 , 7 , and 8 ) . These results indicated that the binding of one or more proteins to the IL1B promoter region was altered by rs1143627 polymorphism , which could cause the observed difference in IL-1β expression that was associated with these genotypes . Bioinformatics-based prediction analysis using alibaba and match algorithm ( http://www . gene-regulation . com ) and previous studies of the IL1B promoter region indicated that transcription factors C/EBPa , C/EBPβ , PU . 1 , TBP and SP1 are involved in the regulation of IL1B gene expression and have the potential to bind in the polymorphic region [43] , [44] . To determine if these proteins were part of the rs1143627-modulated complex 1 , we conducted supershift experiments using antibodies against each transcription factor . Addition of antibodies against C/EBPβ and PU . 1 , but not antibodies against RSRFC4 , C/EBPa , TBP or SP1 , to the rs1143627T oligonucleotide binding reactions resulted in shift of complex 1 to a higher molecular weight species ( Fig . 4D ) . Thus , the protein complex formed on the rs1143627-containing region includes PU . 1 and C/EBPβ , suggesting that the T>C polymorphism could alter IL-1β expression by influencing the binding of transcription complexes that contain these factors . To determine if C/EBPβ and PU . 1 are produced in a manner that is consistent with the proposed role in IL-1β regulation , we quantified the expression of all three genes in the monocyte-like U937 cells after stimulation with heat killed Mtb or infection with the live attenuated strain of Mtb , H37Ra . Consistent with previous reports , both stimuli induced IL1B mRNA expression ( Fig . 5A ) . More importantly , we found Mtb exposure also induced the expression of C/EBPB ( Fig . 5B ) and PU . 1 ( Fig . 5C ) , but no significant difference was observed in TBP expression ( Fig . 5D ) . Furthermore , the mRNA level of IL1B was significantly correlated with that of both C/EBPB ( r = 0 . 887 , P<0 . 0001 , Fig . 5E ) and PU . 1 ( r = 0 . 811 , P = 0 . 0001 , Fig . 5F ) , and not correlated with TBP expression ( r = 0 . 314 , P = 0 . 236 , Fig . 5G ) . The induction of PU . 1 and C/EBPB mRNA expression occurred earlier after stimulation than IL1B ( Fig . 5H-K ) , which is consistent with a role for PU . 1 and C/EBPβ in regulating the IL1B promoter in response to Mtb infection . To assess the role of PU . 1 and C/EBPβ in Mtb-induced IL1B transcription , we investigated whether ectopic PU . 1 and C/EBPβ expression affects IL1B promoter activity in HeLa cells . As shown in Fig . 6A , transfection of a reporter plasmid containing a 1371 bp fragment of the human IL1B promoter ( −1292 to +79 ) produced very low luciferase levels . However , cotransfection of the IL1B promoter plasmid with PU . 1 or C/EBPβ expression plasmids significantly enhanced luciferase levels , and cotransfection of all three plasmids further increased promoter activity ( Fig . 6A ) . Expression levels of PU . 1 and C/EBPβ were similar when transfected either individually or in combination ( Fig . 6C ) . siRNA knockdown studies verified the contribution PU . 1 and C/EBPβ in Mtb-induced IL1B transcriptional activity in this transfection system . The expression of both transcription factors could be partially inhibited by siRNA transfection ( Fig . 6C ) . Knockdown of either protein significantly inhibited the Mtb-induced IL1B promoter activity ( Fig . 6B ) . Simultaneous knockdown had an additive effect ( Fig . 6B ) , consistent with the collaborative activity observed in co-transfection experiments ( Fig . 6A ) . Thus , these transcription factors simultaneously contribute to IL1B promoter activity in HeLa cells . We then utilized this reporter assay to determine if altered PU . 1 and/or C/EBPβ transactivation could account for rs1143627 allele-specific regulation of IL1B gene expression . When -31 T>C alterations were introduced into the IL1B promoter we observed no difference in basal transcriptional activity in HeLa cells . However , upon co-transfection with PU . 1 , C/EBPβ , or both , the T allele produced significantly higher luciferase activity than the C variant ( Fig . 6A ) . The degree of increased activity observed for the rs1143627 T allele was similar to that observed in primary monocytes and PBMC ( Fig . 2 and Fig . 3A ) . Taken together , these results indicate that the SNP rs1143627 alters a cis-regulatory element in the IL1B gene that alters C/EBPβ- and PU . 1-dependent expression of IL1B and susceptibility to TB .
Proinflammatory cytokines , such as IL-1β can play complex and dichotomous roles during chronic infections . This cytokine is initially required to prime the anti-mycobacterial immune response [25] , [26] , [45] , and may also promote resistance to initial infection through the induction of epithelial antimicrobial peptides [46] . However , the prolonged production of this cytokine must be restrained to prevent chronic tissue damage [21] . Similarly complex roles for proinflammatory pathways have been described during a variety of viral and bacterial infections [47] , [48] , [49] . As a result , the production of these cytokines often correlates with the severity of human disease , but their complex biological activities make it difficult to determine if this expression is causing pathology or limiting it . In this study , we define a mechanistic link between a polymorphism in the promoter of the IL1B gene and the severity of TB disease , which implies a causal relationship between the expression of this cytokine and the progression of pathology . A similar association between high IL1B expressing genotypes and inflammatory diseases has been observed in a number of previous studies . Associations between TB and IL1B polymorphisms at -511G>A , +3953T>C , and +3962T>C have been reported in a variety of populations [16] , [20] , [50] , [51] , [52] , although our study is the first to describe a link between high Mtb-induced IL-1β production , inflammation , and TB disease . The specific IL1B promoter allele ( rs1143627T ) that was associated with TB in our large cohort was previously reported to be enriched in a smaller study of Chinese TB patients [35] . Indeed , this allele is also associated with other diseases that involve IL-1β-dependent inflammation or cell death , such as influenza [53] , keratoconus [54] , [55] , and coronary artery disease [56] . Thus , it appears that TB susceptibility can be determined by similar mechanisms to those that underlie other inflammatory diseases of diverse etiology . We found that a single base change in the IL1B promoter increased the synergistic activity of the C/EBPβ and PU . 1 transcription factors leading to increased IL-1β expression in monocytes and PBMC . These two transcription factors are known to act in a coordinated manner to drive macrophage differentiation [57] , and our studies suggest they also promote the effector functions of these cells . Individuals carrying the high-IL-1β-producing rs1143627T allele were more prone to develop active TB , to have severe pathology in the lung , and to harbor extrapulmonary lesions . The association between IL-1β production and severe TB disease is consistent with our recent finding that NLRP3 inflammasome dependent IL-1β enhances neutrophil recruitment and exacerbates pulmonary pathology in mice infected with Mtb [21] . In the chronically-infected mouse , IL-1β activity is restrained at a posttranslational level through the nitric oxide ( NO ) -dependent nitrosylation of NLRP3 [21] , an essential component of the inflammasome complex that processes pro-IL-1β into its active form . As this regulatory pathway has also been described in human cells [58] , the processing of rs1143627T-induced pro-IL-1β into its bioactive form is likely mediated either by residual NLRP3 activity or through inflammasome-independent processes . Two general mechanisms could account for the TB susceptibility that is associated with increased IL-1β expression; inhibition of specific antimicrobial immunity or exacerbated inflammatory tissue damage . The former hypothesis is unlikely to explain our current observations , as IFN-γ and IL-17 responses were either unchanged or enhanced in the TB susceptible individuals . The correlation that we observed between IL-1β , IFN-γ and TB disease supports the emerging view of IFN-γ as a poor correlate of protective immunity [59] . Mtb antigen-specific IFN-γ production by Th1 , multifunctional Th1 , or Th1/Th17 cells , is associated with clinical severity and bacterial load in TB patients , and not protective immunity [36] , [60] , [61] . Instead , the significant correlation we observed between IL-1β levels and the number of neutrophils in BALF of patients with active TB supports a specific role for granulocytic inflammation in promoting the progression of TB disease . This model is consistent with the pathological role played by neutrophil recruitment in a number of TB-susceptible mouse strains [22] , [62] , [63] , and the predominance of this cell type in the BALF of humans with active TB [29] , [64] . The high IL-1β expressing rs1143627TT genotype was associated with the severity of lung pathology in TB patients both before and after anti-tuberculosis treatment . This finding is consistent with the poor treatment response of individuals with a high frequency of IL-17/IFN-γ double-producing cells [61] , which our studies suggest as a possible marker of IL-1β production ( Fig . 3 ) . These observations suggest that sustained IL-1β production causes persistent lung damage that could contribute to the permanently decreased lung function observed in patients with advanced and/or recurrent TB [65] . Thus , anti-inflammatory treatments targeted to the IL-1 pathway could be a useful adjunct therapy to mitigate the long-term pulmonary impairment caused by Mtb infection , particularly for individuals of high IL-1β-expressing genotypes .
All protocols for this study were reviewed and approved by the Research Ethics Committee of Shenzhen Third People's Hospital ( No . 2012–003 ) , and conducted according to the Declaration of Helsinki . The use , for research purposes , of excess BALF leftover from clinically indicated bronchoscopies was deemed exempt from a requirement for informed consent beyond the consent normally obtained for this clinical procedure . The Research Ethics Committee approved the collection of peripheral blood exclusively for research purposes with the written informed consent of all participants . Three case-control cohorts were used in this study to investigate the association between IL1B gene polymorphisms and susceptibility to TB . All subjects were genetically unrelated members of the Chinese Han population . The Shenzhen experimental cohort involved 1533 patients with active TB and 1445 healthy controls , which have been used in our previous IL6 polymorphism study[10] . Of the 1533 patients , 1432 were diagnosed with pulmonary TB ( PTB ) , and 101 were with extrapulmonanry TB ( ETB ) including tuberculous lymphadenitis ( n = 62 ) , tuberculous meningitis ( n = 13 ) , and osteoarticular TB ( n = 26 ) . The Shanghai cohort was used for validation and consisted of 266 PTB patients and 262 controls , which has been used for a previous genetic study on IL-17F polymorphisms and TB susceptibility [66] . The diagnosis of tuberculosis was based on clinical symptoms , radiological evidence , and findings from Mtb examination as described previously [10] , [67] . Healthy controls with normal chest radiograph findings and without a clinical history of TB were recruited . The characteristics of the study population are shown in Table 1 . The whole blood samples were store at −80°C after collection for DNA extraction . Peripheral blood mononuclear cells ( PBMCs ) were isolated from whole blood through density gradient centrifugation over Ficoll-Hypaque as described elsewhere [68] and stored in nitrogen . The broncho-alveolar lavage fluid ( BALF ) was collected from Mtb culture confirmed pulmonary TB patients ( n = 57 ) before initiation of anti-TB chemotherapy . Genomic DNA was prepared from peripheral whole blood according to the standard protocols of QIAamp DNA Blood Mini kit ( Qiagen , Hilden , Germany ) as described previously[10] . Since we were particularly interested in SNPs with regulatory activity , we focused on SNPs located in putative transcription factor binding sites and microRNA target sites . To search for functional SNPs , we referred to Jaspar , UniPROBE , TRANSFAC , and PITA databases , then calculated the binding score of alleles to transcription factor , or the minimum hybridization energy and thermodynamics to microRNA as described [9] . Three SNPs in the promoter ( rs1143627 T>C , rs16944 G>A , rs1143623 G>C ) and one in 3-UTR ( rs2853550 T>C ) of the IL1B gene were genotyped using the MassARRAY system ( Sequenom , San Diego , CA ) as described elsewhere [9] , [10] . The relative height ( intensity ) of the peaks and the signal-to-noise ( SNR ) ratio were analyzed using Caller software to call genotypes in real-time . Typer software can apply cluster analysis to the genotype calls assigned by the Caller software . After cluster analysis , manual curation of spectra was performed to further validate the outcome . Assays with low call rates ( <90% ) were discarded or redesigned , i . e . , all assays shown in this study have a call rate of>90% . HRCT were performed at 10 mm section interval ( 120 kV , 50–450 mAs ) , ( 1 mm slice thickness , 1 . 5s scanning time ) with a window level between 2550 and 40 Hounsfield Units ( HU ) and window width between 300 and 1600 HU using the Toshiba Aquilion 64 CT Scanner ( Toshiba , Tokyo , Japan ) . HRCT scans were analyzed two independent chest radiologists and final conclusions on the findings were reached by consensus . The arbitrary scores were based on the percentage of lung parenchyma abnormality as previously described [36] , [37] . Human monocytic cell lines U937 ( ATCC CRL 1593 ) and HeLa cells ( strain S3 ) were cultured in antibiotic-free Dulbecco's modified Eagle medium ( DMEM ) containing 10% fetal bovine serum ( FBS ) ( Hyclone , Logan , UT , USA ) . After differentiation in the presence of phorbol 12-myristate 13-acetate ( PMA , 20 ng/ml ) for 24 hours , U937 cells were rested in fresh complete medium for 24 h before further stimulation . The differentiated U937 cells were exposed to heat-killed M . tuberculosis lysate ( 20 µg/mL ) , or live Mtb H37Ra strain at a multiplicity of infection of 10∶1 . The cells were harvested after additional 24 hours and used for gene expression assays or nuclear protein binding activity assays . Primary monocytes were isolated from PBMCs of 72 healthy controls ( n = 24 for each rs1143627 genotype ) by positive selection using magnetic CD14 MicroBeads ( Miltenyi Biotech , Germany ) according to the manufacturer's instructions . Purified CD14+ monocytes and PBMCs were transferred into a 96-well plate in serum-free AIM-V medium ( Gibco , Carlsbad , CA , USA ) , with 2×105 cells/well in the absence or presence of heat killed Mtb lysate ( 20 µg/mL ) or Mtb 19 kDa lipoprotein ( 0 . 5 µg/mL; Lionex GmbH , Braunschweig , Germany ) . The cells were harvested after 24 h or 48 h and used for gene expression assays , and the supernatant was collected to determine cytokine ( IL-1β , IL-1Ra , IFN-γ , and IL-17A ) production using ELISA . Total RNA extraction was performed with the RNAeasy Mini kit ( Qiagen , Valencia , CA ) , and residual DNA was digested using RNAse-free DNAse ( Qiagen ) . cDNA was synthesized using an oligo-dT primer and SuperScript II reverse transcriptase ( Invitrogen , Carlsbad , CA ) . Gene expression was measured using a previously described SYBR Green-based real-time quantitative PCR [69] . For all assays , target genes were normalized against the glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) level . The qPCR primers were designed as follows: for the IL1B gene: , 5'-TTCTTCGACACATGGGATAACG-3' ( forward ) and 5'-TGGAGAACACCACTTGTTGCT-3' ( reverse ) ; for the IL1RN gene: 5'-GGAAGATGTGCCTGTCCTGT-3' ( forward ) and 5'-TCTCGCTCAGGTCAGTGATG-3' ( reverse ) ; for the PU . 1 gene: 5'-CAGCTCTACCGCCACATGGA-3' ( forward ) and 5'-TAGGAGACCTGGTGGCCAAGA-3' ( reverse ) ; for the C/EBPB gene: 5'-AACTCTCTGCTTCTCCCTCTG-3' ( forward ) and 5'-AAGCCCGTAGGAACATCTTT-3' ( reverse ) ; for the TBP gene: 5'-TCTGGGATTGTACCGCAGC-3' ( forward ) and 5'-CGAAGTGCAATGGTCTTTAGG-3' ( reverse ) ; for the GAPDH gene: 5'-GCACCGTCAAGGCTGAGAAC-3' ( forward ) and 5'-TGGTGAAGACGCCAGTGGA-3' ( reverse ) . The levels of IL-1β , IL-1Ra , IFN-γ , IL-17A in the supernatants of stimulated PBMCs or CD14+ monocytes were determined using commercially available ELISA kits ( R&D , Minneapolis , MN ) , following the manufacturer's instructions . The human IL1B promoter ( −1292 to +79 ) was amplified by PCR and inserted into pGL3-Basic vector ( Promega ) upstream of the firefly luciferase coding region at XhoI and NcoI sites . Positive clones were subjected to site-directed mutagenesis using Quick Change Site-Directed mutagenesis Kit ( Stratagene , La Jolla , CA ) to obtain the desired alleles . Expression vectors for the full-length PU . 1 and C/EBPB were constructed by inserting the respective coding regions into pcDNA3 . 1 expression vector ( life technology , Carlsbad , CA ) at XhoI and EcoRI sites . The primer used for cloning were as follows: for PU . 1 , 5'-TATCTCGAGAACTTGTGCTGGCCCTGCAATG-3' ( forward ) and 5'-TATGAATTCTGTGGGGCGGGTGGCGCCGCT-3' ( reverse ) ; for C/EBPB , 5'-TATCTCGAGGAGTCAGAGCCGCGCACGGGACT-3' ( forward ) and 5'-TATGAATTCTGCAGTGGCCGGAGGAGGCGAGCAGGGGCT-3' ( reverse ) . HeLa cells ( 2×105 ) were plated in 24-well plates 24 h before transfection . A total of 0 . 8 ug plasmid DNA including 0 . 3 ug of either rs1143627 T or rs1143627 C reporter vector , 0 . 2 mg of pcDNA3 . 1-PU . 1 or pcDNA3 . 1-C/EBPB expression vector , and 0 . 1 mg of pRL-TK control vector , were co-transfected into the cells using Lipofectamine 2000 reagent ( life technology ) . Cells were stimulated by PMA ( 50 ng/ml ) for 20 h , following 24 h of transfection . Cells were then harvested and lysed in 150 uL passive lysis buffer ( Promega ) , the lysates were assayed for both the firefly and Renilla luciferase activities using the dual-luciferase reporter assay system ( Promega ) . Promoter activity was measured as the ratio between firefly and Renilla luciferase . The transfection for each construct was performed three times and each construct was assayed for promoter activity in duplicates . HeLa cells transfected with plasmid expressing PU . 1 or C/EBPβ were collected 16–20 h after transfection . The cells pellet was lysed using lysis buffer ( 10 mM Tris , pH 7 . 5 , 150 mM NaCl , 1% Triton X-100 , 1 mM phenylmethylsulfonyl fluoride , 0 . 2 mM sodium orthovanadate , 0 . 5% Nonidet P-40 ) supplemented with a cocktail of protease inhibitors ( Sigma-Aldrich , Steinheim , Germany ) . The lysates were separated by SDS-PAGE on a 12% polyacrylamide gel and then transferred onto a polyvinylidene difluoride membrane . The membranes were sequentially probed with the respective primary antibodies , followed by appropriate HRP-conjugated secondary antibodies ( Promega , Madison , WI ) and then visualized by exposure to X-ray films . Nuclear extracts were prepared from U937 monocytes as reported previously [70] . Two sets of complementary DNA-oligonucleotide sequences containing IL1B rs1143627 C or T allele were designed and biotin-labelled or left unlabelled ( Life technology ) . The oligonucleotides used were as follows: rs1143627C variant , 5'-CCTACTTCTGCTTTTGAAAGCCATAAAAACAGCGA GGGAGAAA-3'; and rs1143627T variant , 5'-CCTACTTCTGCTTTTGA AAGCTATAAAAACAGCGAGGAGAAA-3' . Equimolar amounts of each strand were combined in annealing buffer ( 10 mM Tris , 1 mM EDTA and 50 mM NaCl ) by heating to 95°C for 2 min , and cooling slowly to room temperature over 2 h . EMSA assays were performed by using the LightShift chemiluminescent EMSA kit ( Pierce , Rockford , IL ) . Binding reactions contained 20fmol biotin-labelled double- stranded probe , 2 . 0 uL nuclear extract and 1 . 0 ug poly ( dI:dC ) in a total volume of 20 uL binding buffer ( 10 mM Tris , 50 mM KCl , 3 mM MgCl2 , 0 . 1 mM EDTA and 1 . 0 mM DTT ) . After incubation for 20 min at room temperature , complexes were separated on a 6% native polyacrylamide gel , and blotted onto a positively charged nylon membrane ( Millipore , Billerica , MA ) , and visualized by exposure to X-ray films . EMSA images were quantified by densitometry using Quantity One , version 4 . 5 , software ( Bio-Rad ) . For relative quantification , the integrated optical density value was determined with background values taken below each band of interest to account for non-specific antibody staining in the lane . For competition experiments , a 200-fold molar excess of unlabelled probe was added prior to addition of labeled probe . To assess the putative binding sites within the IL1B promoter in the region encompassing the SNP rs1143627 , we used two bioinformatic tools , AliBaba2 and TFSearch , to predict the interaction of the different probes with proteins and set the cutoff for the dissimilarity matrix at 15% . In antibody supershift assays , anti-ZEB1 , MEF2 , C/EBPα , C/EBPβ , PU . 1 , TBP , and SP1 ( each at the final concentration of 1 ug/mL , Santa Cruz Biotechnology , Santa Cruz , CA ) were added after binding of nuclear extracts to labeled probe and incubated for 20 min at room temperature . A previously established in-house IFN-γ ELISPOT assay was used to measure Mtb antigen specific IFN-γ spot forming cells ( SFCs ) in peripheral blood samples from patients with TB [41] , [42] . Briefly , a total of 2×105 cells/well of PBMCs were cultured in duplicate in 96-well plates in the presence of ESAT6 protein ( protein ) or peptide pools derived from ESAT6/CFP10 ( peptides ) for 24 h . PBMCs cultured in medium alone or in the presence of phytohemagglutinin ( Sigma ) at 2 . 5 ug/ml were used as negative or positive controls , respectively . IFN-γ producing cells were visualized as spot forming cells after incubation with biotinylated anti–IFN-γ monoclonal antibody ( eBioscience ) for 4 h , followed by streptavidin-alkaline phosphatase conjugate for 2 h and then substrate solution . The number of SFCs was counted using an automated ELISPOT reader ( BioReader 4000 Pro-X; Biosys , Germany ) . The number of SFCs to protein and peptides was expressed as the number of SFCs per 0 . 2 million PBMCs . The background number of SFCs in the negative control well was subtracted . For knockdown of PU . 1 , or C/EBPB , we used lentiviral vectors expressing gene-specific small interfering RNA ( siRNA ) to specifically block expression . Oligonucleotide sequences of PU . 1 and C/EBPB specific siRNAs were as follows: PU . 1 siRNA , 5'-GCTTCGCCGAGAACAACTTCA-3' , C/EBPB siRNA , 5'-TTCTCCGAACGTGTCACGTTTC-3' . The stem-loop oligonucleotides were synthesized and cloned into a lentivirus-based vector carrying the green fluorescent protein ( GFP ) gene ( pGCSIL-GFP , Genechem , Shanghai , China ) . A universal sequence ( LV1-NC: 5'-TTCTCCGAACGT GTCACGT-3' ) was used as a negative control for RNA interference . Individual human shRNA lentiviral clones were prepared and isolated as previously described [71] . For infection with siRNA-carrying lentiviral vector constructs , the viruses were diluted in serum-free OptiMEM ( life technonogy ) and cells were infected at a multiplicity of infection of 10 for 3 h in the presence of 5 ug/mL polybrene . After 48 h of infection in serum-containing medium , cells were harvested and tested for PU . 1 or C/EBPβ expression by Western-blot . The Hardy-Weinberg Equilibrium ( HWE ) for IL1B polymorphisms distribution was analyzed in healthy controls and cases . The allelic and genotypic frequencies of SNPs between cases and controls were compared using the Pearson X2 test . The unconditional logistic regression adjusted by gender and age were performed to calculate the Odd ratios ( ORs ) , 95% confidence intervals ( CIs ) and corresponding P values under four alternative models ( multiplicative , additive , dominant and recessive ) . The one-way analysis of variance ( ANOVA ) /Newman-Keuls multiple comparison test was used for statistical analyses to compare the differences among multiple groups . The paired t test was used to compare the IFN-γ production with or without treatment of anti-IL-1β . Correlations were assessed using Spearman's rank correlation , and Pearson's t test was used to analyze the correlation . We used GraphPad Prism software ( version 4 . 0 ) for all the statistic analysis . Two-tailed statistical tests were conducted with a significance level of 0 . 05 . IL1B: [Homo sapiens ( human ) ] . NCBI Gene ID: 3553 . C/EBPB: [Homo sapiens ( human ) ] . NCBI Gene ID: 1051 . PU . 1: [Homo sapiens ( human ) ] . NCBI Gene ID: 6688 . TBP: [Homo sapiens ( human ) ] . NCBI Gene ID: 6908 . IL1RN: [Homo sapiens ( human ) ] . NCBI Gene ID: 3557 .
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IL-1β is important for the initial establishment of antimicrobial adaptive immunity , but prolonged IL-1β expression can also cause progressive immunopathology during M . tuberculosis infection . The paradoxical activities of IL-1β in promoting both antimycobacterial immunity and chronic tissue damage have left the ultimate contribution of this cytokine to TB progression in human populations unclear . In this work , we address the role of IL-1β-mediated inflammation using a combination of human genetics and molecular biology , and suggest that exuberant IL-1β responses are causatively associated with TB progression and poor treatment outcome in humans . This work furthers our understanding of the immunological factors that underlie TB disease and provide a strong rationale for the development of specific anti-inflammatory adjunctive therapies that could improve the long-term outcome of TB treatment . In addition , these insights inform the design of future TB control efforts that include the rational design of disease-preventing vaccines and genotype-targeted delivery of TB chemotherapy .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"bacterial",
"diseases",
"infectious",
"diseases",
"tuberculosis",
"medicine",
"and",
"health",
"sciences"
] |
2014
|
Allele-Specific Induction of IL-1β Expression by C/EBPβ and PU.1 Contributes to Increased Tuberculosis Susceptibility
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Beta-lactamases represent the main bacterial mechanism of resistance to beta-lactam antibiotics and are a significant challenge to modern medicine . We have developed an automated classification and analysis protocol that exploits structure- and sequence-based approaches and which allows us to propose a grouping of serine beta-lactamases that more consistently captures and rationalizes the existing three classification schemes: Classes , ( A , C and D , which vary in their implementation of the mechanism of action ) ; Types ( that largely reflect evolutionary distance measured by sequence similarity ) ; and Variant groups ( which largely correspond with the Bush-Jacoby clinical groups ) . Our analysis platform exploits a suite of in-house and public tools to identify Functional Determinants ( FDs ) , i . e . residue sites , responsible for conferring different phenotypes between different classes , different types and different variants . We focused on Class A beta-lactamases , the most highly populated and clinically relevant class , to identify FDs implicated in the distinct phenotypes associated with different Class A Types and Variants . We show that our FunFHMMer method can separate the known beta-lactamase classes and identify those positions likely to be responsible for the different implementations of the mechanism of action in these enzymes . Two novel algorithms , ASSP and SSPA , allow detection of FD sites likely to contribute to the broadening of the substrate profiles . Using our approaches , we recognise 151 Class A types in UniProt . Finally , we used our beta-lactamase FunFams and ASSP profiles to detect 4 novel Class A types in microbiome samples . Our platforms have been validated by literature studies , in silico analysis and some targeted experimental verification . Although developed for the serine beta-lactamases they could be used to classify and analyse any diverse protein superfamily where sub-families have diverged over both long and short evolutionary timescales .
In this article we demonstrate the value of different clustering and analysis platforms for classifying an important superfamily of bacterial proteins , the beta-lactamases . Our approaches are based largely on the sequence properties of the relatives although structural information is considered for some analyses . The purpose of the classification was to aid the identification of functional determinants ( FDs ) , i . e . residue sites influencing the functional properties of the relatives , where these properties relate to implementation of the catalytic mechanism or substrate profiles . In particular , we aimed to show that identification of these sites could aid in the prediction of phenotype for newly determined relatives not yet experimentally characterised . Beta-lactamases represent the main bacterial mechanism of resistance to beta-lactam antibiotics and are a significant challenge to modern medicine . Beta-lactam antibiotics are characterised by the possession of a four-atom beta-lactam ring , as shown in red in the main categories of antibiotics ( penicillins , cephalosporins , carbapenems and monobactams ) in Fig 1 . Beta-lactamases catalyse the hydrolysis of the bond between the nitrogen atom and the carbonyl group of the beta-lactam ring , breaking the ring open and thus inactivating the antibiotic . There is a large pool of naturally occurring beta-lactamases in environments such as the human gut that are selected for , mutated and transmitted horizontally into pathogenic bacteria following the introduction of new antibiotics [1] . All beta-lactamases are assigned the Enzyme Commission ( EC ) number 3 . 5 . 2 . 6 which is shorthand for “a member of the hydrolases , acting on carbon-nitrogen bonds , other than peptide bonds , in cyclic amides” . The EC functional classification scheme does not extend to more specific distinctions than this . The Gene Ontology ( GO ) [2] molecular function ontology term GO:0008800 represents “beta-lactamase activity” which is further subdivided into GO:0033250 “penicillinase activity” and GO:0033251 “cephalosporinase activity” . Both terms refer to activity against a broad range of chemically distinct antibiotics ( i . e . having different “R-groups” ) based on the penicillin and cephalosporin core structures shown in Fig 1a and 1c , which also includes ampicillin to illustrate an example penicillin “R-group” ( Fig 1b ) . There are also other beta-lactam antibiotic core structures , such as that possessed by carbapenems which are commonly reserved as antibiotics of last resort to combat multi-resistant bacteria ( see Fig 1d ) . The recent spread of carbapenemases , such as the New Delhi metallo-beta-lactamase NDM-1 is a cause for some alarm [3] . A frequently used term in the scientific literature , “broad spectrum” indicates that penicillins and cephalosporins are inactivated at the same rate , while the term “extended-spectrum” indicates the ability to inactivate third-generation cephalosporins with an oxyimino side chain as well as monobactams ( see Fig 1e ) . Inhibitors such as clavulanic acid inhibit the activity of some beta-lactamases and are often used in treatments in conjunction with beta-lactam antibiotics . An early classification of beta-lactamases by Ambler [4] , based on sequence comparison and preliminary structural data grouped beta-lactamases into classes A and B . A class A structure ( PDB 1BTL ) was experimentally determined in 1987 , providing structural evidence for the involvement of a key catalytic serine residue in the hydrolysis reaction [5] . In 1995 , the first class B structure was experimentally determined ( PDB 1BMC ) , which represented a new type of active site zinc-binding protein fold . Based on differences in sequence motifs , classes C and D have subsequently been added and revealed to possess the same protein fold and the same catalytic serine as the class A beta-lactamases . The single domain serine beta-lactamases ( Classes A , C and D ) are revealed by structural and catalytic residue similarity to be closely related to the beta-lactam antibiotic targets , the DD-peptidases ( also known as DD-transpeptidases ) . The serine beta-lactamases are thought to have evolved from the DD-peptidases about 2 billion years ago after fungi evolved the ability to synthesize beta-lactam antibiotics [6] . The DD-peptidases are involved in cross-linking bacterial cell walls , which is essential to their survival . The metallo-beta-lactamases ( Class B ) are a group of enzymes that are structurally unrelated to serine beta-lactamases and appear to have evolved independently of DD-peptidases [7] . Singh et al . [8] report a graph-based clustering of best bi-directional hits ( generated using BLASTP ) of beta-lactamase sequences that reproduces the four classes proposed by Ambler ( A , B , C and D ) . They also suggest the possibility of two additional small groups that they classify as E and F , which seem to be more closely related to class B metallo-beta-lactamases than to the serine beta-lactamases . An online database “Dlact” is also reported but this does not seem to be available at the time of writing . Two other online databases do provide some limited information about beta-lactamase antibiotic resistance specificity: the ARDB Antibiotic Resistance Genes Database ( http://ardb . cbcb . umd . edu/ ) [9] and the Beta-LActamase Database , BLAD ( http://www . blad . co . in ) [10] . Developing a simple tool or database for relating a sequence cluster or motif to antibiotic specificity is likely to be challenging . This is well illustrated by the Bush-Jacoby classification of beta-lactamase sub-types , where a different group can be assigned following the mutation of a single residue and by the study of Verma et al . [11] . In an extensive investigation of the physiochemical properties of class A beta-lactamases , Verma et al . [11] revealed that new antibiotic resistance activities , including those found in “extended-spectrum” beta-lactamases , are evolutionarily easy to achieve because they come about through small changes that do not globally affect structure nor the concomitant electrostatic properties ( e . g . electrostatic network , pairwise energies , electrostatic network composition , residue charge , and per residue pKa shifts ) . They do , however , report a statistically significant correlation between global protein charge and antibiotic resistance specificity . Guthrie et al . [12] also report success with a network model used to identify co-evolving residues within the class A type TEM beta-lactamases . Triple mutant combinations are found that increase cefotaxime resistance . Mandage et al . [13] analyse residue conservation on the surface of beta-lactamases using the ConSurf [14] server but this property does not appear to relate clearly to antibiotic resistance specificity . The Livesay group have developed a Distance Constraint Model ( DCM ) to examine changes in protein stability and flexibility and this been applied to proteins from Class C serine beta-lactamases [15] and metallo-beta-lactamases [16] . The goal of the work reported here is to analyse sequence features of serine beta-lactamases at different levels of classification: 1 ) ‘Classes’–distinguishing different implementations of the mechanism of action; 2 ) ‘Types’ or sequence clusters; and 3 ) ‘Variants’ , that provide a context within which to understand the subtle evolution of antibiotic resistance specificity . Our FunFHMMer algorithm [17] identifies functional families ( FunFams ) that distinguish well the Class A , C , D serine beta-lactamases . Subsequent clustering of the Class sequences , using CD-HIT [18] based on an optimal sequence identity cut-off , largely reproduces well-characterised types within the Class A serine beta-lactamases . To identify key functional positions ( e . g . catalytic residues ) and FDs that vary significantly between different types , we developed the novel Active Site Structural Profile ( ASSP ) algorithm , which exploits both structure and sequence and uses parsimony to characterise residues in the enzyme active site , which are likely to have a functional role . Over the last few decades , the introduction and overuse of Man-made antibiotics have driven the evolution of beta-lactamase variants with broader substrate profiles . In particular , novel variants in the Class A TEM-type are responsible for a significant proportion of clinically reported inhibitor resistance . We use another parsimony-based approach , Secondary Shell Parsimony Analysis ( SSPA ) , to identify driver mutations in serine beta-lactamase Class A variants that confer resistance to Man-made beta-lactam antibiotics and beta-lactamase inhibitors . We examine the locations of these variant mutations relative to the conserved core of the active site and the FDs that distinguish the different classes and types . In summary , we propose that the precise antibiotic resistance specificity and inhibitor resistance of serine beta-lactamases can be seen as a synthesis of various levels of classification: 1 ) implementation of the mechanism of action ( distinguishing A , C , D classes ) ; 2 ) a sequence cluster correlating with specificity ( beta-lactamase type ( s ) ) ; and 3 ) variant ( beta-lactamase sub-type ) . We focus mainly on the Class A beta-lactamases , the class which currently has most clinical relevance , and apply our classification approach to identify Class A beta-lactamase types in all complete bacterial genome sequences in our comprehensive CATH-Gene3D resource [19 , 20] . Our classification approaches are then applied to find and examine novel types in microbiome samples from human gut and drain .
It is already known that beta-lactamases fall into two distinct structural superfamilies and this is supported by the results of our structure comparisons using SSAP [21 , 22] . Classes A , C and D ( i . e . serine beta-lactamases ) are assigned to CATH DD-peptidase/Serine beta-lactamase superfamily , ( 3 . 40 . 710 . 10 ) , on the basis of both structural similarity and conservation of key catalytic residues in the active site . Class B metallo-beta-lactamases adopt a different structural fold and are assigned to CATH superfamily 3 . 60 . 15 . 10 ( see S1 Fig ) . The DD-peptidase/Serine Beta-Lactamase superfamily contains a large number of DD-peptidases . Although Class A , C and D beta-lactamases tend to have lower structural similarity with the DD-peptidases than with each other ( see S1 Table ) , there is conservation of the structural core across this superfamily . In particular , the active site and catalytic serine , which is found in both DD-peptidases and the Class A , C and D beta-lactamases , superpose well ( see S2 Fig ) . In this study we focus on the classification and analysis of serine beta-lactamases . Whilst S1 Table and S3 Fig show that structural similarity can be used to distinguish Class A , C and D beta-lactamases , most beta-lactamases in public repositories and discovered by metagenome studies have not been structurally characterised yet . Therefore , we developed sequence-based approaches to distinguish these classes . Serine beta-lactamases are thought to have evolved independently from the DD-peptidases three times ( i . e . Class A , C , D beta-lactamases ) more than 2 billion years ago [23] . We predicted 105 , 810 sequences from UniProt [24] and Ensembl [25] belonging to the CATH DD-peptidase/Beta-Lactamase superfamily ( 3 . 40 . 710 . 10 ) using our in-house Gene3D classification protocol [19 , 20] . This superfamily is moderately functionally diverse as summarised in S2 Table . All member domains are hydrolases and belong to three main “branches”: peptidase activity; hydrolase activity acting on carbon-nitrogen ( but not peptide ) bonds; and hydrolase activity acting on ester bonds . The region of the GO Molecular Function Ontology ( MFO ) Directed Acyclic Graph ( DAG ) that is encompassed by experimentally determined UniProt [24] annotations within this superfamily has eleven most specific terms , of which seven are leaf terms [2] . The DD-peptidases use the same mechanism of action as the beta-lactamases but the chemistry is a little different since an N-C peptide bond is being broken as opposed to a N-C bond in a cyclic amide . It is possible that the mechanism of action is as ancient as the fold itself and we expect similar mechanisms of actions are also used by the other main esterase “branch” in the GO molecular function ontology , as illustrated in S2 Table . All scissile bonds are characterised by delocalisation of electrons , which may be an essential feature of the mechanism of action . Thus , in this superfamily it appears that mechanism is the most conserved and evolutionarily ancient aspect , perhaps as old as the fold itself , and that its specific implementation , are secondary . Simple pairwise sequence approaches ( e . g . BLAST ) can be used to recognise homologues with very closely related sequences ( i . e . greater than 60% identity ) in each class of beta-lactamases . However , since distant relatives in each class can share less than 30% sequence identity ( see S3 Table ) more sensitive techniques are needed to distinguish classes . Our FunFHMMer protocol [17] sub-classified the superfamily into distinct functional families ( FunFams ) . Manual inspection of the UniProt [26] descriptions of the serine beta-lactamases confirmed that three FunFams captured well the three classes A , C and D respectively . Small manual adjustments result in complete agreement between FunFam classification and beta-lactamase classes . For each FunFam ( i . e . Class A , C , D ) we inspected the experimental annotations given in UniProt and removed those few sequences having non beta-lactamase annotations , e . g . having a DD-peptidase annotation . These comprised fewer than 2% of sequences within each FunFam . Two large and sequence diverse , functionally pure DD-peptidase FunFams are also automatically identified by FunFHMMer . Almost every domain sequence that can be assigned to the DD-peptidase/Serine beta-lactamase superfamily has an SXXK motif that maps to equivalent structural locations when the domain structures are superposed ( S2a Fig ) . There are 3 catalytic residues ( Ambler residues serine 70 , lysine 73 and lysine 234 ) that are common to all known DD-peptidases and beta-lactamases ( see S2b Fig ) . There have been a number of studies examining how residue differences in these proteins account for their diverse substrates ( linear versus cyclic peptides ) but the mechanistic roles of the residues remain unclear apart from a few relatives [23 , 27] . Within each serine beta-lactamase class relatives have diverged considerably in sequence identity and in their phenotypes , e . g . the ability to degrade different ranges of beta-lactam substrates . Several classification approaches have been used to distinguish relatives . In particular , ‘types’ are commonly referred to in the literature and these groups tend to be associated with particular substrate profiles and efficacies . Another approach , based more on clinical phenotypes , e . g . resistance to specific beta-lactamase inhibitors , is the Bush-Jacoby classification . However , it is not always clear from the literature that the identified types and Bush-Jacoby ( BJ ) classes have been identified using the same standardised experimental screening against an explicit repertoire of compounds . For that reason , we derived a classification protocol , the results of which matched the ‘types’ and ‘BJ classes’ reported in the literature as far as possible , but which exploits standard sequence-based approaches that would be easy to replicate by other biomedical researchers . Table 1 shows the sequence population of each serine beta-lactamase Class ( i . e . the number of Gene3D sequence counts ) and lists types that have been identified in the literature and that have at least ten annotated members , together with their UniProt annotations and a representative structural domain . We first considered the Class A ( 3 . 40 . 710 . 10 . blA ) and Class C ( 3 . 40 . 710 . 10 . blC ) FunFams as these are sufficiently sequence diverse to benefit from a sequence-based classification that could ultimately be used to characterise changes in functional residues likely to be modifying the phenotypes . Furthermore , the sequence diversity was sufficient for HMMs derived for these classes to be powerful enough to recognise both close and remote homologues in metagenome sequences . Because the Class C FunFam ( 3 . 40 . 710 . 10 . blC ) only contains one major clinically significant type ( and three sub-types ) we focused on the class A FunFam ( 3 . 40 . 710 . 10 . blA ) that contains fifteen clinically significant types and which , as we demonstrate here , contains sufficient sequence information to accurately characterise changes in functional residues in the active site . Because of their clinical significance , the type names: CTX-M , TEM , SHV , Z , L2 , KPC , OXY , PER , OKP , GES , LEN , CfxA , RAHN , CARB and PSE , or variations thereon , are frequently used in the UniProt descriptions of the protein sequences and thus provided a guide for automatically subdividing the Class A FunFam into types [26] . We have only considered well-populated types having at least ten annotated sequences in CATH-Gene3D . 1 , 321 out of 2 , 154 ( ~60% ) full-length Gene3D domain sequences assigned to the Class A FunFam are annotated with clinical type information in UniProt . CATH-Gene3D domain sequence intra- and inter-type pairwise sequence identities are derived from the full FunFam alignment and their distributions are shown in S4 Fig . Edit distance from the UniProt annotation of types ( number of split and merge operations ) is calculated for a range of sequence identity cut-offs used in CD-HIT clustering and a minimum is found at 60% sequence identity ( see S5 Table ) . Clustering with a 60% sequence identity cut-off is performed for all 2 , 154 Gene3D domain sequences and the resulting “cluster60” distributions of inter- and intra-cluster sequence identities are shown in Fig 4 . Using this cut-off , the 15 types highlighted in the literature fall into 9 cluster60s ( i . e . 9 predicted types , see Table 2 ) . The 60% cut-off for separation of function specificity are supported by other studies relating functional similarity to sequence identity [35 , 36] and may avoid over-fitting to the currently available annotation data and therefore over estimation of the number of types that can be found in nature . Where different types defined in the literature are merged into the same 60% sequence identity cluster , the Bush-Jacoby groups ( i . e . resistance phenotype ) associated with them tend to be very similar ( see Table 2 ) . Using the 60% threshold to cluster CATH-Gene3D sequences in the Class A FunFam into types , we identified 151 types of which 142 are new types not reported in the scientific literature ( ftp://ftp . biochem . ucl . ac . uk/pub/cath/v4_0_0/supplementary_files/151_types_uniprot_cath-gene3d . dat ) . In order to explore the differences between the Class A types and understand changes in their substrate specificities and efficacies , we developed a new approach ( the ASSP protocol , see Methods ) to landscape the active site characteristics of these different groupings . Although FunFHMMer can identify conserved sites differing between pairs of types , because there are 151 types an optimisation strategy is needed to identify the specific residues differing between all types . Furthermore , some types have few relatives to date , most of which are recently diverged . FunFHMMer’s entropy-based approach works best in distinguishing residue sites conserved differently between groups over significant evolutionary time-scales . Comparing types that have recently emerged is challenging , since many residues appear to be conserved sites over these much shorter time scales and need to be considered as possible FDs . To narrow down the number of residue sites to consider , ASSP exploits structural information and uses a parsimony based approach to explore different combinations of residues in the active site that could be influencing the substrate and resistance profiles . An initial Active Site Structural Profile ( ASSP ) was derived ( see Methods ) based on all 151 types identified in the CATH-Gene3D Class A FunFam . It comprised all those Ambler residues that lie within 8Å of the catalytic serine . This gave an ASSP with 31 positions . S6 Table shows the residues found at each ASSP position for each of the 9 predicted Class A types having clinical annotations in UniProt . For many positions in the ASSP , many types share the same residues . The next steps of the ASSP method find the smallest combination of residue positions in this original ASSP for which all Class A types have different residues . In other words , the smallest combination of residues best able to capture the active site diversity of Class A types . To do this we analysed first two-residue , then three-residue , up to N-residue permutations of the 31 residue positions in the first stage ASSP to identify unique configurations of residues between all the types ( see Methods for a schematic representation of the approach ) . For each N-residue configuration examined , the number of unique residue combinations across all types was counted . Subsequently , the distribution of these counts was plotted for each N . Z-scores ( minimum and maximum ) were calculated for each distribution ( i . e . from the maximum or minimum number of types observed ) . Fig 5 shows the distribution of the number of observed configurations in the 151 types , for all 4 , 495 three-position ( triplet ) permutations of the 31 positions in the first stage ASSP . Minimum and maximum Z-scores for configurations of up to eight positions ( N = 8 ) are shown in Table 3 and it can be seen in Fig 6 that 7 residues in the configuration are necessary to fully distinguish all of the Class A types . The highest maximum Z-score occurs for a triplet configuration ( N = 3 ) . Although not all types have a unique configuration until N = 7 ( see S1 Text for ASSP N = 7 residue configuration and S5 Fig for the N = 7 functionally important positions highlighted in the Class A serine beta-lactamase domain ) , the maximum Z-score for this number of residues in the configuration is not very significant , i . e . finding a unique configuration of these number of residues is not very unlikely . The line in Fig 6a rises steeply up to N = 3 but then takes a long time to level off and a triplet configuration distinguishes 114/151 ( 75% ) of the predicted types with a highly statistically significant Z-score of 3 . 86 . The lowest minimum Z-score for the configuration which captures positions common to all types is difficult to identify as the algorithm has not converged by 8 positions and is too computationally expensive to proceed to higher numbers of positions . Based on the highest maximum Z-score in Table 3 , FDs distinguishing between the types are given by a triplet consisting of Ambler positions 74 , 129 , and 244 . We assume that this configuration of positions has been under strong selective pressure for long evolutionary periods to efficiently inactivate the wide variety of beta-lactam antibiotics that have been produced by fungi . The 8 positions giving the lowest minimum Z-score achieved in our analysis ( i . e . residues conserved between all types which should include the known catalytic residues ) together with the 3 positions likely to be FDs and differing in their composition between most of the types , are shown in Table 4 below . The Type 1 Class beta-lactamases , which include the TEMs , are a highly populated type , capturing a significant proportion of clinically characterised beta-lactamases . Recent divergence of these enzymes has given rise to relatives with extended-spectrum beta-lactam resistance ( i . e . ability to inactivate third-generation cephalosporins with an oxyimino side chain as well as monobactams ) and inhibitor resistance ( e . g . resistant to the inhibitors Clavulanic acid and Sulbactam ) . We were interested in exploring the mutations responsible for these clinically significant phenotypes . In this case , we are dealing with very recent divergence and many residue positions will appear conserved across the TEMs . Here , we wished to determine which mutations occurring in a variant TEM sequence , were contributing to the phenotype . However , reports in the literature of multiple driver mutations , some occurring remote from the active site ( see S8 Table ) , meant that we could not restrict our analysis to active sites residues . We therefore developed another parsimony-based approach to identify driver mutations likely to be conferring these phenotypes . We validated our approach by examining how well our predictions agreed with experimentally confirmed genotype-phenotype data in the literature . As well as using our approaches to analyse sites implicated in beta-lactam resistance , we also applied FunFHMMer and ASSP to search for novel Class A types in metagenomes sampled from human gut and a bathroom drain environment . Although BLAST can be used to detect known types ( i . e . sequences having greater than 60% sequence identity to one of the Class A types identified using the CD-HIT clustering above ) , novel Class A types ( i . e . having < 60% sequence identity ) are difficult to distinguish from Class D beta-lactamases . Furthermore , microbiome sequences are sometimes incomplete and a preliminary analysis of BLAST matches revealed incomplete sequences with > 60% identity to a Class A beta-lactamase but lacking fragments of sequence containing the catalytic or FD residues , making it impossible to identify the type . Therefore , we used FunFHMMer to identify very safe matches within these microbiomes , which could then be subjected to experimental validation . Sequences taken from thirteen human gut microbiomes ( see Methods for details ) were scanned against the HMM for the Class A FunFam using FunFHMMer . This identified 136 full length matches to Class A . These human gut microbiome beta-lactamase sequences clustered into 8 types , of which 7 were previously identified by our classification of Class A types above , and 3 of those 7 had clinical annotations . Therefore , 1 out of the 8 types found in gut microbiome sequences is novel , suggesting a reasonable level of novelty in the human gut metagenome . This new cluster , which is a singleton , has a unique FD triplet , FEV . However , the sequence lacked a signal peptide , suggesting that it may have evolved a different function and therefore it was not tested for activity . Scans of sequences from our in-house drain metagenome data against the Class A FunFam HMM identified one match . This had 37% sequence identity to the closest Class A beta-lactamase in our CATH-Gene3D dataset , marking it out as a novel type . This was confirmed by the detection of a unique FD triplet , IQA ( combination 1 in Table 8 ) . This sequence was cloned and expressed in E . coli , and its activity was tested against a range of beta-lactam compounds known to be acted on by Class A beta-lactamases ( see Methods ) . For this purpose , a qualitative agar-diffusion test was performed with the following antibiotics: amoxicillin , ampicillin , oxacillin , cloxacillin and carbenicillin at concentrations of 2 , 5 , 10 and 20 μg/ml . The size of zone of inhibition around 10 and 20 μg/ml of amoxicillin suggested that both with the native signal and the pelB signal , candidate beta-lactamase could give resistance to this antibiotic and that the one with native signal has higher activity . 5 different concentrations of amoxicillin were then tested: 10 ( the lowest concentration that inhibited growth ) , 15 , 20 , 25 , 30 μg/ml , all of which gave positive results . The agar-diffusion test was also performed with higher concentrations of ampicillin , oxacillin , cloxacillin and carbenicillin: 10 , 20 , 25 , 50 μg/ml . The size of zones of inhibition suggests that the candidate beta-lactamase could also give resistance to ampicillin , again the protein with native signal has higher activity . The lowest concentration that inhibited growth was 25 μg/ml of ampicillin . We were surprised that so few Class A matches were found in the drain microbiome sample . However , this could reflect the fact that the sequence samples lack important regions of the sequence and therefore fail to meet the strict Class A FunFam HMM inclusion threshold . We therefore examined 14 matches which failed to meet the inclusion threshold but which gave high scores against the Class A FunFam and significantly higher matches to Class A FunFams than to DD peptidases , Class C or Class D beta-lactamases . These putative matches were examined for the following criteria: 1 ) contained all three motif regions identified by FunFHMMer for Class A beta-lactamases ( see Methods for details ) , 2 ) contained a new combination of FD residues , and 3 ) had a bit score very close to the Class A inclusion threshold and very far from the DD-peptidase , and Class C and D inclusion thresholds . Three unique combinations of the FDs were found ( see combinations 2 , 3 and 4 in Table 8 ) suggesting that there are potentially three further novel types within this microbiome .
In conclusion , we have constructed a classification and analysis platform for beta-lactamases that applies a number of structure and sequence-based algorithms to distinguish beta-lactamases from DD-peptidases and to sub-classify classes and types of serine beta-lactamases . Importantly , our protocols search for residue sites likely to be exerting an influence on the function . This could relate to implementation of the catalytic mechanism or to the substrate profile . Our protocols provide a strategy for recognising previously unreported ‘types’ , which could have novel resistance profiles and reveal emerging resistance to new drug regimes . Although sequences sharing high sequence similarity ( > 60% ) to known serine beta-lactamases can easily be recognised by BLAST , in the twilight zone of sequence identity ( < 30% ) it is difficult to distinguish different classes of serine beta-lactamases from each other and from the DD-peptidases . Structural analyses can provide important clues , as we and others have reported , but few of the sequences emerging from high throughput studies e . g . metagenome studies , have structural data . Therefore , our classification pipeline focused mainly on sequence data . Our FunFHMMer derived FunFams for the Class A , C and D beta-lactamases allowed us to recognise even very distant relatives of these beta-lactamase classes ( < 20% sequence identity ) as they capture distinct residue patterns associated with each class . Our results show that FunFHMMer was not only able to distinguish sequences with the beta-lactamase Gene Ontology ( GO ) term from sequences coming from other conflicting GO Molecular Function “branches” in the DD-peptidase superfamily , but also to separate FunFams corresponding to different implementations of the mechanism of beta-lactamase action i . e . separate the Class A , C and D beta-lactamases . Detailed analysis of the Functional Determinant ( FD ) residues differing between these classes revealed residue positions likely to be contributing to differences in the implementation of the catalytic mechanism . Many of these positions are validated by reports in the literature . Other FDs revealed by our method suggest sites that could be targeted to gain better understanding of the determinants separating the classes from each other and from the DD-peptidases . The Class A beta-lactamases are the largest and most diverse class , responsible for most of the resistance to clinically relevant beta-lactams . We therefore decided to perform more detailed analyses of this Class . Fifteen clinically relevant types are reported in the literature , having largely different substrate profiles . However , it is not clear whether these assignments are based on standardised compound screening protocols . We found that using a sequence identity threshold of 60% , a value that corresponds to other studies identifying functionally related proteins [35 , 36] , we obtained a good separation of the clinically reported types that also largely corresponded to similarity of Bush-Jacoby groups within each predicted type . Applying this threshold identified 151 types amongst the UniProt and Ensembl sequences assigned to the Class A FunFam in CATH-Gene3D , 142 more than reported in the literature . Again , by revealing specific residue sites differing between the types and likely to be influencing the phenotypes ( i . e . substrate profiles ) we can provide a more refined analysis tool for classifying these types . FunFHMMer was not so suited to this task since some types are very recently diverged and because it is not designed to identify residues differing across multiple groups . We found that a simpler parsimony based approach ( ASSP ) , that focused on residues close to the active site , could be used to find these FDs . Our ASSP predictions of catalytic sites showed significant agreement with catalytic positions reported in the literature , and the putative FDs were shown to be located very close to the catalytic residues or in the secondary shell . Further studies using docked substrates and using a substrate bound to an inactive mutant supported proximity of the FDs to the beta-lactam substrate . One of the positions makes a hydrogen bond with the beta-lactam and there are reports in the literature of its involvement with the catalytic activity . The other positions are more remote from the catalytic residues but located within the secondary shell of the active site where they may influence conformational rearrangements necessary to support changes along the reaction pathway . Finally , we analysed variants in the TEM-Type Class A beta-lactamases , the type responsible for much of the clinically relevant resistance to beta-lactams . Again , the fact that some of these variants or ‘subtypes’ emerged very recently and that some driver mutations have been found quite far from the active site meant that a new strategy was needed . SSPA is not restricted to sites close to catalytic residues but examines all mutations . Validation against positions reported in the literature , showed that SSPA successfully identified 5 sites known to be associated with inhibitor resistance and 5 known to be associated with extended-spectrum resistance phenotype . Inspection of the SSPA predictions in 3D showed that many SSPA sites not yet experimentally verified lie close to ‘hot regions’ which are lying in or near the active site , or close to the omega loop which is thought to have a functional role . We tested the validity of our SSPA approach by applying it to an important subtype in the beta-lactamase TEMs , i . e . mutants having a 2be phenotype in the Bush-Jacoby classification . However , the success of SSPA in identifying previously experimentally characterised sites suggests that it would be useful to apply SSPA to other subtypes which have sufficient genotype-phenotype data necessary for this approach . We tested the ability of our Class A FunFam to recognise Class A serine beta-lactamases in two microbiome samples . A putative novel type was identified in the drain microbiome , which met the Class A FunFam inclusion threshold but which was likely to be a novel type as it shared less than 40% sequence identity to any Class A beta-lactamase in our Gene3D dataset and contained a unique FD triplet . Experimental validation confirmed its resistance to a range of compounds associated with Class A beta-lactamase activity . Much more extensive screening work can now be done to comprehensively explore its substrate range and how that differs from other known types . Because of the stringency of the FunFam inclusion threshold , and the general poor quality of the metagenome sequences the matches reported in this study actually only represent about 2% of all the significant matches ( E-value ≤ 0 . 0001 ) that were found . Manual analysis of a sample of these missed significant matches showed that fragments with key catalytic or FD residues were missing from the sequence . If the metagenomic data were of better quality , then we might reasonably expect to see at least an order of magnitude more novel beta-lactamase clusters . In summary , we have developed a classification and analysis platform that allows us to separate relatives within the serine beta-lactamase superfamily according to their implementation of the mechanism of action and their substrate profiles . Our FunFHMMer method can separate the known beta-lactamase classes and identify those positions likely to be responsible for the different implementations of the mechanism of action in these enzymes , which emerged independently from DD-peptidases , three times during evolution . The ASSP algorithm detects FD sites which can help to classify the different Class A Types , whilst the SSPA algorithm detected sites conferring inhibitor resistance or extended-spectrum resistance phenotypes . Each algorithm has specific features designed to suit the nature of the dataset being analysed . The FDs that we recognise can be used as fingerprints to classify new relatives and predict their likely resistance profiles . We tested the predictive value of our classification by uncovering and experimentally verifying a new Class A Type within a drain microbiome ie having a unique fingerprint of FD residues . Finally , our parsimony based approaches for identifying FDs and for distinguishing driver from passenger mutations could obviously be applied to other protein superfamilies and one can imagine other medical applications where resistance to chemical challenges has emerged recently in evolution . For example , kinases implicated in certain cancers , which evolve resistance to drugs , and where residue configurations close to catalytic residues or other functional sites e . g . activation loops , could be analysed to detect driver mutations associated with different phenotypes , such as responses to drug treatments . Our functional family classification and analysis pipeline provides a strategy for detecting residue sites playing a functional role in the emergence of new phenotypes .
Domain structure representatives for each of the Class A , B , C and D beta-lactamases , and DD-peptidases were selected from our in-house CATH classification of protein domain superfamilies [20] . Each structural domain pair was compared using the in-house SSAP structure comparison algorithm [21 , 22] . The SSAP algorithm uses a well-established double dynamic programming algorithm to identify a reliable residue alignment between each pair of structures . A SSAP score is returned in the range of 0 to 100 , where 100 indicates identical structures . The SSAP alignment was used as input to the ProFit algorithm ( Martin , A . C . R . , http://www . bioinf . org . uk/software/profit/ ) , which superimposes the structures and calculates their RMSD . For our analysis of beta-lactamase proteins we used the dataset of protein domains classified in our in-house Gene3D resource [19] . Gene3D is a sister resource of CATH [20] and version 12 comprises nearly 50 million domain sequences from UniProt version 2013_02 and Ensembl version 70 , predicted to belong to CATH superfamilies . Domain sequences are assigned to a particular CATH superfamily following hmmscan scans against superfamily HMMs built from representative sequences ( 17 ) . An in-house automatic function classification method FunFHMMer [17] was used to sub-classify the CATH-Gene3D DD-peptidase/serine beta-lactamase superfamily into distinct functional families ( FunFams ) . The superfamily sequences are initially clustered using the GeMMA agglomerative clustering algorithm [50] that creates a hierarchical tree of sequence relationships within the superfamily . GeMMA clusters close sequence relatives into starting clusters using CD-HIT [18] . Multiple sequence alignments for each starting cluster are built using MAFFT [51] . GeMMA then performs an iterative all-against-all profile-profile comparison of a set of clusters using COMPASS [52] followed by merging of the most similar clusters and realignment of the merged clusters by MAFFT . This iterative process continues until one cluster remains . The merging order is then used to build a hierarchical tree from the leaf nodes to the root rode . Once the tree has been generated , functional families ( FunFams ) are identified by FunFHMMer , which partitions the tree based on the identification of positions which are differentially conserved in different FunFams . Thresholds for partitioning superfamily trees have been optimised by validation against experimentally determined functions and functional sites [17] . Once FunFams have been identified , HMM profiles are built for each FunFam using HMMER version 3 [53] . Putative serine beta-lactamases can be identified by scanning query sequences against the Class A , C , D FunFam HMMs . Sequences are assigned to a particular FunFam provided they return a bit score that is greater than or equal to the inclusion threshold for that FunFam ( 14 ) . FunFHMMer has been validated in silico [17] and independently validated for its performance in function prediction , ranking in the top 5 ( out of 126 methods ) in the international Critical Assessment of Protein Function Annotation [54] ( CAFA ) 2 experiment ( Radivojac , P . , personal communication ) . FunFHMMer exploits the GroupSim [55] method to detect residue sites that are differentially conserved between FunFams . It was used to report sites differentially conserved between Class A , C , D FunFams and thus likely to play a functional role [17 , 56] . GroupSim takes an alignment containing pre-defined functional groups as input and provides a prediction score for each column in the alignment . The score ranges from 0 to 1 , where any position in the alignment having a score greater than 0 . 65 may be a functional determinant ( FD ) [17] . To identify key FD residues between the three serine beta-lactamase classes ( A , C and D ) we built a three-way structural alignment of the corresponding FunFams . This was done by selecting representative sequences ( at 60% sequence identity ) , with known structure , from each class and constructing a multiple alignment by performing successive pairwise structure alignments against the representative that best matches all other representatives . After this , hmmbuild from the HMMER package [53] was used to create an HMM for the structure-based alignment . Sequence relatives from the Class A , C , D FunFams were then aligned to the HMM using the hmmalign command from the HMMER package [53] . The resulting structure-based sequence alignment was then used for site analysis by applying GroupSim [55] . To sub-classify relatives in the serine beta-lactamase Class A FunFam into clusters corresponding to ‘types’ identified in the literature , the CD-HIT [18] algorithm was used . CD-HIT can very rapidly cluster protein sequences according to sequence identity at levels of similarity above about 40% . It is widely used in computational biology due to its speed and the reliability of its results . In order to help understand the evolution of beta-lactamases , we characterised the extent and nature of the active site by the construction of Active Site Structural Profiles ( ASSPs ) . These structure-based profiles were applied to the Class A serine beta-lactamases and first capture all residues within a threshold distance of well-characterised catalytic residues reported in the scientific literature . Subsequently , a parsimony-based approach identifies those residues ( FDs ) likely to have a role in modifying functional features between types . This approach helped to distinguish differences in key residue sites between Class A serine beta-lactamase types . We decided to apply structural criteria in ASSP as a number of other methods have successfully explored residues lying close to catalytic residues to detect additional functionally important sites . For example , JESS [57] , uses an initial active site template ( constituting 2–5 amino acid residues ) from the Catalytic Site Atlas ( CSA ) [58] to search for similar conformations of residues in other protein structures . For putative matches , residues within a 10 Å sphere are compared to calculate a local similarity score ( SiteSeer score ) that is used to rank the template match [59] . Similarly , the Evolutionary Trace method [60] identifies functionally important residues by partitioning a phylogenetic tree to identify subfamilies and focusing on highly conserved residues that lie within 4 Å of each other . Whilst the subfamily classification method , DASP ( Deacon Active Site Profiler ) [61 , 62] , selects all residues within a 10 Å sphere of known catalytic residues which are then concatenated to build a structure based profile . Structural relatives having similar profiles are clustered into subfamilies and the subfamily profiles subsequently transformed into PSSMs and used to identify sequence relatives . The first stage in the construction of the ASSPs is the analysis of the PDB data of a representative structure for the FunFam . The PDB 1SHV was chosen as the representative structure for ASSP analysis . This structure satisfied a number of criteria: it had a high score when compared to the HMM representing the FunFam; it is a wild-type sequence; it was expressed in reasonably physiological-like experimental conditions; and it has no bound ligand . 1SHV not only satisfied all of these criteria but its use of the standard Ambler residue numbering scheme helped with reference to the literature and analysis of mutation and phenotype data [63] . Details of the construction of the initial ASSP and its processing to produce the final ASSP is described in Figs 11 and 12 . The first plasmid borne beta-lactamase was identified in E . coli in Greece in 1963 and was named “TEM” after the patient from whom it was isolated [65] . Today it is the most commonly encountered beta-lactamase in Gram-negative bacteria and the TEM-1 subtype accounts for up to 90% of ampicillin resistance in E . coli . Mutation and phenotype data for variant TEM beta-lactamases are made available in Supporting Information by Guthrie et al . [12] . A parsimony-based approach was applied to this Guthrie dataset to distinguish driver from passenger mutations associated with the inhibitor-resistant ( e . g . Clavulanic acid and Sulbactam ) and extended-spectrum phenotypes ( i . e . resistant to penicillins , cephalosporins and third-generation cephalosporins ) . SSPA matrices were created for each of the two phenotypes where each column in the matrix represents a residue position where a mutation is found relative to the consensus sequence of the multiple alignment of all the variant TEM beta-lactamase sequences . Each variant possessing a distinct phenotype ( i . e . inhibitor-resistant [12 , 30 , 32 , 42] or extended-spectrum phenotype [12 , 42–49] ) occupies a row in the matrix . We then determine the minimum number of columns ( i . e . putative driver mutations ) for which one or more of these positions is mutated in every variant with a phenotype . To identify novel Class A types we analysed two different microbiomes–gut and drain . Metagenome sequences were scanned against the Class A FunFam HMM . Sequences assigned to the Class A ( i . e . meeting the inclusion threshold for the FunFam ) were then compared against the sequences classified into the 151 types identified in this class to identify novel types having less than 60% sequence identity to sequences in any of these types . Pre-processed gut metagenome sequences were obtained from the MG-RAST [66] and EBI Metagenomics [67] resources ( S12 Table ) . Some of the MG-RAST and EBI microbiomes were already partially assembled into contigs but where this was not the case , MetaVelvet [68] was used for assembly to increase the chance of finding complete beta-lactamase domain sequences . Additional metagenome data derived from a bathroom drain and sequenced using Illumina MiSeq technology was processed by the Ward group at UCL ( deposited in the EBI Metagenomics resource under project ID ERP011520 ) . The paired-end reads were quality assessed and filtered using the Paired-End ToolKit ( PETKit version 1 . 1b , http://microbiology . se/software/petkit/ ) . Contiguous read assembly was performed on the clean reads using IDBA-UD [69] . Contig sequences were translated into protein sequences using a 6-frame translation with the tool Transeq from EMBOSS v6 . 6 . 0 . 0 [70] . Open-reading frames were predicted using Prodigal v2 . 6 . 2 [71] . Gene sequences from the drain environment and contig sequences from the human gut environments were scanned by FunFHMMer [17] against HMMs from the DD-peptidase/Serine beta-lactamase superfamily . If the resulting bit-score was greater than or equal to the inclusion threshold , the sequence was assigned to that FunFam [17] . Any sequence that was less than 80% of the average length of all sequences assigned to the FunFam was deemed a fragment and filtered out . Sequences sharing less than 60% sequence identity to any of the CATH-Gene3D Class A serine beta-lactamases were selected as potential novel types . To further refine matches likely to be novel types , metagenome-derived sequences giving a significant match to the Class A FunFam were aligned to the existing Class A alignment using the MAFFT algorithm [72] . Sequences long enough to contain the three main functional motifs [27 , 30] in Class A beta-lactamases , and capturing all the serine beta-lactamase catalytic residues ( Motif 1: Ambler nos . 70–73 ( SXXK ) ; Motif 2: Ambler nos . 130–132 ( SDN loop ) ; Motif 3: Ambler nos . 234–236 ( K[T/S]G ) ) and the FDs identified by the ASSP method ( Ambler residue nos . 74 , 129 and 244 ) were examined closely to analyse changes in residues . Those having a novel combination of the three FDs distinguishing the types , and not observed in any of the types classified in CATH-Gene3D [19] were considered for experimental validation . A predicted gene encoding beta-lactamase , bla-29843 , was amplified directly from the drain metagenomic DNA by a two–step PCR using a Phusion High-Fidelity DNA Polymerase ( NEB ) and conditions suggested by the manufacturer . The following PCR primers were used: forward , 5’- CATATGCGACGCGCCTCTCTCGTG– 3’ and reverse , 5’–GCGGCCGCGTTGACGGTAAGGAAATGGTCGTAAGCG– 3’ . The blunt-ended PCR product was ligated into pCR-Blunt vector with a Zero Blunt PCR Cloning Kit ( Invitrogen ) followed by the transformation into chemically competent E . coli DH5α . pCR-Blunt vector containing bla-29843 gene was confirmed by DNA sequencing . This vector was further used as a template for PCR amplification with primers designed to incorporate 5′ NdeI restriction site followed by a pelB leader sequence and a 3′ NotI restriction site . The N-terminal pelB leader sequence was added to enable the periplasmic secretion of beta-lactamase via the Sec translocation machinery . Two PCR products were generated for bla-29843 , one with its native N-terminal signal sequence and the other with the pelB leader sequence instead . The following PCR primers were used: ( i ) forward and reverse primers for bla-29843 with the native signal sequence were 5’- CATATGCGACGCGCCTCTCTCGTG—3’ and 5’—GCGGCCGCGTTGACGGTAAGGAAATGGTCGTAAGCG—3’ ( ii ) forward and reverse primers for bla-29843 with pelB sequence were 5’- TATACATATGAAATACCTGCTGCCGACCGCTGCTGCTGGTCTGCTGCTCCTCGCTGCCCAGCCGGCGATGGCCATGGCACCCGCAACAACGATCGCG– 3’ and 5’–GCGGCCGCGTTGACGGTAAGGAAATGGTCGTAAGCG– 3’ . PCR products were purified and restriction cloned into NdeI and NotI sites of the bacterial expression vector pET-29a ( + ) ( Novagen ) . The resulting vectors encode beta-lactamases containing an N-terminal leader sequence and a C-terminal poly-histidine tag preceded by 5 amino acids . Expression of beta-lactamases was carried out in BL21 ( DE3 ) pLysS E . coli cells ( Invitrogen ) harbouring pET29a- beta-lactamases vectors described above . To test susceptibility to antibiotics , diffusion in solid agar was used . All antibiotics ( amoxicillin , ampicillin , oxacillin , cloxacillin , kanamycin ) were purchased from Sigma except carbenicillin that was purchased from Invitrogen . Bacteria for lawn seeding were grown overnight at 37°C with shaking in Luria-Bertani ( LB ) medium supplemented with 50 μg/ml of kanamycin . Inoculum was spread on solid LB agar plates supplemented with 1mM IPTG . Holes were punched with a plastic tip and filled with the same amount of antibiotic solutions . Plates from three independent replicates were analyzed individually for the inhibition zone diameter . BL21 ( DE3 ) pLysS E . coli cells carrying an empty pET29a vector were used as a negative control .
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Beta-lactamases are bacterial proteins largely responsible for resistance to beta-lactam antibiotics and so pose a significant challenge to modern medicine . Whilst there are many studies cataloguing beta-lactamases , antibiotic screening has not always been consistent or comprehensive , causing confusion in the classification of these proteins and difficulty in recognising bacteria with different resistance profiles . We therefore developed strategies for automatically and consistently classifying distinct classes and types of beta-lactamases , having particular antibiotic resistance profiles . Our methods focus mainly on the sequences of the beta-lactamases , as for most new bacterial strains we will only know the sequence . We have classified all sequenced beta-lactamases stored in major public repositories into classes . We then mainly focus on the Class A beta-lactamases as these are responsible for most of the resistance to clinically relevant antibiotics . We applied methods to pinpoint key sequence sites where changes result in new antibiotic resistance properties . Understanding which sites confer resistance is important for recognizing whether new evolving strains can evade current antibiotic regimes . Our classification methods allowed us to classify 151 Class A serine beta-lactamase types and to recognize a new type of Class A beta-lactamase in a bacteria found in a drain sample .
|
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2016
|
Novel Computational Protocols for Functionally Classifying and Characterising Serine Beta-Lactamases
|
Helicobacter pylori lipopolysaccharide promotes chronic gastric colonisation through O-antigen host mimicry and resistance to mucosal antimicrobial peptides mediated primarily by modifications of the lipid A . The structural organisation of the core and O-antigen domains of H . pylori lipopolysaccharide remains unclear , as the O-antigen attachment site has still to be identified experimentally . Here , structural investigations of lipopolysaccharides purified from two wild-type strains and the O-antigen ligase mutant revealed that the H . pylori core-oligosaccharide domain is a short conserved hexasaccharide ( Glc-Gal-DD-Hep-LD-Hep-LD-Hep-KDO ) decorated with the O-antigen domain encompassing a conserved trisaccharide ( -DD-Hep-Fuc-GlcNAc- ) and variable glucan , heptan and Lewis antigens . Furthermore , the putative heptosyltransferase HP1284 was found to be required for the transfer of the third heptose residue to the core-oligosaccharide . Interestingly , mutation of HP1284 did not affect the ligation of the O-antigen and resulted in the attachment of the O-antigen onto an incomplete core-oligosaccharide missing the third heptose and the adjoining Glc-Gal residues . Mutants deficient in either HP1284 or O-antigen ligase displayed a moderate increase in susceptibility to polymyxin B but were unable to colonise the mouse gastric mucosa . Finally , mapping mutagenesis and colonisation data of previous studies onto the redefined organisation of H . pylori lipopolysaccharide revealed that only the conserved motifs were essential for colonisation . In conclusion , H . pylori lipopolysaccharide is missing the canonical inner and outer core organisation . Instead it displays a short core and a longer O-antigen encompassing residues previously assigned as the outer core domain . The redefinition of H . pylori lipopolysaccharide domains warrants future studies to dissect the role of each domain in host-pathogen interactions . Also enzymes involved in the assembly of the conserved core structure , such as HP1284 , could be attractive targets for the design of new therapeutic agents for managing persistent H . pylori infection causing peptic ulcers and gastric cancer .
Helicobacter pylori is well-adapted to survival in the human stomach mucosa and establishes persistent infection , which causes chronic gastritis and can lead to peptic ulcer disease and gastric adenocarcinoma [1 , 2] . Lipopolysaccharide ( LPS ) , a highly acylated glycolipid compactly anchored in the outer leaflet of the outer membrane ( OM ) , is a key factor for H . pylori to establish colonisation and persistence in the gastric niche [3–7] . As a constituent biomolecule of most Gram-negative bacteria , the LPS is typically composed of three domains: the hydrophobic lipid A ( or endotoxin ) , which anchors the molecule in the OM; the variable O-antigen extending from the cell to the external environment; and the core-oligosaccharide ( which can be further divided into the inner and outer core ) , which links the O-antigen to the lipid A [8] . H . pylori constitutively modifies the de novo synthesized bi-phosphorylated and hexa-acylated KDO2-lipid A into a mono-phosphorylated and tetra-acylated KDO-lipid A to evade host immune surveillance and establish a persistent colonisation [9] . This unique lipid A structure confers H . pylori with the ability to resist cationic antimicrobial peptides ( CAMPs ) , and to evade Toll-like receptor 4 ( TLR-4 ) recognition [9] . In addition , the O-antigen of H . pylori LPS contains fucosylated oligosaccharides that mimic human Lewis antigens [5 , 10–12] . H . pylori is known to extensively vary its Lewis antigen expression pattern in vivo , which also contributes to its ability to evade host immune detection and adapt to the host environment during persistent infection [5] . Our group has recently summarised the studied LPS structure and biosynthesis in H . pylori [13] . Being the first H . pylori strain with complete genome sequencing [14] , the LPS structure of H . pylori strain 26695 is the most-studied and best-characterized [15–17] . The lipid A and Lewis antigens of H . pylori LPS have been well-characterised in terms of biosynthesis , structure and function [13] . However additional work is needed in regard to the characterization of the core-oligosaccharide domain . Similar to other Gram-negative bacteria [8] , the core-oligosaccharide domain of 26695 is conceptually divided into two parts , the inner core and outer core [13] . The inner core is built as a conserved hexasaccharide ( Glc-Gal-DD-Hep-LD-Hep-LD-Hep-KDO ) and the first two LD-Hep residues ( designated as Hep I and Hep II ) are added sequentially by heptosyltransferases HP0279 and HP1191 [18] . However , the enzyme responsible for the transfer of the third DD-Hep residue ( designated as Hep III ) to Hep II remains to be identified . The outer core structure of H . pylori 26695 LPS was initially postulated to contain a DD-heptan with the first DD-Hep residue connecting a side-branched α-1 , 6-glucan [6 , 16 , 17 , 19–22] , but a recent reinvestigation into the structure of 26695 LPS revised the outer core as being a linear arrangement of DD-heptan and α-1 , 6-glucan linked to the inner core through a trisaccharide ( GlcNAc-Fuc-DD-Hep ) termed as Trio [15] ( Fig 1A ) . Furthermore , the attachment site of the O-antigen to the core-oligosaccharide has not been identified [13 , 15] , and therefore the precise assignment of the O-antigen and core-oligosaccharide domains remains unclear . Continuing the structural investigations of H . pylori LPS performed by the research groups of Trent [9 , 18 , 23–28] , Moran [29–31] , Altman [15 , 19 , 22 , 32–35] and Feldman [36 , 37] , the goal of this study was to precisely define the core-oligosaccharide and O-antigen domains . The LPS of the H . pylori strain G27 was chosen to be analysed in this study , as this fully sequenced strain has been used extensively in H . pylori research [38] , and for which the LPS structure has not been characterised . Using a combination of mass spectrometry and NMR spectroscopy , we redefined the core-oligosaccharide domain of H . pylori LPS to comprise solely the inner core conserved hexasaccharide of the previous model ( Glc-Gal-DD-Hep-LD-Hep-LD-Hep-KDO , see annotations on Fig 1B and Fig 1D ) . Therefore , we propose that the H . pylori O-antigen domain includes the outer core structure of the previous model . We further demonstrate that deletion of a conserved putative heptosyltransferase HP1284 from both G27 and X47 strains resulted in the attachment of O-antigen onto an incomplete core-oligosaccharide missing Hep III and the adjoining Glc-Gal unit ( Fig 1C ) , suggesting that HP1284 is likely to be the Hep III transferase of the core-oligosaccharide domain . In addition , mutations of HP1284 and waaL led to increased sensitivity to polymyxin B and loss of colonisation in the mouse model compared to wild-type . Mapping the mutagenesis and colonisation data of previous studies onto the newly defined H . pylori LPS core-oligosaccharide and O-antigen domains suggests that the conserved Trio and the intact core domains are critical for colonisation .
Using preparative isolation , highly pure LPS from wild-type G27 was obtained for MS structural analysis . GC-EI-MS analysis of monosaccharides as their TMS ( trimethylsilyl ) derivatives ( Fig A in S1 Text ) revealed that wild-type G27 LPS contains Fuc , Gal , Glc , Hep , GlcNAc and KDO . Methylation linkage analysis ( Table A in S1 Text ) indicated a complex monosaccharide composition including terminal and 3-linked Fuc; terminal , 2- , 3- and 4-linked Gal; terminal , 3- , and 6-linked Glc; 2- and 3-linked DD-Hep; 2- , 3- , 7- and 2 , 7-linked Hep; and terminal , 3- , 4- and 3 , 4-linked GlcNAc . Overall the monosaccharide composition of wild-type G27 LPS is very similar to the recently re-investigated LPS from strain 26695 [15] . Terminal and 2-linked Gal , and 3 , 4-linked GlcNAc that are characteristics of the LacNAc element of Lewis antigens were found , together with terminal Fuc , suggesting the existence of both Lex and Ley epitopes that are observed in the LPS of H . pylori strains including 26695 [6 , 7 , 15–17] , SS1 [6 , 16 , 32] , J99 [6] , NCTC11637 [16] . The wild-type G27 LPS was subjected to methanolysis to facilitate further MS analysis as intact LPS molecules are normally too large for direct MS characterisation . Matrix-assisted laser desorption/ionization time of flight ( MALDI-TOF ) mass fingerprints of the methanolysis products of the wild-type G27 LPS sample after permethylation are shown in Fig 2A . The annotation of MS peaks was based on the previously characterised strain 26695 LPS and monosaccharide composition provided by the GC-EI-MS analyses . An MS peak at mass-to-charge ratio ( m/z ) 518 . 2 shown in Fig 2A was annotated as LacNAc , and a peak at m/z 568 . 4 was annotated as N-acylated-glucosamine from lipid A . A cluster of MS peaks at m/z 695 . 3 , 899 . 4 , 1103 . 5 , 1307 . 5 and 1511 . 6 shown in Fig 2A was assigned to glucan-Hep-Fuc structures with Glc repeating from one to five times respectively . A peak at m/z 2598 . 2 in Fig 2A corresponds to a phosphorylated Glc-Gal-tri-Hep-KDO-lipid A structure whose methanolysed products give rise to most other peaks in the spectrum . The Trio is cleaved into two parts: the GlcNAc remains attached to the phosphorylated Glc-Gal-tri-Hep-KDO structure , giving rise to peaks at m/z 2843 . 3 and 2336 . 0 in Fig 2A , and the -DD-Hep-Fuc portion is attached to the previously mentioned glucan clusters . Since all the components of the wild-type G27 LPS are also found in the LPS of strain 26695 , we propose that both LPS molecules are structurally very similar ( compare Fig 1A and Fig 1B ) . To characterise the heptan in the wild-type G27 LPS , mild HF hydrolysis was used to cleave the 1 , 3-linked Fuc-GlcNAc glycosidic bond in the Trio moiety so that the entire heptan-glucan structure could be observed . The MALDI-TOF spectrum of wild-type G27 LPS after mild HF hydrolysis and permethylation gives an MS pattern of glycan fragments strongly suggesting that the heptan and glucan can be as long as 23 Hep and 5 Glc units respectively ( Fig 2B ) . This full-length heptan-glucan structure gives rise to a peak at m/z 7721 . 7 . To further determine the length of the poly-LacNAc portion , wild-type LPS was mildly oxidised using sodium periodate and the MALDI-TOF spectrum was recorded ( Fig 2C ) . Periodate oxidation cleaves glycans between neighbouring hydroxyl groups , therefore poly-LacNAc/Lewis structures survive the reaction while most other glycan structures are completely oxidised . Notably , for the Ley epitope , two terminal Fuc residues were oxidised , leaving intact LacNAc structures; whereas for the Lex epitope , terminal Gal and Fuc were oxidised , leaving GlcNAc-LacNAc structures . As expected , two clusters of peaks at m/z 1228 . 8 , 1678 . 1 , 2127 . 3 and 2576 . 4 , and at m/z 983 . 7 , 1433 . 0 and 1882 . 1 that are representative of the Lex and Ley epitopes respectively can be observed in the MS spectrum ( Fig 2C ) . The longest observed poly-LacNAc has 6 repeating units . Collectively , our data suggest that the fundamental architecture of G27 wild-type LPS is very similar to strain 26695 . They both contain fucosylated LacNAc ( Lex and Ley ) ( Fig B in S1 Text ) , heptan , glucan , Trio , a phosphorylated Glc-Gal-tri-Hep-KDO structure and lipid A . Not all glycosyltransferases responsible for H . pylori LPS assembly have been identified [13] . Of particular interest is the heptosyltransferase responsible for the transfer of Hep III to which the conserved Trio motif is attached ( Fig 1A ) . A targeted approach to identify the Hep III transferase of the core LPS was based on the identification of highly conserved putative glycosyltranferases in the H . pylori genome [13] . The putative protein sequence of HP1284 in strain G27 was found to share 36% identity to LPS Hep III transferase WaaQ from Haemophilus influenza 86-028NP . Therefore , the corresponding mutant G27ΔHP1284 was constructed . Silver staining of LPS extracted from G27ΔHP1284 displayed a similar pattern to the wild-type LPS , although it appeared to be missing bands sized around 15–20 kDa LPS ( Fig 3A , lane 2 ) , indicating that HP1284 is involved in the biosynthesis of LPS . Genetic complementation of G27ΔHP1284 mutant restored the wild-type LPS profile ( Fig 3A , lane 3 ) . To gain higher resolution of the core region , Tricine-SDS-PAGE was used to separate the low molecular weight LPS species more effectively . This revealed that mutation of HP1284 in G27 resulted in a clear change in bands of low molecular weight LPS ( about 10 kDa ) corresponding to core lipid-A ( Fig 3B , lane 2 ) . The same profile was also observed for HP1284 isogenic mutants made in strains 26695 and X47 ( Fig 3B , lanes 6 and 8 ) demonstrating that this gene’s role in the biosynthesis of the H . pylori LPS core region is conserved across different strains . Upon genetic complementation of G27ΔHP1284 mutant , the 10 kDa band species on Tricine-SDS-PAGE were restored ( Fig 3B , lane 3 ) . To gain further insight into the role of HP1284 gene in the H . pylori LPS biosynthesis , the same MS-based strategies were used for analysing the structure of LPS purified from G27ΔHP1284 . The MS spectra of the mild HF hydrolysed and sodium periodate oxidised LPS from G27ΔHP1284 were very similar to the wild-type strain ( Fig C in S1 Text ) , indicating the existence of poly-LacNAc , heptan , glucan and the Trio moiety . However , the MS spectrum of permethylated G27ΔHP1284 LPS after methanolysis presented a distinct MS pattern ( Fig 4A ) . The MS peak at m/z 1183 . 4 in Fig 2A corresponding to the phosphorylated GlcNAc-tri-Hep-KDO structure shifted to a peak at m/z 1180 . 6 in Fig 4A . Analysis using MALDI-TOF/TOF of this peak suggested that it corresponded to a phosphorylated glycan with a sequence of GlcNAc-Hep-Hep-KDO ( Fig C in S1 Text ) , which indicated that the Hep III residue of the core together with the Glc-Gal disaccharide attached to it were missing . Methylation linkage analysis also provided supportive data , i . e . , no terminal-Glc , 4- linked Gal or 2 , 7-linked Hep was found ( Table A in S1 Text ) . Together , the sequence homology of HP1284 to the LPS Hep III transferase WaaQ of H . influenza and the genetic and structural data presented above indicate that the conserved putative heptosyltransferase HP1284 is very likely to be the Hep III transferase , participating in the biosynthesis of the core-oligosaccharide of H . pylori LPS . The HP1284 mutation in the core-oligosaccharide did not affect further LPS synthesis and the function of O-antigen ligase WaaL , though it did lead to a simultaneous loss of the Hep III residue of the core and the Glc-Gal disaccharide . To precisely identify the O-antigen attachment site , and to define the core and O-antigen domains of H . pylori LPS , we analysed the LPS structure of the waaL mutant that lacks the O-antigen and only harbours core-lipid A [36] . Therefore , core-lipid A was purified from the waaL deletion mutant G27ΔwaaL ( Fig 3B , lane 4 ) to carry out methanolysis/MS analysis . No peak corresponding to the LacNAc , heptan , glucan and Trio was observed , suggesting that the core-oligosaccharide in H . pylori G27ΔwaaL lacks all the glycan structures from the Trio outwards ( Fig 4B ) . An MS peak at m/z 1591 . 7 corresponding to a short core-oligosaccharide with a sequence of Glc-Gal-tri-Hep-KDO was observed ( Fig 4B ) . MS peaks at m/z 1886 . 9 and 2598 . 4 that are indicative of core-lipid A structures were also observed ( Fig 4B ) . GC-EI-MS linkage analysis revealed a much simpler monosaccharide composition including terminal-Glc , 4-linked Gal , 2- , 3- and 7-linked Hep ( Table A in S1 Text ) . To further confirm the structure of G27ΔwaaL LPS , NMR experiments on intact LPS were carried out for LPS samples in D2O containing deuterated dodecylphosphocholine ( D38-DPC ) , thus anchoring the lipid tails into micelles . This technique has been used previously for the study of glycolipids [39] and rough-type LPS [40] . Tentative assignments of some resonances in the 1H and 13C spectra of the G27ΔwaaL LPS derived from analysis of 2D TOCSY and NOESY spectra are listed in Table B in S1 Text . Seven anomeric proton signals attributable to three α-Hep , one α-GlcN , one α-Glc , one β-GlcN and β-Gal were observed , which is fully consistent with our MS data and previous research [32] . In accord with our MS experiments , the NMR data suggest that the G27ΔwaaL LPS is truncated from the Trio , indicating that H . pylori G27 synthesises a very short core-oligosaccharide with a sequence of αGlc1-4βGal1-7αHep1-2αHep1-3αHep-KDO . Deletion of the waaL gene in H . pylori strain X47 also resulted in a short core LPS ( Fig D in S1 Text ) . In addition , double mutation of HP1284 and waaL led to further reduction in the size of the core-oligosaccharide , supporting that HP1284 encodes for the putative heptosyltransferase that is responsible for transfer of the Hep III residue ( Fig D in S1 Text ) . The LPS molecules purified from X47 wild-type and X47ΔHP1284 were subjected to methanolysis and analysed by MS . The resulting data indicate that X47 LPS shares the same structural architecture with G27 and 26695 LPS ( Fig E in S1 Text ) . In addition , the deletion of HP1284 in the X47 background led to the same structural change with the core-oligosaccharide missing the Hep III residue , as seen in G27ΔHP1284 ( Fig E in S1 Text ) . Together , these results led to the redefinition of the core and O-antigen domains of H . pylori LPS and to the identification of the putative Hep III transferase of the core-oligosaccharide domain ( Fig 1B , 1C and 1D ) . Taking into consideration the redefinition of the H . pylori LPS core-oligosaccharide and the O-antigen domains in this study , we assessed the roles played by the putative Hep III transferase HP1284 and the O-antigen ligase WaaL in H . pylori's resistance to CAMPs . Polymyxin B has a similar mechanism of action to CAMPs , and therefore is an experimental substitute for CAMPs in laboratory settings [9 , 18] . Minimal inhibitory concentration ( MIC ) of polymyxin B was determined for the HP1284 and waaL mutants in three different H . pylori strains G27 , X47 and 26695 , using polymyxin B Etest strips . As the KDO hydrolase ( HP0579/HP0580 ) mutation confers strong sensitivity to polymyxin B due to a deficiency in lipid A modification [18] , it was introduced in strain G27 and X47 ( G27ΔHP0579 and X47ΔHP0579-HP0580 mutants respectively ) for comparative determination of polymyxin B MICs . Resistance to polymyxin B varied substantially among three H . pylori wild-type strains , with MICs of 4 . 8 ± 1 . 1 , 332 . 8 ± 70 . 1 and 149 . 3 ± 37 . 0 μg/mL in G27 , X47 and 26695 , respectively ( Table 1 ) . As expected , KDO hydrolase mutants G27ΔHP0579 and X47ΔHP0579-HP0580 showed a marked 37 . 0 and 1147 . 0 fold decrease in resistance to polymyxin B when compared to their corresponding wild-type strains . The HP1284 mutant in G27 , X47 and 26695 showed a 5 . 3 , 3 . 1 and 2 . 5 fold decrease in resistance to polymyxin B , and the waaL mutant in G27 and X47 showed a 3 . 7 and 5 . 7 fold decrease in resistance to polymyxin B . Compared to the severe decrease in polymyxin B resistance of the KDO hydrolase mutant , the HP1284 and waaL mutants only exhibited a moderate decrease in resistance to polymyxin B . We investigated the role of HP1284 in colonisation of the gastric mucosa using strain X47 , a robust mouse coloniser [41] . Two independent sets of mouse experiments were performed using C57BL/6J mice . In both Experiment 1 and 2 , two weeks after oral challenge , the X47 wild-type strain could establish colonisation within the mouse stomach , whereas no bacteria could be recovered from the mice challenged with X47ΔHP1284 ( Table 2 ) . This suggests that HP1284 is required by H . pylori strain X47 for host colonisation . To assess the mutation of WaaL on colonisation , a third mouse experiment was performed . Again , X47 wild-type strain colonised the mice well by weeks 2 and 8 . However , X47ΔwaaL strain was unable to colonise C57BL/6J mice at the two time points ( Table 2 ) , suggesting that the O-antigen ligase WaaL is also required for the survival of H . pylori strain X47 within a host .
In this study , a combination of genetic and structural analysis of H . pylori LPS from wild-type and mutant strains enabled the experimental identification of the O-antigen attachment site and the precise assignment of the core-oligosaccharide and O-antigen domains . In addition , HP1284 is proposed to encode the Hep III transferase of the LPS core domain , based on structural analysis of corresponding mutant LPS in two strains , sequence homology to the Hep III transferase WaaQ of H . influenza , and the reduction in the size of the core-oligosaccharide of the double HP1284/waaL mutant compared to the single waaL mutant . Deletion of HP1284 and the O-antigen ligase waaL led to a moderate decrease in resistance to polymyxin B and loss of colonisation in the mouse model . The structural analysis of core-oligosaccharide accumulating in the O-antigen ligase mutant , G27ΔwaaL , enabled the identification of the Hep III residue as the precise attachment site of the H . pylori O-antigen ( Fig 5 ) . The core-oligosaccharide in the G27ΔwaaL mutant is a short hexa-saccharide comprised of Glc-Gal-DD-Hep-LD-Hep-LD-Hep-KDO ( Fig 1D ) , indicating that the missing glycan structure , from the Trio outwards , is transferred as the O-antigen by ligase WaaL to the Hep III residue . As O-antigen biosynthesis in H . pylori is initiated in the cytoplasm through the action of WecA transferring a GlcNAc onto a undecaprenyl phospholipid ( UndPP ) carrier [36] ( Fig 5 ) , we propose that the first GlcNAc residue in the Trio , not the GlcNAc of the Lewis antigen , is the first sugar of the long H . pylori LPS O-antigen encompassing the Trio , the glucan , the heptan and Lewis antigens ( Fig 1B ) . This finding challenges the previous LPS model with an inner core and an outer core decorated with an O-antigen composed of Lewis antigens only [16] ( Fig 1A ) . The linear architecture of the heptan-glucan-Trio of the G27 LPS structure is consistent with the recently revised structures of LPS in strains 26695 [15] , SS1 [32] and serogroup O:3 [34] , supporting that the linearity of this region may be a general feature of H . pylori strains . This overturns the paradigm of H . pylori LPS outer core containing a DD-heptan with the first DD-Hep residue connecting a side-branched glucan in earlier studies [6 , 16 , 17 , 19–22] . Furthermore , our study demonstrated the presence of the Trio in both G27 and X47 LPS structures . This moiety has also been reported in the revised LPS structures of 26695 [15] , SS1 [32] and serogroup O:3 [34] , suggesting that the Trio is a common LPS feature of H . pylori strains . Conservation of the Trio among H . pylori strains contrasts with the variability of the distal domains of O-antigen such as glucan , heptan and Lewis antigen in 26695 [15] , and a short oligomer of 1 , 2-linked ribofuranose ( riban ) and Lewis antigen in SS1 [32] . Of note , the O-antigen in HP1284 deletion mutant is transferred onto an incomplete core-oligosaccharide missing the Hep III and attached disaccharide ( Fig 1C ) . This suggests that when the Hep III residue is missing , H . pylori WaaL can still use the Hep II residue as the attachment site for the O-antigen ligation , and that the branched disaccharide in the core-oligosaccharide is not relevant for the activity of H . pylori WaaL . This differs from Escherichia coli and Salmonella enterica in which the terminal GlcNAc residue in the core-oligosaccharide acceptor is crucial for recognition by WaaL [42 , 43] . However , it has been shown that a waaL mutant of E . coli cannot be cross-complemented by the waaL gene of H . pylori [36] , supporting that WaaL in different bacteria has high specificity for its cognate core-oligosaccharide [37 , 44] . Prior to this study , glycosyltransferases responsible for transferring the Hep III , the GlcNAc and Fuc residues of the Trio , and the heptan were unknown [13] . Here the targeted discovery of the highly conserved putative heptosyltransferase gene HP1284 combined with our genetic and structural data support that HP1284 is the Hep III transferase ( Fig 1C ) . As discussed earlier , WecA can now be considered the enzyme transferring the GlcNAc residue of the Trio , whereas the fucosyltransferase of Trio is yet to be identified . Combined with the previously characterised LPS biosynthetic genes , currently known glycosyltransferases were assigned to the complete structure of H . pylori G27 LPS ( Fig 6 ) . To assess the role of the core-oligosaccharide and O-antigen domains in CAMPs resistance , polymyxin B MICs were measured in corresponding mutants and compared to the KDO hydrolase mutant . This mutant is highly sensitive to CAMPs due to deficiency in the constitutive modifications of lipid A [18] , the primary mechanism involved in H . pylori's resistance to CAMPs [9] . In this study , the KDO hydrolase mutants displayed a 37- and 1147-fold higher susceptibility , in G27 and X47 respectively , confirming the key role of lipid A modification in resistance to CAMPs in H . pylori . In contrast , the lack of O-antigen in the waaL mutant or the absence of core Hep III and adjoining Glc-Gal disaccharide in the HP1284 mutant only led to a 3- to 5-fold increase in susceptibility to polymyxin B , suggesting that HP1284 or waaL mutations is unlikely to affect the highly-ordered lipid A modification process . Of note , polymyxin B MICs varied almost 70-fold among the three tested wild-type H . pylori strains with MICs increasing in the following order , G27 , 26695 and X47 . This suggests that strain specific mechanisms other than the constitutive lipid A modifications also contribute to resistance to CAMPs . For example , the incorporation of host cholesterol onto the surface of H . pylori [45] , or the regulation of the glycerophospholipid content in the OM as reported in S . typhimurium [46] may also be critical for CAMP resistance . Finally , mapping LPS mutagenesis and colonisation data of previous studies onto the redefined H . pylori LPS structure suggested that the conserved Trio and the short core of H . pylori LPS are important for colonisation whereas the less conserved domains are less important ( Fig 6 ) . For example , the HP0826 mutant in SS1 strain , which is devoid of Lewis antigen , is still able to colonise the host , although less efficiently compared to the wild-type [16 , 35] . The DD-heptan and the glucan of the O-antigen are not essential for colonisation [19 , 33 , 47] , while the HP0479 mutant truncated at the Hep residue of the Trio resulted in loss of colonisation ability [22] . The variability of the O-antigen beyond the conserved Trio may enable H . pylori to adapt to a specific host for example via host mimicry and the presence of Lewis antigens . In contrast , the conserved core and Trio may reflect evolutionary adaptation to shared mammalian traits such as the innate immune system , enabling the survival and persistence of H . pylori within the gastric mucosa . The requirement of a conserved LPS moiety for gastric colonisation was also observed in this study . The loss of mouse colonisation in the X47ΔwaaL mutant could be attributed to the truncation of the conserved Trio moiety rather than the remaining variable regions . However , the inability of the X47ΔHP1284 mutant to colonise the mouse stomach is intriguing as the LPS structure in this mutant lacks only three carbohydrate units of the core-oligosaccharide yet still harbours the extended O-antigen . In addition , the mutant exhibited only a moderate susceptibility to polymyxin B . This warrants further investigation into the role of the structure of the core-oligosaccharide in host colonisation . In summary , we propose the redefinition of the core-oligosaccharide and O-antigen domains of H . pylori LPS . HP1284 is found to be the putative Hep III transferase , and its mutation leads to an abnormal core-oligosaccharide but does not affect the O-antigen ligation . Future studies are needed to elucidate the essential role of HP1284 in host colonisation .
The bacterial strains and plasmids and oligonucleotides used in this study are listed in Table C and D respectively in S1 Text . H . pylori strains were cultured on Columbia Blood Agar ( CBA ) plates , supplemented with 5% horse blood and 5% new-born calf serum ( NCS ) . Antibiotic selection in H . pylori was carried out by supplementing media with chloramphenicol ( 10 μg/mL ) or streptomycin ( 10 μg/mL ) where appropriate . Plates were incubated at 37°C for 24∼48 h under microaerobic conditions established in sealed jars using the Anoxomat MarkII system ( Mart Microbiology B . V . , the Netherlands ) after one atmosphere replacement with the following gas composition N2:H2:CO2 , 85:5:10 . LPS microextraction was prepared as previously described [51] . Briefly , bacterial cells ( OD600 = 3 . 0 ) harvested from CBA plates were suspended in 100 μL LPS lysis buffer ( 2% SDS , 4% β-mercaptoethanol , 0 . 1% bromophenol blue , 10% glycerol , 1 M Tris-HCl ( pH 6 . 8 ) ) . Samples were heated at 100°C for 10 min . Thereafter , 5 μL proteinase K ( PK ) ( 20 mg/mL ) was added to the cooled samples , and incubated in a 55°C water bath overnight . The obtained LPS samples were run on 15% SDS-PAGE gels or 16% Tricine-SDS-PAGE gels and visualized by silver stain [52] and by Western blot , using mouse anti-Lex ( 1:1500 ) and anti-Ley ( 1:1500 ) antibodies ( Santa Cruz ) . After incubation with a secondary rabbit anti-mouse peroxidase-conjugated IgM antibody ( 1:10 , 000 ) ( Jackson ImmunoResearch ) , detection of the HRP conjugates was accomplished by chemiluminescence ( Sigma ) using a LAS-3000 Intelligent DarkBox ( Fujifilm ) ( software Image reader LAS 3000 V2 . 2 ) . LPS from wild-type G27 and X47 , G27ΔHP1284 and X47ΔHP1284 was extracted using the hot phenol-water method [51] with modifications . The bacterial cells grown on 25 CBA plates ( 24 h growth ) were harvested into 15 mL of 10 mM Tris-HCl buffer ( pH 8 . 0 ) containing 2% SDS , 4% β-Mercaptoethanol and 2 mM MgCl2 . After thorough suspension , 100 μL PK ( 20 mg/mL ) was added to the cell mixture and incubated in a 55°C water bath overnight . Subsequently , 2 mL cold sodium acetate ( 3 M ) and 20 mL cold absolute ethanol were added in order and the suspension was allowed to precipitate at -20°C overnight . LPS was then centrifuged down at 10 , 000 rpm for 10 min at 4°C , the supernatant discarded and the precipitation procedure repeated to remove residual SDS . After the final centrifugation , the pellet was suspended in 10 mL of 10 mM Tris-HCl buffer ( pH 7 . 4 ) . 100 μL DNase I ( 20 mg/mL ) and 100 μL RNase ( 20 mg/mL ) were added and the solution was incubated in a 37°C water bath for 4 h . The LPS mixture and 10 mL 90% liquid phenol were then transferred to a 68°C water bath for 10 min . The preheated phenol was then added to the LPS mixture , and incubated at 68°C for 30 min and then put on ice box for 10 min . The cooled mixture was centrifuged at 4 , 000 rpm for 60 min at 4°C and the resulting aqueous layer was carefully transferred to a 50 mL Falcon tube . The hot phenol-water extraction was repeated again and the two aqueous layers were combined . Phenol was removed by dialysis against water for 2 days . The LPS sample was then lyophilized and suspended in 10 mL water , and ultracentrifuged at 100 , 000 g for 12 h at 4°C . The resulted gel-like pellet was washed three times in 2 mL chloroform-methanol mixture ( 1:2 v/v ) to remove phospholipids . After air drying , the LPS was resuspended in 3 mL H2O and lyophilized . LPS from G27ΔwaaL was extracted using the EDTA-promoted method [53] with modifications . The bacterial cells grown on 25 CBA plates ( 24 h growth ) were harvested into 15 mL of 10 mM Tris-HCl buffer ( pH 8 . 0 ) containing 2mM MgCl2 . The bacterial cells were sonicated , and DNase I and RNase A were added to a final concentration of 100 μg/mL and 25 μg/mL , respectively . The suspension was incubated in a 37°C water bath for 4 h . After the incubation , 5 mL of 0 . 5 M EDTA , 2 . 5 mL of 20% SDS , and 2 . 5 mL of 10 mM Tris-HCl buffer ( pH 8 . 0 ) were added to give a final volume of 25 mL , and the pH was raised to 9 . 5 . The solution was vortexed , and centrifuged at 50 , 000 g for 30 min at 20°C to remove peptidoglycan . The supernatant was transferred to a new 50 mL tube , and PK was added to give a final concentration of 200 μg/mL . The sample was then incubated in a 55°C water bath overnight . Two volumes of 0 . 375 M MgCl2 in 95% ethanol were added , and the mixture was cooled in a -20°C freezer for 1 h . After the cooling step , the sample was centrifuged at 12 , 000 g for 15 min at 4°C . The obtained pellet was resuspended in 25 mL of 10 mM Tris-HCl ( pH 8 . 0 ) containing 2% SDS and 0 . 1 M EDTA . The sample was then sonicated , and the pH was lowered to 7 . 0 by the addition of 4 M HCl . The solution was then incubated at 85°C for 30 min to ensure denaturation of SDS-resistant proteins . After cooling , the pH was raised to 9 . 5 by the addition of 4 M NaOH . PK was then added to give a final concentration of 25 μg/mL , and the sample incubated in a 55°C water bath for 4 h . After the incubation , two volumes of 0 . 375 M MgCl2 in 95% ethanol were added , mixed , and cooled in -20°C freezer for 1 h . The sample was centrifuged at 12 , 000 g for 15 min at 4°C . The obtained LPS pellet was resuspended in 10 mL of 10 mM Tris-HCl ( pH 8 . 0 ) containing 25 mM MgCl2 , and subjected to sonication . The LPS solution was ultracentrifuged at 100 , 000 g for 12 h at 15°C . The obtained pellet was washed three times in 2 mL chloroform-methanol mixture ( 1:2 , v/v ) to remove phospholipids . After air drying , the LPS pellet was resuspended in 10 mL of 10 mM Tris-HCl ( pH 8 . 0 ) containing 25 mM MgCl2 , and ultracentrifugation was repeated as described above . The obtained LPS pellet was resuspended in H2O ( 3 mL ) and lyophilized . Trimethylsilyl derivatised monosaccharides were prepared as previously described [54] and analysed on a PerkinElmer Clarus 500 instrument fitted with a RTX-5-fused silica capillary column . The oven temperature was initially 65°C , and increased to 140°C at the rate of 25°C/min , and then to 200°C at the rate of 5°C/min . The temperature was finally raised to 300°C at a rate of 10°C/min and held for 5 min . Partially methylated alditol acetates were prepared as previously described [55] . A Bruker 456-GC/SCION SQ instrument fitted with a Bruker BR-5ms column was used to carry out the experiments . The sample was injected into the column at 60°C and held for 1 min , and the temperature was increased to 300°C over 30 min at a rate of 8°C/min and held for 5 min . LPS samples were dissolved in 0 . 5 M methanolic-HCl solution and incubated at 50°C for 40 min . The supernatant was collected and dried under a stream of nitrogen . The dried methanolysis products were permethylated and analysed by MS . For HF hydrolysis , LPS samples ( 200 μg ) were hydrolysed as previously described [56] except that samples were dissolved in 50 μL of 48% HF and incubated at 4°C for 24 h with reagents being removed under a stream of nitrogen . The LPS sample ( 200 μg ) was dissolved in 100 μL sodium periodate solution ( 20 mM , in 100 mM ammonium acetate buffer , pH = 6 . 5 ) . The solution was incubated at 4°C for 20 h in the dark . The reaction was terminated by adding ethylene glycol and was kept at room temperature for 1 h before lyophilisation . Sodium borohydride ( 400 μL , 10 mg/mL in 2 M ammonium hydroxide ) was added to the sample and incubated at room temperature for 2 h . The reaction was quenched by adding 3–5 drops of acetic acid and purified by Dowex resin . Sodium hydroxide ( three to five pellets per sample ) was crushed in dimethyl sulfoxide ( 3 mL ) . The resulting slurry ( 0 . 75 mL ) and iodomethane ( 0 . 85 mL ) were added to the sample . After agitating at room temperature ( 60 min ) , the reaction was quenched by adding ultrapure water ( 2 mL ) with shaking . The glycans and lipo-glycans were extracted with chloroform ( 2 mL ) , and the solution was washed with ultrapure water ( 2x ) . The chloroform was then removed under a stream of nitrogen . MALDI-TOF spectra were recorded by either a Voyager DE-STRTM MALDI–TOF or a 4800 MALDI-TOF/TOF mass spectrometer ( Applied Biosystems , Darmstadt , Germany ) with MALDI-TOF/TOF spectra acquired with the latter instrument . The 4700 Calibration standard kit ( Applied Biosystems ) was used for calibrating the MS mode and fibrinopeptide B ( Sigma ) was used for calibrating the MS/MS mode . The collision energy for MS/MS was set to 1 kV , and the collision gas was argon . 2 , 5-Dihydroxybenzoic acid and 3 , 4-diaminobenzophenone were used as matrix . Permethylated samples were dissolved in methanol ( 10 μL ) and the solution premixed with matrix ( 20 mg/mL ) with a ratio of 1:1 ( v/v ) with the mixture ( 1 μL ) being spotted on the plate . The LPS samples were incorporated into micelles as previously described [57] . Briefly , 6 . 5 mg LPS sample was mixed with D38-DPC ( Cambridge Isotope Laboratories . Inc . ) at an estimated molar ratio of 1:40 dissolved in deuterated potassium phosphate ( 50 mM , pD = 6 ) , and transferred to a 5 mm NMR tube . 1- and 2D TOCSY and NOESY NMR spectra were recorded at 30°C using a Bruker Avance III 600MHz NMR spectrometer equipped with a TXI/TCI cryoprobe . Minimum inhibitory concentrations were determined using polymyxin B Etest strips ( Biomérieux ) . Bacteria from 24 h CBA plate cultures were collected in PBS and standardized to an OD600 = 2 . 0 . New blood agar plates were uniformly inoculated with standardized bacterial suspensions using a cotton swab and allowed to dry completely , followed by addition of the Etest strip to the centre of the plate . The plates were incubated at 37°C in a microaerobic atmosphere for 72 h before reading . Each experiment was repeated in triplicate , and the averages and standard deviations are reported . C57BL/6J female mice aged 6–8 weeks were purchased from Animal Resource Centre , Australia . Each mouse was orally challenged with 0 . 2 mL bacterial inoculum in HI broth containing 1× of 109 colony-forming units ( CFU ) of H . pylori strains harvested from CBA plates after 24 h growth at 37°C under microaerobic conditions . Three independent sets of experiments were performed . In Experiment 1 , two groups of mice ( n = 5 per group ) challenged with wild-type X47 or X47ΔHP1284 were sacrificed at 2 weeks . In Experiment 2 , two groups of mice ( n = 10 per group ) challenged with wild-type X47 or X47ΔHP1284 were also sacrificed at 2 weeks . In Experiment 3 , four groups of mice ( n = 5 per group ) challenged with wild-type X47 or X47ΔwaaL and were sacrificed at 2 and 8 weeks . After sacrifice , the stomachs were removed from the mice and were opened along the greater curvature . The non-mucosal , squamous forestomach was discarded and the stomach content was gently removed with a sterile loop . Stomach tissue was cut into small pieces and homogenized in 1 mL HI broth by a Tissue lyser ( Qiagen ) . Aliquots ( 100 μL ) of neat , 1× 10−1 and 1× 10−2 dilutions of the stomach homogenate were spread onto H . pylori selective plates ( CBA containing 5% horse blood , Dent supplement ( Oxoid ) , nalidixic acid at 10 μg/mL and bacitracin at 100 μg/mL . After culture for 4–5 days , the number of colonies was counted and CFU/stomach was calculated to determine bacterial load . Mouse experimental procedures were reviewed and approved by the Institutional Animal Care and the Animal Ethics Committee of the University of Western Australia ( AEC Approval No: RA 03/100/1085 ) and adhered to the Australian code for the care and use of animals for scientific purposes ( 8th Edition , 2013 ) and the Animal Welfare Act ( 2002 ) of Western Australia . Animals were euthanased using Isofluorane inhalation and cervical dislocation .
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The bacterial pathogen Helicobacter pylori chronically infects the human stomach and causes ulcers and gastric cancer . The H . pylori lipopolysaccharide harbors unique properties that promote persistent infection through immune evasion . Despite the key role of H . pylori lipopolysaccharide in the bacterium’s pathogenesis , its precise domain organisation is still not available . Here , using a multidisciplinary approach involving biochemistry , genetics and analytical chemistry , we elucidated the H . pylori lipopolysaccharide structure and domain organisation . We found that the core domain is a short conserved hexasaccharide missing the canonical inner and outer core organisation . The O-antigen encompasses motifs previously assigned as the outer core domain , starting with a conserved trisaccharide , the variable glucan and heptan moieties and finishing , usually , with Lewis antigens . Furthermore , we demonstrate that the integrity of the core domain and the conserved trisaccharide of the O-antigen are critical for H . pylori to colonise the gastric niche . Together , the redefinition of the H . pylori lipopolysaccharide domains warrants future studies to dissect their roles in host-pathogen interactions and persistence . Also enzymes involved in the assembly of the conserved structure could be attractive targets for the design of new therapeutic agents for managing persistent H . pylori infection .
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] |
2017
|
The redefinition of Helicobacter pylori lipopolysaccharide O-antigen and core-oligosaccharide domains
|
Tumor necrosis factor alpha ( TNF-α ) blockers are recognized as a risk factor for reactivation of granulomatous infections . Leishmaniasis has been associated with the use of these drugs , although few cases have been reported . We performed a retrospective observational study including patients with confirmed leishmaniasis acquired in the Mediterranean basin that were under TNF-α blockers therapy at the moment of the diagnosis . Patients diagnosed in our hospital from 2008 to 2018 were included . Moreover , a systematic review of the literature was performed and cases fulfilling the inclusion criteria were also included . Forty-nine patients were analyzed including nine cases from our series . Twenty-seven ( 55 . 1% ) cases were male and median age was 55 years . Twenty-five ( 51% ) patients were under infliximab treatment , 20 ( 40 . 8% ) were receiving adalimumab , 2 ( 4 . 1% ) etanercept , one ( 2% ) golimumab and one ( 2% ) a non-specified TNF-α blocker . Regarding clinical presentation , 28 ( 57 . 1% ) presented as cutaneous leishmaniasis ( CL ) , 16 ( 32 . 6% ) as visceral leishmaniasis ( VL ) and 5 ( 10 . 2% ) as mucocutaneous leishmaniasis ( MCL ) . All VL and MCL patients were treated with systemic therapies . Among CL patients , 13 ( 46 . 4% ) were treated with a systemic drug ( 11 received L-AmB , one intramuscular antimonials and one miltefosine ) while 14 ( 50% ) patients were given local treatment ( 13 received intralesional pentavalent antimonials , and one excisional surgery ) . TNF-α blockers were interrupted in 32 patients ( 65 . 3% ) . After treatment 5 patients ( 10 . 2% ) relapsed . Four patients with a CL ( 3 initially treated with local therapy maintaining TNF-α blockers and one treated with miltefosine ) and one patient with VL treated with L-AmB maintaining TNF-α blockers . This data supports the assumption that the blockage of TNF-α modifies clinical expression of leishmaniasis in endemic population modulating the expression of the disease leading to atypical presentations . According to the cases reported , the best treatment strategy would be a systemic drug and the discontinuation of the TNF-α blockers therapy until clinical resolution .
Tumor Necrosis Factor-α ( TNF-α ) is a crucial cytokine in the inflammatory cascade by activating the type 1 T helper ( Th1 ) immune response , enhancing the activity of the macrophages and essential for the formation and maintenance of granulomas [1] . Since TNF-α has been implicated in numerous immune-mediated disorders , the blockage of this cytokine has been studied as a therapeutic strategy against such diseases . Nowadays , the anti-TNF based therapy is widely used and approved for the treatment of chronic inflammatory conditions as rheumatoid arthritis , polyarticular juvenile idiopathic arthritis , plaque psoriasis and psoriatic arthritis , ankylosing spondylitis and inflammatory bowel diseases [2] . The first approved TNF-α blocker was etanercept ( Enbrel ) in May 1998 followed by infliximab ( Remicade ) in November 1999 , adalimumab ( Humira ) in December 2002 , certolizumab ( Cinzia ) in April 2008 and golimumab ( Simponi ) in April 2009 . Since their first use , the TNF-α blockers were recognized as a risk factor for reactivation of granulomatous infections such as tuberculosis , intracellular infections such as salmonellosis or listeriosis and other opportunistic fungal or viral infections [3] . Leishmaniasis is a parasitic granulomatous infection and it is endemic to South America , South Asia , Africa and South Europe . The protozoon is an obligate intracellular parasite of mononuclear phagocytic system cells . The clinical spectrum of leishmaniasis comprises subclinical ( asymptomatic ) , localized ( cutaneous ) and disseminated infection ( cutaneous , mucosal and visceral ) . Its clinical expression is determined on one hand by the species and zimodeme of the parasite and on the other hand by host factors and immune response [4] . Leishmaniasis has been associated with the use of TNF-α blockers , but only few cases have been reported in the literature , mainly in the Mediterranean basin [5 , 6] . We report nine more cases related to the use of TNF-α blockers and systematically review the published cases acquired in the Mediterranean basin . We also analyze their clinical presentation and discuss the relationship with immunomodulatory therapy . Finally , a therapeutic approach is discussed .
Categorical variables are expressed as percentages , and numerical data as the mean±SD for variables with a normal distribution or the median ( IQR ) for those with a skewed distribution . Categorical variables were compared with the chi-square test or Fisher exact test , and continuous variables with the Student t or the Mann-Whitney U test , depending on distribution . All statistical tests were 2-tailed , and significance was set at P < . 05 . Statistical analyses were performed using SPSS version 20 . 0 ( SPSS , Inc . , Chicago , IL , USA ) . Due to its retrospective design , oral consent was obtained by phone contact from the included patients . The study was approved by the Ethics Committee of Vall d’Hebron Research Institute .
A total of 33 publications were retrieved in our search including forty cases that fulfilled inclusion criteria [8–39] . Thus , a total of 49 cases were analyzed including our 9 cases ( Table 1 ) . Twenty-seven ( 55 . 1% ) cases were male and the median age was 55 ( range 7–80 ) years . Twenty-five ( 51% ) patients were under infliximab treatment at the moment of leishmaniasis diagnosis , twenty ( 40 . 8% ) were receiving adalimumab , two ( 4 . 1% ) were receiving etanercept , one ( 2% ) golimumab and another one ( 2% ) received a non-specified TNF-α blocker . From greater to lesser frequency , the underlying disease was psoriatic arthritis in twelve ( 24 . 5% ) cases , rheumatoid arthritis in twelve ( 24 . 5% ) cases , ankylosing spondylitis in nine ( 18 . 4% ) cases , Crohn’s diseases in five ( 10 . 2% ) cases , plaque psoriasis in five ( 10 . 2% ) cases and rheumatoid arthritis with psoriasis , ulcerative colitis , juvenile idiopathic arthritis , giant cell arthritis , seronegative arthritis and folliculitis decalvans in one case each other . All patients were diagnosed in European hospitals and probable place of infection was Spain in thirty-three ( 67 . 3% ) cases followed by Greece in five ( 10 . 5% ) cases , Italy in four ( 8 . 2% ) cases , France in two ( 4 . 1% ) cases , Malta in two ( 4 . 1% ) cases , Algeria in two ( 4 . 1% ) cases and Turkey in one ( 2% ) case . Regarding the clinical presentation , CL was the most frequent form ( 28 patients , 57 . 1% ) , eighteen cases presenting as a solitary ulcerative lesion and ten cases including one attended in our hospital who presented multifocal lesions ( Fig 1 ) . Sixteen ( 32 . 6% ) patients presented VL and five ( 10 . 2% ) patients had MCL , three in the nasal cavity ( Fig 2 ) , one as hyperplasic lesions around perianal mucosa and one as an infiltrative tumor involving upper lip , hard palate and nasal septum . Bone marrow aspirate was performed in one patient with MCL and one patient with CL . Although no amastigotes forms were observed , the patient with MCL had a positive Leishmania RT-PCR on the bone marrow sample . Identification to the level of species could be performed in eighteen patients . L . infantum was identified in seventeen cases and one was reported as L . donovani complex . All MCL and VL patients were treated with a systemic therapy . Fifteen ( 71 . 4% ) of them were treated with liposomal amphotericine B ( L-AmB ) , including one patient treated in combination with intralesional pentavalent antimonials , three ( 14 . 3% ) patients were treated with parenteral pentavalent antimonials and one ( 4 . 8% ) patient with miltefosine . Among CL cases , thirteen ( 46 . 4% ) patients received a systemic treatment; eleven were given L-AmB , one intramuscular antimonials and one miltefosine . Local treatment was given to fourteen ( 50% ) patients , thirteen received intralesional pentavalent antimonials , one combined with surgery and another with cryotherapy and one patient was treated with surgical excision of the lesion . Only one patient defaulted and did not receive any treatment . In thirty-two ( 65 . 3% ) cases TNF-α blockers therapy was interrupted . After treatment , four ( 8 . 2% ) patients with CL diagnosis relapsed . Three of these cases were initially treated with local medication and anti-TNF-α was not stopped . After relapse , the three patients received systemic treatment and anti-TNF-α therapy was discontinued in one patient , all achieving clinical cure . Another relapsing patient was initially treated with miltefosine and finally cured with local antimonials therapy . TNF-α blocker therapy was discontinued in both treatment courses . The last relapsing patient was a VL treated with L-AmB and TNF-α blockers were not stopped as her rheumatic disease was active presenting a MCL form 20 months after . When comparing clinical cure of CL patients , although statistical significance could not be reached cure ratios were 92 . 3% vs . 78 . 6% ( p = 0 . 6 ) when patients received systemic treatment or not and 94 . 1% vs 70% ( p = 0 . 13 ) when TNF-α blockers therapy could be stopped or not . Finally , the patient who did not receive any treatment was lost to follow up . Two ( 4 . 1% ) patients died: one patient after a bacterial superinfection in relation to his immunosuppression and the other patient as a result of a fatal arrhythmia during his treatment with systemic antimonials . Case presentation , treatment and outcomes are represented in Fig 3 .
The increase in the use of TNF-α antagonist has been associated with the emergence of new cases of leishmaniasis . The blockage of TNF-α favors the reactivation of latent leishmaniasis modulating its expression and worsening its clinical outcome . Once the leishmaniasis is confirmed , systemic drug treatment and the discontinuation of the TNF-α blockers therapy until clinical recovery seems to be the best therapeutic approach when possible . Otherwise , those patients receiving such therapy and coming from endemic areas require a close monitoring in order to detect early forms and start adequate treatment . Prospective studies and more participation on declaring existing cases in the adverse events notification system is required in order to assess the risk of leishmaniasis and other opportunistic diseases related to the use of anti TNF-α treatment more accurately .
|
Tumor necrosis factor alpha ( TNF-α ) blockers are widely used in numerous inflammatory diseases such rheumatoid arthritis , psoriasis or inflammatory bowel diseases . They have been recognized as a risk factor for reactivation of granulomatous infections . Although few cases have been reported , Leishmaniasis has been associated with the use of these drugs . Leishmania infantum is the main causative agent of leishmaniasis in Southern Europe and is prone to produce the visceral form . However , TNF-α has been implicated in the initial events of the infection mediating the disease expression . In our series , we have observed a surprisingly high proportion of cutaneous form ( 32 . 6% ) and muco-cutaneous form ( 10 . 2% ) . Clinical outcome observed in this series is also unusual . Four cases ( 14 . 3% ) with cutaneous leishmaniasis who received local therapy relapsed . Among patients with visceral leishmaniasis , one patient who maintained TNF-α blockers therapy relapsed despite etiological treatment . This data supports the assumption that the blockage of TNF-α modifies clinical expression of leishmaniasis leading to atypical presentations . According to the cases reported we proposed as best treatment strategy a systemic drug and the discontinuation of the TNF-α blockers therapy until clinical resolution .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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] |
2019
|
Leishmaniasis and tumor necrosis factor alpha antagonists in the Mediterranean basin. A switch in clinical expression
|
Polymorphisms identified in genome-wide association studies of human traits rarely explain more than a small proportion of the heritable variation , and improving this situation within the current paradigm appears daunting . Given a well-validated dynamic model of a complex physiological trait , a substantial part of the underlying genetic variation must manifest as variation in model parameters . These parameters are themselves phenotypic traits . By linking whole-cell phenotypic variation to genetic variation in a computational model of a single heart cell , incorporating genotype-to-parameter maps , we show that genome-wide association studies on parameters reveal much more genetic variation than when using higher-level cellular phenotypes . The results suggest that letting such studies be guided by computational physiology may facilitate a causal understanding of the genotype-to-phenotype map of complex traits , with strong implications for the development of phenomics technology .
The phenotypic variance cumulatively explained by marker loci found to associate with complex traits in genome-wide association studies ( GWAS ) is usually much less than the narrow-sense heritability [1]–[6] , the ratio of additive genetic variance to total phenotypic variance . Several explanations have been proposed for this unexplained variance , popularly called the missing heritability [1] , including imprecise heritability estimates; insufficient sample size; exclusion of particular types of polymorphisms such as copy number variants and rare SNPs in GWAS; unaccounted epistatic effects; and underestimated effect size of associated SNPs due to incomplete linkage with causal variants [3] , [6] . Recently it was shown [7] that a large proportion of the missing heritability can be accounted for if one estimates the variance explained by all available marker loci together . This suggests that most of the genetic variation underlying complex trait variation is due to marginal effects of many loci that are too small to pass stringent significance tests . Strong support for this interpretation comes from several meta-analyses of genome-wide association data [8]–[10] . While this insight appears to resolve much of the missing heritability issue as such , it also implies that standard GWAS approaches will not be very helpful for disclosing which genetic variants do actually contribute to additive variance . Part of the problem underlying the missing heritability is that while the genotype-phenotype map in reality arises from complex biological systems best described by nonlinear dynamic models , the statistical machinery of quantitative genetics , including GWAS methods , is built upon linear models of gene action . The aim of this study is not to improve the statistical methods per se , but rather to explore how more of the missing heritability can be explained and understood by combining nonlinear dynamic models with existing GWAS methods . The research program of linking system dynamics and genetics was suggested more than 40 years ago [11] and has been an active research area for more than 10 years [12]–[24] . Emergent properties of nonlinear systems , such as systemic silencing [25] , might lead to a situation where genetic variation that penetrates to low-level phenotypes underlying a higher-level phenotype does not necessarily manifest in the higher-level phenotype itself . Doing GWAS on these low-phenotypes may thus reveal more of the genetic variation influencing the higher-level trait . This line of reasoning is reflected in recent GWA studies on metabolite profiles [26] , [27] , in pathway and network-based analysis of genome-wide association studies [28] and in GWAS analyses on global gene expression data [29]–[30] . While all these studies represent important contributions , they do not combine a genetic mapping framework with mathematical models describing how high-level trait variation emerges from low-level trait variation , i . e . they do not provide a quantitative framework for elucidating how genetic variation affecting a low-level phenotype do actually influence a focal high-level phenotype . If a dynamic model can describe the phenotypic variation of a given trait , it follows that irrespective of the biological resolution of the model , the genetic variation underlying the phenotypic variation will have to be reflected as variation in the parameters of the model . We therefore hypothesized that performing GWAS on parameters in computational physiology models might reveal much more of the underlying genetic variation , as well as shedding light on how this variation actually causes phenotypic variation . To test the plausibility of this reasoning , we combined GWAS methodology with a causally-cohesive genotype-phenotype ( cGP ) model linking genetic variation to phenotypic variation . More specifically , a cGP model [19] is a mathematical model of a biological system where low-level parameters have an articulated relationship to an individual's genotype , and higher-level phenotypes emerge from the mathematical model describing the causal dynamic relationships between these lower-level processes . Our approach bears some resemblance to that of functional GWAS ( fGWAS ) [31] , where the genetic control of traits is analyzed by integrating biological principles of trait formation into the GWAS framework through mathematical and statistical bridges . Fu et al . [23] recently extended the functional mapping framework [15] to handle cyclic phenotypes such as circadian rhythms by combining differential equations with functional mapping of QTLs . However , there are clear differences between functional mapping and the cGP approach . In functional mapping the phenotypic measurements are currently done at the systems level , while lower-level parameters are estimated by combining curve-fitting with more classical QTL methods . In contrast , the cGP approach advocated here focuses on measuring lower-level parameters based on the idea that they are highly relevant phenotypes of the system . We studied a cGP model of a mouse heart cell [24] , where genetic variation is mapped to parametric variation , which propagates through the physiological model to generate multivariate phenotypes for the action potential ( an electrical signal ) and calcium transient ( linked to muscle contraction ) under regular pacing . The rationale for using a heart cell model was that multiscale and multiphysics modelling of the mammalian heart has a solid empirical basis , and arguably comprises the most complex mathematical conceptualization of any organ or physiological trait available . For clarity of exposition , and because heart cell models lie at the core of this class of multiscale whole organ models [32]–[37] , we deemed it sufficient to illustrate our points using a single cell model only . We used HapMap data [38] , [39] as a guide to generate genetic variation with realistic allele frequencies and linkage disequilibrium to underlie variation in the model parameters . Based on HapMap [39] individuals we simulated complex pedigree populations and performed GWAS on both low-level parameters and high-level phenotypes arising from the cGP model . The layout of the computational pipeline used for this study is depicted in Figure 1 . We show that genome-wide association studies on parameters reveal many more of the underlying SNPs than when using higher-level cellular phenotypes . Furthermore , the SNPs identified by GWAS on parameters can be used to build multivariate prediction models of higher-level phenotypes giving much higher explained variance than from GWAS on higher-level phenotypes alone . Our results suggest that combining statistical genetics with computational biology will facilitate both identification of genetic variation underlying complex traits and a much deeper understanding of how this genetic variation becomes causative .
The cell model [40] extends that of Bondarenko et al . [41] with more realistic calcium handling , conservation of charge , and detailed re-parameterization to consistent experimental data for the C57BL/6 “black 6” mouse . State variables include ion concentrations of sodium , potassium and calcium in the cytosol , calcium concentration in the sarcoplasmic reticulum , and the state distribution of ion channels , whose transition rates between open , closed , and inactivated conformations may depend on transmembrane voltage . Formulated as a system of coupled ordinary differential equations , this model provides a comprehensive representation of membrane-bound channels and transporter functions as well as fluxes between the cytosol and intracellular organelles . As the action potential and calcium transient features following an electrical stimulation are the only state descriptors fed into higher level features of current multiscale heart models [33] , we used these and associated aggregated measures as high-level phenotypes , see “Parameter to phenotype mapping” below . See Vik et al . [24] for a detailed description of this model including model diagram , differential equations and a CellML implementation . Out of the 86 model parameters we chose 34 to mediate the effects of genetic variation ( Table 1 and Table S1 ) . Because the genotype to parameter map for parameters describing ion channel properties may in general be much more straightforward than what is the case for many others , we picked mainly parameters describing affinities , conductivities and ion permeabilities for the ion channels and pumps underlying four potassium outward currents , one calcium current , one chloride current , one sodium current , the sodium-calcium exchangers , the sarcoplasmic reticular calcium ATPase ( SERCA ) , the sodium potassium pump , cytosolic calmodulin , the ryanodine receptors on sarcoplasmic reticulum and the calcium handling processes within sarcoplasmic reticulum . To ensure some realism in the construction of the genetic structure of our in silico populations in terms of allele frequencies and LD patterns , we extracted HapMap3 data [39] for 2 , 000 evenly spaced SNPs ( ∼5000 nucleotides apart ) for each of the first 20 autosomal chromosomes . We extracted genotypes for the 40000 SNPs for the 1301 individuals in the 11 HapMap 3 populations . We then expanded this into a population of 5000 individuals by use of the Python package simuPOP [42] . The population expansion by simuPOP maintained allele frequencies and LD patterns in accordance with the HapMap 3 data . Mutations were introduced based on a symmetric diallelic mutation model , all recombinations were based on genetic maps estimated from the HapMap data and migrations between the 11 subpopulations were allowed for . The mutation rate , migration rate and number of generations used as input to the simuPOP population expansion were 1e-8 , 0 . 001 and 500 , respectively . Assuming a purely additive genetic model , 400 causal SNPs were randomly sampled from the virtual genome for each of the 34 parameters selected to mediate genetic variation . The genotype to parameter mapping for each parameter was set up by defining the 5 , 000×40 , 000 genotype matrix G , where each element gij denoted the genotype of individual i at SNP j ( −1 for the homozygous with the least frequent allele , 0 for the heterozygous and 1 for the homozygous with the most frequent allele ) . We then constructed for each parameter the 40 , 000x1 relative effect vector E , where element ej was sampled from a Laplace ( 0 , 0 . 0035 ) distribution if the j-th SNP was among the 400 parameter-specific causative SNPs , and set to 0 otherwise ( the relative effect being defined as the percentage increase or decrease of the baseline parameter value ( Table 1 and Table S1 ) ) . The 5000-element vector of parameter values for all individuals was then computed as p ( GE+1 ) , where p is the baseline value . With this procedure , each of the focal 34 parameters was varied within ∼±35% of its baseline value , and for each causal SNP , the heterozygous individuals were assigned the baseline parameter value ( Table 1 and Table S1 ) . The ±35% parameter variation range was chosen as a compromise between getting ample genetic signals and avoiding too many physiologically unrealistic phenotypes . We also tested a genetic model with 200 causative SNPs for each parameter , the only difference being that the standard deviation of the Laplace distribution was set to 0 . 0049 . Cellular phenotypes for individual parameter sets were generated by a virtual experiment of constant pacing as described in Bondarenko et al . [41] . The potassium current was stimulated by −15 V/s for 3 ms at the start of each stimulus interval . Convergence was checked by comparing successive intervals with respect to the initial value of each state variable as well as the integral of its trajectory over that interval . A running history of 10 intervals was kept , and after each interval we checked for a match ( within a relative tolerance of 5% for all state variables ) against the previous one . This was done for three different pacing rates with stimulus intervals of intervals 100 , 200 and 300 ms , respectively . The cell dynamics was categorized as “failure” if it did not converge to non-alternating dynamics within 10 minutes of simulation time . The Python code of the heart cell model was autogenerated from CellML [43] , using the code generating service available at the CellML repository ( www . cellml . org ) . The equations were integrated using the CVODE solver [44] with a Python wrapper . Eight scalar phenotypes ( see Table 2 and Table S2 ) were extracted from each computed action potential and calcium transient curve: the initial value ( apbase and ctbase ) , the amplitude ( apamp and ctamp ) , the peak value ( appeak and ctpeak ) , the time to peak value ( apttp and ctttp ) , the time to 25% , 50% , 75% , and 90% of the initial base value ( apd25 , apd50 , apd75 , apd90 and ctd25 , ctd50 , ctd75 , ctd90 ) . We removed individuals with physiologically unrealistic phenotypes within each of the 100 datasets analyzed . The exclusion criterion was based on the inter-quartile range ( IQR ) ; points that were more than twice the IQR above the third quartile or below the first quartile were excluded . Each filtered data set , containing 4000–5000 individuals , was divided into a training set of 2500 individuals and a test set consisting of the remaining individuals . The training data set was used to detect causal SNPs , compute the false positive rate and sensitivity characteristics . The test set was used to estimate the phenotypic variation accounted for by the detected SNPs . The same GWAS procedure was used for each parameter and each phenotype . The quantitative trait association analysis was performed with the program PLINK [45] on the training data . We used a threshold of 1e-5 on the Bonferroni-corrected p-value from PLINK to determine the set of significant SNPs . The detected SNP set and associated discovery rates were defined as follows . Let Si denote the set of significant SNPs from GWAS on the i-th parameter and let Ci denote the causal SNPs set of the i-th parameter . The set of detected SNPs of the i-th parameter was then computed as Di = Si∩Ci , and the discovery rate of i-th parameter was computed as di = |Di|/|Ci| . The union of causal SNP sets for parameters defined the causal SNP set underlying all cellular phenotypes , and the detected SNP set and the discovery rate for each cellular phenotype was computed in the same way as for each parameter . The set of false positive SNPs of the i-th parameter or phenotype , Fi , consists of SNPs in the set of significant SNPs Si that are not in the causal SNPs set Ci . . The false positive rate of the i-th parameter or phenotype was defined as the number of false positive SNPs in Fi divided by the number of signals in Si , |Fi|/|Si| . To quantify explained genetic variance a multiple regression model was constructed by regressing the phenotype or parameter value of the training set on the causal SNPs detected by GWAS ( similar to the weighted genomic profile approach in [46] ) . Then the phenotypes of test set individuals were predicted using the corresponding fitted models . We measured the explained variation by the R2 values from regressing observed values on predicted phenotypic values for the individuals in the test set . We quantified the linear sensitivity [47] of each phenotype to each parameter using linear regression in the training set . For each high-level phenotype and Monte Carlo simulation we used the 2500 simulated phenotypes as response and performed a series of univariate regressions each time with a single parameter as predictor . We measured global sensitivity by the coefficient of determination ( R2 ) .
The proportion of true causative SNPs detected by GWAS was as expected substantially higher for the parameters than for the cellular phenotypes ( Figure 2 and Figure S2 for the 200 SNPs case ) . Median detection rates of causal SNPs were in the range 3 . 5%–4% after GWAS directly on parameter values ( Figure 2A ) , and this number dropped to ∼0 . 05% for GWAS studies on action potential phenotypes and ∼0 . 02% for calcium transient phenotypes ( Figure 2B ) , and the corresponding figures in the 200 SNPs case were 8–8 . 5% , ∼0 . 16% and ∼0 . 08% . The low overall detection rates were to be expected since we sampled SNP effects from an L-shaped distribution resulting in datasets where a small proportion of the SNPs underlying a given parameter will explain a substantial part of the variation . The main explanation for the decrease in detection rates is that the number of causal SNPs increases 34 times and the relative effects of all causal SNPs decrease , making them harder to pick up . Another , probably less important , phenomenon contributing to lower detection rates at the higher-level phenotypes is that going from parameter level to the system-level phenotype introduces nonlinearities in the SNP effects , and standard GWAS methods pick up only the additive part . The difference between parameter and cellular phenotypes is also evident when looking at the amount of phenotypic variance explained by SNPs detected in the GWAS ( Figure 3 and Figure S3 for the 200 SNPs case ) . The median explained variance is typically in the range 30–40% for parameter phenotypes ( Figure 3A ) , 10–20% for action potential phenotypes and ∼5% for calcium transient phenotypes ( Figure 3B ) . The proportion of phenotypic variance explained by detected SNPs was on average 2 . 6 ( 2 . 0 in the 200 SNP case ) and 5 . 6 ( 3 . 9 for the 200 SNPs case ) times higher for a parameter phenotype than for an action potential and calcium transient phenotype , respectively . However , when we made use of the SNPs detected for parameters we were able to explain 1 . 8 and 3 . 9 times ( 1 . 6 and 2 . 9 times for the 200 SNPs case ) more of the phenotypic variance of the action potential and calcium transient phenotypes , respectively , approaching the levels obtained for the parameters ( Figure 3C ) . We also calculated the explained variances with all significant SNPs and obtained similar results . This suggests that our approach can be tested empirically in a straightforward way . The gain in explained variance by using parameter-associated SNPs was not as dramatic for the action potential phenotypes as for the calcium transient phenotypes ( Figure 3C ) , but even in this case the gain in number of identified SNPs was on average 13 . 9× ( 12 . 3 for the 200 SNPs case ) . The corresponding figure for the calcium transient phenotypes was 39 . 4× ( 26 . 5 for the 200 SNPs case ) . Because these additional SNPs are attached to one or more parameters describing specific biological processes or features that are causally related according to the functional structure of the mathematical model , the gain in our causal understanding of the genotype to phenotype map may be substantial . Both the detection rate of causal SNP and the variances explained for the calcium transient phenotypes were overall significantly lower than those for the action potential phenotypes ( Figure 2B and 3B ) . We investigated this further by a linear global sensitivity analysis of how variation in the cellular phenotypes depended on variation in the parameters , and compared this with the number of causative SNPs for each parameter detected by performing GWAS on high-level cellular phenotypes . We found that the GWAS results for the two cellular phenotype groups are predominantly a consequence of the sensitivity structure of the dynamic model ( Figure 4 and Figure S4 for the 200 SNPs case ) , and that the action potential phenotypes are overall more sensitive to fewer parameters than the calcium transient phenotypes . The only exception to this latter pattern is the parameter Kup , quantifying the affinity of SERCA to calcium ions ( Figure 4A ) . It has a substantial impact on the calcium transient base value phenotype ( ctbase ) , and the amount of variance explained by the SNPs detected for this phenotype is on par with the action potential phenotypes ( Figure 3B ) . This suggests that SNPs associated with traits that are sensitive to few parameters will have a higher penetrance than SNPs associated with traits that are sensitive to many parameters for a given model resolution . Moreover , the results imply that the more poly-parametric the sensitivity profile of a model phenotype is , the more will be gained in terms of added explained variance by performing GWAS on parameters . On the other hand , the results also imply that a sensitivity analysis can be used to systematically reveal hotspots for genetic variation underlying a complex trait and thus guide a parameter phenotyping program . Within this framework a SNP affecting a parameter to which the focal higher-level phenotypes are not very sensitive will have little impact on these phenotypes unless it is highly penetrant at the parameter level . GWAS methods are well known for producing false positives due to multiple testing and high LD between SNPs . A typical GWAS block of SNPs in high LD is often reduced to a subset of tagSNPs in low LD ( typically with a pairwise correlation <0 . 2 ) . The GWAS methods are aimed at identifying significant tagSNPs , and the task of distinguishing the causal SNPs from false positives in high LD has to be done with other methods such as functional studies of candidate SNPs . Our approach is not intended to solve this problem ( but see e . g . [48] , [49] for reviews of methods for identifying causal variants after GWAS ) and in our study the increases detection rate for parameters is accompanied by an expected increased false positive rate ( Figure S1 and Figure S5 for the 200 SNPs case ) . However , as parameters as a rule are closer to mechanism than higher-level phenotypes , it should be noted that to do GWAS on parameters could become very instrumental for identifying candidate mechanisms and genes for follow up studies . We envision that ongoing efforts such as the RICORDO project [50] aimed at developing semantic interoperability for biomedical data and models will facilitate bioinformatic identification of candidate mechanisms and genes from cGP model sensitivities and GWAS results on parameter phenotypes . We made deliberate use of the simplest possible genotype to parameter map in this study . A more complex map incorporating genetic dominance and various types of epistasis [51] would have diminished the SNP discovery rates and the explained variances of the parameters . However , this reduction in penetrance would apply equally well at higher phenotypic levels , and so would not affect our conclusions . We did not put any environmental variation on the parameters as we deemed this unnecessary in a context where the main focus was to compare the genetic signal strength at the parameter and cellular phenotype levels . However , in future studies this aspect needs to be taken into account in order to make quantitative assessments of how well we will be able to pick up genetic signals as function of environmental variation . Our approach will remain useful in conjunction with future advances in statistical GWAS methodology , as it is applicable to any phenotypic variation that can be described by computational physiological modeling , irrespective of its position in the phenotypic hierarchy . Even in those cases where the parameters of a computational model are quite high-level phenotypes , our results suggest that one will be able to gain insights about the genotype to phenotype map that would otherwise be challenging to achieve . There has been an enormous expansion in efforts to model complex biological systems the last decade , and steadily expanding model repositories such as http://www . cellml . org and http://biomodels . net facilitate exchange and reuse of such models . Illustratively , our study benefited from the reuse of a model available in CellML format . Future development of the cGP approach and systems genetics in general will benefit greatly from these standards and online resources as well as modeling efforts like the Virtual Physiological Human ( http://www . vph-noe . eu ) . Reflecting upon how to improve the current performance of large-scale GWA studies aiming to find the genetic determinants underlying complex diseases , Dermitzakis and Clark stated recently that “A major breakthrough will be to predict and interpret the effect of mutational and biochemical changes in human cells and understand how this signal is transmitted spatially ( among tissues ) and temporally ( spanning development ) ” [52] . Our results suggest that combining GWAS methodology with a mature phenomics technology guided to fit the needs of computational physiology [53] , may contribute substantially to making this vision come true .
|
Despite an ever-increasing number of genome locations reported to be associated with complex human diseases or quantitative traits , only a small proportion of phenotypic variations in a typical quantitative trait can be explained by the discovered variants . We argue that this problem can partly be resolved by combining the statistical methods of quantitative genetics with computational biology . We demonstrate this for the in silico genotype-to-phenotype map of a model heart cell in conjunction with publically accessible genomic data . We show that genome wide association studies ( GWAS ) on model parameters identify more causal variants and can build better prediction models for the higher-level phenotypes than by performing GWAS on the higher-level phenotypes themselves . Since model parameters are in principle measurable physiological phenotypes , our findings suggest that development of future phenotyping technologies could be guided by mathematical models of the biological systems being targeted .
|
[
"Abstract",
"Introduction",
"Methods",
"Results/Discussion"
] |
[
"systems",
"biology",
"genetics",
"biology",
"computational",
"biology",
"genetics",
"and",
"genomics"
] |
2012
|
Parameters in Dynamic Models of Complex Traits are Containers of Missing Heritability
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In motor tasks , errors between planned and actual movements generally result in adaptive changes which reduce the occurrence of similar errors in the future . It has commonly been assumed that the motor adaptation arising from an error occurring on a particular movement is specifically associated with the motion that was planned . Here we show that this is not the case . Instead , we demonstrate the binding of the adaptation arising from an error on a particular trial to the motion experienced on that same trial . The formation of this association means that future movements planned to resemble the motion experienced on a given trial benefit maximally from the adaptation arising from it . This reflects the idea that actual rather than planned motions are assigned ‘credit’ for motor errors because , in a computational sense , the maximal adaptive response would be associated with the condition credited with the error . We studied this process by examining the patterns of generalization associated with motor adaptation to novel dynamic environments during reaching arm movements in humans . We found that these patterns consistently matched those predicted by adaptation associated with the actual rather than the planned motion , with maximal generalization observed where actual motions were clustered . We followed up these findings by showing that a novel training procedure designed to leverage this newfound understanding of the binding of learning to action , can improve adaptation rates by greater than 50% . Our results provide a mechanistic framework for understanding the effects of partial assistance and error augmentation during neurologic rehabilitation , and they suggest ways to optimize their use .
When learning to swim , the proper stroke motion is usually taught on the pool deck . Although a student might seem to have mastered this motion on dry land , upon entering the water she will have difficulty in accurately reproducing it underwater . However , after many laps , the student eventually learns to produce the pattern of motor output that leads to the proper stroke motion while swimming . This learning occurs via the formation of internal models of the physical dynamics experienced which allow the programming of movement to contend with the dynamics of the environment [1]–[4] . These internal models have been shown to predict the dynamics of the environment as a function of motion rather than as a function of time [5]–[8] – a strategy that makes sense in light of the viscoelastic and inertial physics of our own limbs and the objects we interact with . Consequently , the neural plasticity which underlies this learning must establish associations between motion state ( i . e . , position and velocity vectors ) and motor output which can counteract environmental forces . Although the existence of these associations has been well established , the mechanism by which they form is not yet understood . How does this state-dependent learning arise during the course of motor adaptation ? One possibility is that on individual trials , an internal model of the environment is updated based on a combination of the errors experienced and the motion plans that led to those errors . Another possibility is that internal models are updated based on errors experienced in combination with the actual motion states associated with those errors . It is remarkable that previous work on motor learning in neural systems has widely assumed the former [4] , [9]–[16] , despite the fact that direct evidence for this hypothesis is scant . The idea that learning is associated with the motion that was planned ( plan-referenced learning ) is especially pervasive in the learning rules of the algorithms that have been proposed to model the process of adaptation in the neuromotor learning literature [4] , [9] , [11]–[12] , [15] , however it is difficult to find work that addresses the validity of this assumption , explores its implications or provides a clear rationale for its use . The machine learning community has developed , in parallel , a series of algorithms for updating internal models in robotic systems . Interestingly , these algorithms almost uniformly involve learning rules in which internal models are updated based on a combination of the errors experienced and the actual motion associated with those errors ( motion-referenced learning ) rather than the motions that were planned [17]–[21] . The choice of these learning rules is grounded in the idea that adaptive changes should be provably stable in the sense that , under a set of reasonable assumptions , updated internal models should never result in worse performance [17]–[21] . Here we ask the question: Do the associations between motor output and motion state formed during human motor learning arise from adaptation based on planned or actual motions ? The answer to this question is important not only for theories of motor control , and issues of stability during learning , but also because knowledge of how associations are formed during motor learning can be leveraged to improve the efficiency of training procedures . Motor adaptation can be described as the process of tuning motor output to reduce the errors between plan and action . Thus the associations between motion state and motor output formed during this process result from the way that responsibility for these errors is assigned . This is known as a credit assignment problem . This problem can be posited as the task of assigning blame after an error is experienced to the set of actions that would be most likely to give rise to similar errors in the future . This set of actions could then be modified in order to improve performance in subsequent trials . Viewed in this way , the distinction between plan-referenced learning ( PRL ) and motion-referenced learning ( MRL ) corresponds to whether the blame for motor errors should be assigned to the planned versus actual motion . Consequently , the amount of adaptation on a given trial will be determined by the magnitude of the error , however the location of the adaptation ( which future motions will benefit from the adaptation ) will be determined by the credit assignment mechanism . Here we studied the generalization of motor adaptation to untrained conditions in order to elucidate the credit assignment mechanism used by the CNS , and then used our understanding of this mechanism to design a training paradigm that takes advantage of it to improve the efficiency of motor adaptation .
The adaptations that would occur at different stages of training for reaching arm movements in a velocity-dependent force-field ( FF ) for the PRL and MRL credit assignment hypotheses are shown in Figure 1 . The green shaded region around the planned motion – which is essentially straight toward the target for short ( 10 cm ) movements [22] – represents the space of future motions which would benefit from the adaptation to the greatest degree under PRL ( Figure 1A ) . Alternatively , each red shaded region represents the space of future motions which would benefit maximally under MRL . A more direct visualization of the adaptive changes predicted by each credit assignment hypothesis can be made by representing motion and the resulting adaptation in velocity-space rather than position-space , since the adaptation to the velocity-dependent dynamics studied in the current series of experiments is believed to be mediated by an internal model largely composed of velocity-dependent motor primitives [8] , [10] , [12]–[13] , [23] . These primitives are the learning elements which contribute to the compensatory motor output ( i . e . , compensatory force ) in a velocity-dependent manner . Figure 1B shows how individual motor primitives would adapt based on PRL versus MRL credit assignment early on in training . Here each circle represents a single motor primitive ( centered at its preferred velocity ) with a color intensity denoting the amount of adaptation that would arise from the illustrated trial . The left and right panels of Figure 1B show the adaptations predicted by PRL ( green ) and MRL ( red ) , respectively . As in Figure 1A , adaptation is centered on the planned motion for PRL and centered on the actual motion for MRL . As training proceeds over the course of several trials , the activation levels of the adapted primitives would continue to increase . This continued increase in activation ( not illustrated ) leads to increased compensatory force , resulting in greater compensation of the external dynamics and thus straighter trajectories . Note that the adapted primitives would be noticeably different for the two credit assignment hypotheses early in training , but would overlap late in training as force compensation increases and the planned and actual motions converge as illustrated in Figure 1A . Given the different implications that the PRL and MRL credit assignment mechanisms have for motor adaptation , we can assess which one is favored by the CNS by asking a simple question: After training , which motions gain the most benefit from the induced adaptation ? The motions that were planned or the motions that were experienced ? Since the mechanism for credit assignment determines which motions will benefit from adaptation on a particular trial , we studied how motor adaptation to a single target direction generalizes to neighboring motion directions . If a particular motion is trained , the pattern of generalization can be viewed as a record of the history of credit-assignment for the errors experienced during a training period . Specifically , the amount of generalization in the directions neighboring the trained movement constitutes the set of actions that the motor system believes should be adapted based on the history of errors experienced . Therefore , PRL and MRL should give rise to different patterns of generalization . In order to cleanly distinguish between these hypotheses , we designed an experiment in which the planned motion and the actual motion were maintained to be distinct from one another during the entire dataset so that the patterns of generalization predicted by PRL vs . MRL would be very different from one another . This is a challenge because , training a motor adaptation generally results in improved performance such that the actual motion converges onto the planned motion , and such a scenario could hamper the ability to clearly distinguish between the PRL and MRL hypotheses . Thus , we designed an experiment in which actual motion would not converge onto planned motion during the course of training , resulting in enduring differences between the predictions of these two hypotheses . To accomplish this , subjects were exposed to a training period consisting of short , successive blocks of movements towards a single target location with a force-field ( FF ) that alternated between clockwise ( CW ) and counterclockwise ( CCW ) directions from block to block ( see Figure 2A ) . The magnitudes of the CW and CCW FFs were , respectively , 9 and −9 N/ ( m/s ) . In these FFs , the peak force perturbations were 2 . 7 and −2 . 7 N , respectively , for an average movement with a peak speed of 0 . 3 m/s . The FF blocks were short enough ( 7±2 trials ) that neither the CW nor the CCW FF could be learned very well before unlearning with the opposite FF occurred . After subjects were exposed to a number of these interfering FF cycles , we measured the generalization of adaptation to untrained movement directions with error-clamp ( EC ) trials ( see Materials and Methods for details ) . The predictions of PRL and MRL are strikingly different for this experiment . For the PRL hypothesis , since the adaptation is associated with motor primitives centered at the same target direction for both FFs ( Figure 2B top panel , blue and orange traces ) , the balanced exposure to these opposite FFs would lead to cancellation of the CW and CCW FF learning resulting in near zero adaptation at the trained target direction and the adjacent directions ( Figure 2B , dashed green trace ) . Note that although target locations are identical between CW and CCW FF trials , the actual movement directions differ . The CW FF perturbs motion towards smaller movement angles whereas the CCW FF does the opposite . Therefore , MRL predicts that smaller movement angles would be preferentially associated with adaptation appropriate for the CW FF ( blue trace in the bottom panel of Figure 2B ) , whereas higher movement angles would be preferentially associated with adaptation appropriate for the CCW FF ( orange trace in the bottom panel of Figure 2B ) . This would lead to the bimodal pattern of generalization illustrated in Figure 2B ( red dashed trace ) . We trained one group of subjects in this FF interference paradigm at a target location of 270° . We found that target directions smaller than the training direction consistently display generalization appropriate for the CW FF ( negative ) whereas target directions greater than the training direction display generalization appropriate for the CCW FF ( positive ) . This is consistent with the bimodal generalization pattern predicted by MRL ( compare the blue and red traces in Figure 2C: r = 0 . 92 , F ( 1 , 7 ) = 36 . 87 , p<0 . 001 ) and quite different from the essentially flat pattern predicted by PRL ( green trace ) . Correspondingly , we found the adaptation levels at the target directions corresponding to the peaks of the predicted generalization pattern ( −30° and +30° , see Figure 2C ) to be significantly different from one another ( t11 = 7 . 26 , p<9×10−6 ) and from zero ( t11 = 5 . 95 , p<5×10−5 for −30° , and t11 = 3 . 89 , p<0 . 002 for +30° ) . These results provide direct evidence for MRL by matching the complex pattern of generalization predicted by it . In our experiment we balanced the direction of the FF that was presented before testing generalization , nevertheless , we noticed a small bias in the generalization function at the training direction consistent with a bias in adaptation level that we observed during the training period ( see Figure S1 and Text S1 ) . This bias is compatible with other results showing somewhat faster learning for a CW FF [8] . In order to eliminate the possibility that this bias or the target location we chose for training ( 270° ) might have somehow contributed to the generalization pattern we observed in the data , we trained a second group of subjects in a version of this experiment that was designed to eliminate the bias and provide training at another target location ( 60° ) . We eliminated the bias by unbalancing the number of CW versus CCW FF trials in each cycle in this second group of subjects ( see Text S1 ) . We found that the close match between the pattern of generalization that these subjects displayed ( Figure 2C , grey trace ) and the pattern predicted by MRL persisted under these conditions ( r = 0 . 93 , F ( 1 , 7 ) = 42 . 61 , p<0 . 001 ) . Correspondingly , the adaptation levels at −30° and +30° were significantly different from each other ( t9 = 5 . 37 , p<3×10−4 ) , and significantly different from zero ( t9 = 3 . 72 , p<0 . 003 for −30° , and t9 = 4 . 38 , p<9×10−4 for +30° ) . Together , these results provide compelling evidence for MRL as the mechanism for credit assignment in motor adaptation . We note that Equations 3 and 4 used for our simulations incorporate local motor primitives that are functions of the initial movement direction ( θ ) rather than of the full time series of the velocity vectors encountered during each trial . This might seem an inappropriate choice since , as we discussed above , velocity-dependent motor primitives are thought to underlie the learning of velocity-dependent dynamics [8] , [10] , [12]–[13] , [23] . However this approximation is a good one when movements are approximately straight , which is essentially the case for the first 400 ms of the movements considered in our study . This approximation , of course , breaks down at the end of the movement when the initial movement direction no longer describes the velocities experienced . However , the amplitudes of the velocity vectors during the end-movement correction are quite low and so the unmodeled spread of learning to the actual motion experienced in this correction phase should have relatively little effect since at low velocities , viscous dynamics have small consequences . This effect can be visualized in the left panel of Figure 1B which shows that the end-movement correction which has a velocity vector that points to the second quadrant would only excite velocity-dependent primitives near the origin under MRL . Note that the separation of the peaks in the bimodal generalization pattern predicted by MRL ( red dashed line in Figure 2C ) results from the size of the errors experienced during training . Consequently , larger force-field perturbations which induce larger errors would result in greater separation between the peaks . However , the separation between the peaks ( about 60° ) is predicted to be greater than the separation between the average errors experienced in the two force-fields ( about 25° ) . There are two reasons for this . The first is that more adaptation occurs on trials with larger errors than those with smaller errors , skewing the center of adaptation for each force-field outwardly from the mean experienced error . The second reason is illustrated in the lower panel of Figure 2B: When the patterns of generalization for the positive and negative force-fields are summed , resulting in a bimodal generalization pattern for MRL , the peaks of this bimodal generalization pattern ( red ) are separated by an even greater distance than the peaks of the positive ( orange ) and negative ( blue ) components because the amount of cancellation between these components is greater at movement directions corresponding to smaller rather than larger errors resulting in further outward skew . Previous work has attempted to measure the generalization functions ( GFs ) associated with learning a single FF . MRL predicts that these GFs will be shifted toward the motion directions experienced during training . Many of these studies have estimated GFs from complex datasets using a system identification framework [10] , [12]–[13] . However the implementation of this framework assumed PRL in these studies , thus preventing a straightforward interpretation of their results . In one study [24] a simpler generalization experiment was conducted , in which subjects were trained with a single FF to a single target location , after which the resulting GF was measured . Because the actual motions approached the planned motions late in training , the shifts predicted by MRL would be subtle . Furthermore , the ability to detect shifts in the generalization function was hampered by a coarse sampling of the generalization function ( 45° ) . Nevertheless , careful inspection of these GFs consistently reveals subtle shifts towards the motions experienced during training as predicted by MRL . However , it is difficult to be certain whether if the shifts observed in this study result from MRL rather than innate biases in generalization functions because only a single FF direction was studied . Innate biases might stem from biomechanical asymmetries or direction-related biases in adaptation . We therefore performed a pair of single-target , single-FF experiments in order to compare the shifts in generalization associated with opposite FFs . The results of these experiments confirm the existence of subtle but significant shifts in generalization [25] . The magnitudes and the directions of these shifts are consistent with the MRL hypothesis [25] . Insights into the mechanisms for learning in the CNS can provide a platform for creating training procedures that leverage these insights to improve the rate of learning – an important goal for both motor skill training and neurologic rehabilitation . With our new understanding of how the CNS solves the credit assignment problem , we looked into the possibility of designing a novel training paradigm to take advantage of this knowledge . A key consequence of plan-referenced learning is that this mechanism for credit assignment would result in a match between what is learned and what is commanded on the next trial if the same motion plan is repeated from one trial to the next during training – like when aiming a dart at the bull's eye repeatedly . In contrast , motion-referenced learning would result in a mismatch . Motion-referenced learning , therefore , predicts that the process of training an accurate movement to a given target location in a novel dynamic environment would be inefficient if that target were repeatedly presented at the same location during training ( single-target training , STT ) as illustrated in Figure 3 . This inefficiency arises because the motion experienced during training does not coincide with the motion that is to be learned , resulting in limited overlap between the motion-referenced learning that occurs and the learning that is desired . The aforementioned inefficiency can be ameliorated by a paradigm which continually changes the locations of the targets presented during the training period as shown in Figure 3 , second column . In this training paradigm , target directions would be shifted from one trial to the next so that the actual motion experienced repeatedly lines up with the motion to be learned . For the CW FF depicted in Figure 3 , this corresponds to left-shifted training ( LST ) . Initial target locations are placed with large leftward shifts with respect to the desired learning direction – in anticipation of the large rightward initial errors with respect to the target location . These leftward target shifts are then gradually reduced as learning proceeds and errors become smaller , in order to maintain alignment between the actual motion experienced and the movement to be learned . The MRL hypothesis predicts that the LST training paradigm should produce faster learning than the standard STT paradigm used in previous motor adaptation studies in which a single target direction was trained [24] , [26] . We tested this idea by comparing the learning curves associated with these training paradigms for adaptation to a clockwise viscous curl force-field . A different group of subjects was studied on each paradigm to avoid the effects of savings [27]–[29] . As a control for a possible increase in attention associated with changing target locations in the LST paradigm , we tested a third group of subjects with a right-shifted training ( RST ) paradigm . Here targets were shifted to the right , mirroring the target positions in the LST paradigm . The MRL hypothesis would predict slower learning for RST than STT or LST because right-shifted targets in a rightward pushing force-field would result in reaching movements even farther away from the desired learning direction than those expected in STT ( see Figure 3 , third column ) . In contrast the PRL hypothesis would predict fastest learning for the STT paradigm and identical learning rates for the LST and RST paradigms because the STT paradigm creates perfect alignment between the desired learning and the planned motion whereas the LST and RST paradigms create misalignments between the desired learning direction and planned motion that are opposite in direction but equal in magnitude . We used a FF magnitude of 22 . 5 N/ ( m/s ) for these experiments – 2 . 5 times the magnitude used in Experiment 1 – in order to magnify the various misalignments discussed above . In all three paradigms , we measured learning at the desired learning direction ( 90° ) by pseudo-randomly interspersing 90° error-clamp trials among the training trials with an average frequency of 20% . We first collected data from a subset of subjects in the STT paradigm in order to estimate the evolution of directional errors across trials . We used this pattern of directional errors to determine the target shifts that would produce good alignment between experienced motion and desired learning direction for the LST paradigm ( see Materials and Methods ) . As shown in Figure 4A , we obtained a good match between motion direction and the desired learning direction ( 90° ) throughout the training period for the LST paradigm , so that misalignment between these directions was dramatically reduced compared to the STT paradigm . Correspondingly , the misalignment between motion direction and the desired learning direction was about twice as great for RST than for STT . The plots shown in Figure 4B illustrate how the adaptation patterns predicted by MRL and PRL would evolve as training proceeds for the training paradigms discussed above . Note that adaptation spreads across a limited range of movement directions consistent with local generalization [24]–[26] , but the alignment between adaptation and the desired learning direction ( 90° ) varies from one paradigm to another ( STT vs . LST vs . RST ) , and from one credit assignment hypothesis to another ( PRL vs . MRL ) . The darkened dots which highlight a slice through these plots at 90° illustrate the amount of adaptation associated with the desired learning direction . These simulations show that the PRL hypothesis predicts that in the STT paradigm , credit assignment will be perfectly aligned with the desired learning direction ( 90° ) throughout training . PRL also predicts an equal but opposite pattern of misalignments between credit assignment and desired learning for the LST and RST paradigms ( Figure 4B ) . These misalignments are initially large but become attenuated during the course of the training because planned and actual motions converge . This results in simulated learning rates that are highest for the STT paradigm and lower , but identical , for the LST and RST paradigms under PRL ( Figure 4B–C ) . In contrast , the simulations for the MRL hypothesis show perfect alignment between the credit assignment and the desired learning direction for the LST paradigm . For STT , the MRL-based simulations show a gross misalignment between the credit assignment and the training direction . For RST , the misalignment is even greater ( Figure 4B ) . This results in learning rates that are predicted to be greatest for the LST paradigm , followed by the STT and RST paradigms , respectively ( Figure 4B , D ) . As with the PRL simulations , the misalignments become attenuated as training proceeds . Our experimental data show a clear difference between the learning curves obtained for the three training paradigms in the early stages of training ( first three EC trials; one-way ANOVA , F ( 2 , 87 ) = 14 . 57 , p<4×10−6 ) . The LST group displays the highest adaptation levels and the RST group displays the lowest adaptation levels as shown in Figure 4E . In particular , the LST group displayed an 86% increase in adaptation levels on the first EC trial and a 52% increase over the first three EC trials , whereas the RST group displayed a 59% decrease in adaptation levels compared to STT over the first three EC trials in the training period . Post-hoc comparisons between groups over the first three EC trials indicate that the LST group showed significantly greater learning than the RST group ( t58 = −5 . 05 , p<3×10−6 ) . This result is in keeping with the MRL prediction , but defies the PRL prediction of equal learning rates for these groups . Our data also shows that the LST group displays significantly greater learning than the STT group ( t58 = −2 . 17 , p<0 . 02 ) , in keeping with the MRL prediction , but opposing the PRL prediction of a greater learning rate for STT . We also find that the STT group displays significantly greater learning than the RST group ( t58 = −3 . 90 , p<2×10−4 ) , corroborating the group order predicted by the MRL hypothesis . These findings provide additional support for motion-referenced learning and demonstrate that a training paradigm that is designed to leverage knowledge about the mechanism for credit assignment can improve learning rates compared to standard training procedures . Inspection of the learning curve for the RST group reveals that the adaptation for the first EC trial after exposure to the FF actually dips a bit below zero . MRL predicts reduced learning for this group but would not predict opposite learning , consistent with the finding that the adaption level at this point , although nominally less than zero , is not significantly so ( t27 = −2 . 01 , p>0 . 05 ) . Additionally , we note that the third-to-last error-clamp trial in the baseline ( which is illustrated along with the full learning curve in Figure S2 ) displays an adaptation coefficient which dips below the average baseline and falls within the error bars of the first point in the RST learning curve , suggesting that the latter is not entirely outside the range of the data . Despite the differences in learning rate predicted by MRL-based credit assignment , angular errors should decrease as the training period proceeds . This results in reduced misalignment between prescribed and actual motion directions for the STT and RST groups , leading to a predicted convergence of the adaptation levels for all three groups ( see Figure S2 and Text S1 ) . Our data bears out this prediction: despite significant differences between groups early in the training period , we find no significant difference between groups late in the training period ( last three EC trials; one-way ANOVA , F ( 2 , 87 ) = 0 . 23 , p>0 . 05 ) . In addition , although we have shown that the MRL-based training paradigm ( LST ) increases the rate of adaptation , our results do not provide any information on the long-term retention for this adaptation . Further studies would be required to assess if the retention of the motor memories acquired using an MRL-based training paradigm is greater than that of memories acquired using single-target training paradigms .
Despite the lack of direct evidence in support of it , plan-referenced learning has been widely assumed in the motor adaptation literature , particularly in modeling work in which a credit assignment scheme must be chosen , even if implicitly so , in order for a learning rule to be defined [4] , [9]–[16] . Interestingly , Wolpert and Kawato ( 1998 ) assumed a hybrid credit assignment scheme: PRL for inverse-model learning and MRL for forward-model learning [4] . In principle , PRL is attractive because adaptation referenced to the previously planned motion would have the greatest effect on the same movement if it were repeated . In fact , Donchin et al . ( 2003 ) , which models motor adaptation with a PRL learning rule , contains what the authors maintain is a proof that PRL-based learning is optimal in their supplementary materials [10] . However inspection of this proof reveals that its derivation is based on the assumption that motor adaptation acts to maximize the benefit that would be accrued if the same movement were repeated . In other words , this proof investigated what the optimal credit assignment procedure should be for STT and found that PRL maximizes the benefit of motor adaptation for STT . Since PRL is optimal for STT , MRL must be suboptimal for STT ( as our simulations predict; see Figure 4B–D ) . This suggests that some training procedure other than STT would be optimal for MRL , and our data show that , for a clockwise FF , left-shifted training ( LST ) is indeed more effective than STT . Effectively , Donchin et al . ( 2003 ) assumed that a credit assignment procedure optimized for performance on STT would be used by the nervous system . Here we show that this is not the case . Instead , the error-dependent learning that occurs on a particular trial is referenced to the actual motion experienced on that trial rather than the planned motion , and as a result , STT produces slower learning than another training procedure ( LST ) . Thus the human motor system does not adapt with the mechanism that would have the greatest effect on the same movement if it were repeated . Why would this be ? The problem with PRL is that the dynamics experienced are generally functions of actual rather than planned motion . For example , the dynamics experienced from moving a small dense mass would be proportional to the actual rather than the planned acceleration of that mass . Note that the dynamics that subjects experienced in our experiments were also dependent on the actual motion state , i . e . , the force was based on the velocity of the actual rather than the planned motion . The key consequence of this state dependence is that since the force pattern experienced during a particular motion does not reflect the planned motion ( because it reflects the actual motion ) , the force pattern that would have been experienced if the planned motion were achieved is unknown . This means that , in principle , the error between the current motor output and the environmental dynamics acting on the planned motion adaptation is also unknown . Because this error is unknown , no learning rule for adaptation referenced to the planned motion can be guaranteed to reduce it . If , however , errors are small enough so that the dynamics experienced in actual and desired trajectories would be very similar to each other , plan-referenced learning schemes could converge because these schemes essentially assume equality between these dynamics . On the other hand , if errors are sufficiently large , using such a credit assignment scheme might result in unstable learning which does not converge on the desired motor output . Clearly , a credit assignment scheme that could lead to instability would be a liability for the CNS . The state dependence of physical dynamics insures that the force pattern experienced corresponds to the actual motion . Thus the error between the motor system's current estimate of the dynamics associated with the actual motion and the environmental dynamics associated with this motion can be determined . Because the motor output error corresponding to the actual motion can be determined , the motor output associated with it can be modified to reduce this error reliably , allowing for stable convergence of the motor output on the true environmental dynamics . This corresponds to motion-referenced learning . Interestingly , this reasoning is reflected in learning rules with mathematically provable stability that are widely used for the estimation of environmental dynamics in robotics and machine learning [17]–[21] , [30] . These learning rules must be motion-referenced in order for stability to be assured . One unfortunate consequence of motion-referenced learning is the suboptimal rate of motor adaptation observed if an individual were to repeatedly invoke the same motor plan when attempting to learn a novel task [30] . We demonstrate this suboptimality in the single-target training ( STT ) paradigm in Experiment 2 ( Figure 4 ) . Since adaptation proceeds according to the actual motion ( rather than the planned motion ) , the STT paradigm leads to adaptation that is not aligned with the desired learning direction so that adaptation proceeds at a slower rate than if the actual motion is aligned across trials as in the LST paradigm . Our finding of motion-referenced credit assignment during motor adaptation is , therefore , compatible with the idea that the CNS favors a stable learning algorithm ( MRL ) over one that maximizes the effect of learning if the same motion plan is repeated at the expense of stability ( PRL ) . Recent studies have provided evidence for reduced learning rates for large errors [31]–[33] . One of these studies proposed the rationale that this occurs because the motor system sees large errors as less relevant than small errors [31] . However , note that in these studies the adaptation was measured not along the motion direction experienced during the training trials , but along the direction of the previously planned movement – equivalent to STT . Therefore the decreased learning rates associated with large errors observed in these studies may be , at least in part , due to misalignment in motion-referenced credit assignment , because larger errors lead to increased misalignment between desired and actual motion during adaptation . This results in a corresponding misalignment between credit assignment and the desired learning , as illustrated in Figures 3 and 4 . Further work will be required to determine the extent to which the apparent reduction in learning rates that has been observed with large errors reflects this misalignment versus a true reduction in the ratio between the amount of adaptation and the size of the error . A recent study by Diedrichsen et al . [34] , provides evidence for the occurrence of use-dependent learning alongside error-based learning in reaching arm movements . This use-dependent learning describes a mechanism by which the trajectory of motion in task-irrelevant dimensions is gradually adapted to resemble the motion experienced on preceding trials . Therefore , use-dependent learning resembles motion-referenced learning in the respect that they both depend on the actual motion experienced . However , as noted by Diedrichsen et al . [34] , use-dependent learning is oppositely directed from motion-referenced error-dependent learning when a perturbing force is experienced . This is because use-dependent learning would act to increase the extent to which future motions resemble the perturbed movement whereas ( motion-referenced ) error-dependent learning acts to oppose the effect of this force in order to reduce the extent to which future motions resemble the perturbed movement . A second key difference is that use-dependent learning is readily observed along task-irrelevant dimensions , but is either greatly reduced or entirely absent along task-relevant dimensions [34] , whereas the motion-referenced learning that we demonstrate in the current study acts primarily along task-relevant dimensions in which error can be readily defined . Taken together , the identification of motion-referenced learning and use-dependent learning expand what we know about the role of sensory information in motor adaptation , in particular sensory information about motion . In addition to the role that this sensory input plays in computing motor errors , the motion-referenced learning and use-dependent learning mechanisms respectively explain how sensed motion is specifically associated with error-dependent changes in motor output to reduce the difference between plan and action , and how sensed motion can be used to adapt which motions are planned to begin with . Information about actual motion states is required for motion-referenced learning . This information can be acquired from delayed sensory feedback or estimated in real time through the use of a forward model , relying on an efference copy of the motor command and past sensory information [4] , [35]–[39] . However , since sensory feedback signals and efference copy are noisy , actual motion must be estimated from imperfect information . Several studies have shown that the motor system integrates prior expectations about motion with noisy sensory feedback in order to estimate actual motion in accordance with Bayes Law [40]–[42] . The influence of prior expectations should increase with the level of sensory feedback noise , and so Bayesian estimation should have greater effects on motion estimation and thus on motion-referenced adaptation when noise levels are high . What is the role of motion-referenced learning in the adaptation to a visuomotor transformation , where there is a dissociation between the actual motion of the hand and the actual motion of the cursor ? A definitive answer to this question will require further experimental work since the present study looks at the adaptation to new physical dynamics rather than visuomotor transformations . A priori , it would seem that for visuomotor transformations , learning should be associated with the actual motion of the controlled object ( cursor ) rather than with the actual motion of the body part that is exerting this control . If motor learning were associated with the actual body motion , it would be difficult to see how large visuomotor rotations could be learned at all , because even late in adaptation , an arbitrarily large mismatch would exist between the planned motion ( e . g . , the motion of the cursor to its target position ) and the actual hand motion . However , previous studies have shown that visuomotor rotations that are wider than the half-width of the generalization function for visuomotor rotation learning ( about 30° ) are readily learned [43]–[44] . A second point is that since ( a ) the motor planning during visuomotor transformation learning corresponds to the planned motion of the cursor ( rather than the hand ) , and ( b ) the relevant motor errors involve the relationship between actual and planned or actual and predicted cursor movements ( rather than hand movements ) [43] , [45] , it would seem logical that the learning resulting from errors in this task would be associated with the cursor as well . Linear state-space models with multiple time courses of adaptation [28] , [46] have been invoked as an explanation of savings – the phenomenon that describes the increase in learning rate when an adaption is relearned compared to the initial learning . However , even when complete behavioral washout of the learning is achieved , there appears to be some capacity for savings [29] . This effect cannot be captured by the aforementioned linear models , leading to the suggestion that significant nonlinearities arise even in simple motor adaptation experiments [29] . However , motion-referenced learning provides another possible explanation: Savings after washout may be due to a mismatch between the actual movement directions experienced in the initial learning and the washout trials rather than nonlinearities in the learning process . Such a mismatch would result in incomplete washout in the actual movement directions experienced during initial learning – similar to the residual direction-dependent adaptation that we demonstrate in Experiment 1 . Further work will be necessary to determine the extent to which this is the case , but if savings after washout resulted in part from a directional mismatch during washout , then the prediction would be that the amount of savings would be reduced if the washout trials spanned the movement directions experienced early in training , rather than being confined to a single target direction as in [29] . Studies with healthy subjects [47]–[48] and subjects with congenital and acquired cerebellar deficits [49]–[51] have provided evidence that the cerebellum participates in motor adaptation . It has been proposed that the simple spike firing of Purkinje cells in cerebellar cortex contributes to motor output and that error signals carried by climbing fibers modify the strength of the parallel fiber synapses onto Purkinje cells [11] , [48] , [52]–[53] . This plasticity alters the effect that the information carried in parallel fibers has on the output of Purkinje cells , and thus on motor output [52]–[53] . Since parallel fibers carry sensory feedback ( amongst other ) signals [38] , [54]–[55] , this plasticity alters the association between sensory feedback about the actual motion and future motor output and may represent a neural mechanism for motion-referenced learning . A common technique in neurorehabilitation is the use of partial assistance , where a therapist or device supplements movement in order to allow patients to better approximate a desired motion [56]–[58] . Since partial assistance reduces the difference between the actual and desired motions , our findings would suggest that it improves the alignment between the adaptation that is learned and the desired motion that is being trained . This would improve the efficiency of the training procedure . However , partial assistance would also reduce the magnitude of the motor errors that drive learning . These opposing effects may decrease the overall benefit of this procedure . Interestingly , a method known as error augmentation that can be thought of as essentially the opposite of partial assistance has recently been proposed as a means to improve the rate of motor learning during rehabilitation . In error augmentation , motor errors are increased beyond normal levels by transiently exposing patients to perturbations that are stronger than those that are to be learned [59]–[61] . The rationale behind this technique is that since error signals drive motor learning , increasing the size of this signal may improve the rate of learning . Our results indicate that , like partial assistance , error augmentation will result in two opposing effects . Whereas , partial assistance increases the alignment between the motion-referenced learning which will occur and the desired learning but reduces the magnitude of the error signal driving adaptation , error augmentation decreases the alignment between the motion-referenced learning which will occur and the desired learning but increases the magnitude of the error signal driving adaptation . Thus , unlike partial assistance , error augmentation may provide a robust error signal for learning , but could in fact lead to decreased learning rates by magnifying the misalignment between the desired motion to be learned and the learned motion in the experienced trials . The problem of opposing effects resulting from both of these training procedures could potentially be solved by the implementation of a training procedure analogous to the LST training we studied which aligned actual and desired movements , but with stronger-than-normal perturbations . Note that the design and implementation of a training procedure that aligns actual and desired motions is somewhat challenging . Even for the simple planar point-to-point movements we studied in Experiment 2 , we first ran another group of subjects to determine the magnitude of the target shifts employed in each trial of our LST paradigm . For training more complex natural motions the challenge will be even greater . With higher-dimensional complex movements , simple manipulations like the altered target position we used in our LST paradigm might not be nearly as effective as a more complex manipulation like the imitation of the entire time course of an altered motion in providing good alignment between actual and desired movement . However , if a training procedure can be created that improves the alignment of the actual motions experienced with the desired motion , even when motor errors are large , such a paradigm may be capable of simultaneously benefitting from increased error-dependent learning and improved transfer of adaptation to the desired motion – the best of both worlds from error augmentation and partial assistance . The improvement afforded by the LST paradigm or derivatives of it might even be more substantial if used in patients undergoing neurorehabilitation . For example , chronic stroke patients are able to adapt to dynamic environments , but display slower learning rates and higher residual errors than healthy controls [62]–[63] . Interestingly , our modeling efforts suggest that MRL-based training would have an even greater effect on subjects with these types of impairments , with the advantage of LST over STT predicted to be greater in magnitude and longer lasting as shown in Figure S2 , because the higher motor errors these subjects normally experience lead to greater-than-normal misalignment under STT ( see Text S1 ) . Further studies would be required to determine whether an MRL-based training paradigm could lead to clinically significant improvements in neurologically impaired subjects .
All experimental participants were naïve to the experimental purpose , provided informed consent and were compensated for their participation . All the experimental protocols were reviewed and approved by the Harvard University Committee on the Use of Human Subjects in Research ( CUHS ) . Subjects performed 10 cm reaching movements in the horizontal plane with their dominant hands while grasping the handle of a 2-link robotic manipulandum . Subjects were seated with their forearm leveled with the robotic manipulandum and supported by a sling . The subjects were presented with 1 cm-diameter circular targets displayed on a vertically oriented LCD monitor . The position of the subject's hand was represented on the LCD monitor by a 3 mm cursor . Position , velocity and force at the handle were measured with sensors installed in the manipulandum at a sampling rate of 200 Hz . The subjects were instructed to produce fast , continuous movements , and were provided visual feedback throughout the movement . Feedback about the movement time achieved was presented at the end of each movement . Ideal completion times ( 500±50 ms ) were signaled by an animation of the target while a chirp sound was played . For movement completion times that were below or above the ideal range the targets were colored blue and red , respectively . The mean peak speed for the movements in all experiments was 0 . 302±0 . 017 m/s . In certain movements , the subjects' trajectories were perturbed by velocity-dependent dynamics . This was implemented by a viscous curl force-field at the handle produced by the motors of the manipulandum , Equation 1 . ( 1 ) In this equation the constant B represents the viscosity associated with this force-field and has units of N/ ( m/s ) . Note that the direction of the force is always orthogonal to the direction of the velocity vector . We assessed the level of adaptation using methods described elsewhere [28] . Briefly , we measured the force pattern that subjects produced when their lateral errors were held to near zero values in an error-clamp [28] , [64]–[65] . We then regressed the measured force pattern onto the ideal force required to fully compensate for the force-field . The slope of this regression was used as the adaptation coefficient that characterized the level of learning . For a force profile that is driven by adaptation to a velocity-dependent force-field , our adaptation coefficient represents the size of the bell-shaped velocity-dependent component of the measured force profile . This velocity-dependent component of the measured force profile specifically corresponds to the force component targeted to counteract the velocity-dependent force-field perturbation . Twenty-eight individuals with no known neurologic impairment ( mean age = 19 . 9±1 . 8 years; 15 male ) were recruited for this experiment . The first twelve subjects practiced the reaching task in 9 different directions ( θ = 180° , 210° , 240° , 245° , 270° , 285° , 300° , 330° , 360° ) for 254 movements ( baseline ) , and were then trained to compensate velocity-dependent force-fields in a particular movement direction ( 270° ) for 672 movements ( training ) with the direction of the FFs alternating every 7±2 movements between CW ( B = 9 N/ ( m/s ) ) and CCW ( B = −9 N/ ( m/s ) ) . Thus the ratio of CW to CCW FF trials was 7∶7 . After blocks of 168 training ( FF ) trials , the pattern of generalization was measured in each direction during a testing block of 40 consecutive EC trials spread across these directions . The direction ( CW or CCW ) of the last FF presented before generalization testing was balanced across the four training blocks . A second group of subjects performed the same experiment but with different baseline/testing directions ( θ = −30° , 0° , 30° , 45° , 60° , 75° , 90° , 120° , 150° ) and training direction ( 60° ) . In this experiment the ratio of CW to CCW FF trials was 6∶8 for the first six subjects and 5∶9 for the subsequent ten subjects . The data from the subjects trained at 270° and that from the last ten subjects trained at 60° ( CW to CCW FF trial ratio of 5∶9 ) are shown in Figure 2C . The CW to CCW FF ratio was adjusted to eliminate the bias towards learning the CW FF we observed in the first 12 subjects – details are provided in Text S1 . The data for the subjects with the 6∶8 CW to CCW FF trial ratio are compared to the other datasets in Figure S1 . Ninety individuals with no known neurologic impairment ( mean age = 22 . 0±5 . 9 years; 44 male ) were recruited for this experiment . One group of subjects ( N = 30 ) were assigned to the single-target training ( STT ) paradigm . Here the subjects performed 75 movements in a single direction ( 90° ) to practice the reaching task ( baseline ) and then were exposed to a CW velocity-dependent force-field ( CW; B = 22 . 5 N/ ( m/s ) ) for 125 reaching movements to the same direction ( training ) . The learning level during baseline and training was assessed with randomly interspersed EC movements ( p ( EC ) = 0 . 2 ) . The mean trial history of angular errors 300 ms into the movement during force-field trials was obtained for this group of subjects and used to design the left-shifted ( LST ) and right-shifted ( RST ) training paradigms . In the LST paradigm , the directions of the reaching targets were adjusted by adding a smoothed fit of the mean trial history of angular errors from the first seventeen subjects of the STT experiment to the desired learning direction on the corresponding trial ( 90° ) . We did this so that when subjects reached to these shifted targets their actual motion would be expected to line up with the desired learning direction if the directional error on that trial was similar to that observed in the STT group as illustrated in Figure 3 . On the other hand , in the RST training paradigm we subtracted this trial history of angular errors from the STT experiment to the desired learning direction ( 90° ) . Therefore these target locations mirrored the LST target locations across 90° . We did this so that when subjects reached to these shifted targets their actual direction of motion would be deviated twice as much from the desired learning direction ( 90° ) as in the STT experiment . In the LST and RST paradigms subjects ( 30 on each group ) also performed 75 baseline movements and then performed 125 training movements using the same CW velocity-dependent FF that was learned by the STT paradigm group . The learning level during baseline and training was assessed by measuring the lateral force profiles produced during randomly interspersed EC trials ( p ( EC ) = 0 . 2 ) directed toward the desired learning direction ( 90° ) . We simulated the adaptation process for the STT , LST , and RST training paradigms for the PRL and MRL credit assignment schemes using the model equations and parameters described below and in Text S1 . However , in this case , since the experiments and simulations were not aimed at assessing generalization , error in the simulations was defined as the difference between the desired adaptation in the target direction and the actual adaptation in that direction . We simulated the adaptation process predicted by PRL and MRL for both experiments . We used linear state-space models [28] with local motor primitives to model the adaptation and its generalization ( see Text S1 for details ) . These are discrete ( trial-dependent ) error driven models , where the error is calculated as the angular difference between the planned movement direction and the actual movement direction , Equation 2 . ( 2 ) In the learning rules presented in Equations 3 and 4 , the adaptation , x , for given movement direction , θ ( θ can take on values encompassing the entire movement space ) , in a given trial , n + 1 , is the sum of the previous adaptation level for the same movement direction weighted by a retention coefficient , A , and the learning occurring in the current trial which is given by the product of the error in the current trial and a local motor primitive , B . For the PRL model ( Equation 3 ) , this local motor primitive , B , is centered at the planned movement direction , planned , implying that after a given trial , the maximum adaptation in the entire movement space occurs at the planned movement direction . ( 3 ) Alternatively for the MRL model ( Equation 4 ) , the local motor primitive is centered at the actual movement direction , actual , which implies that after a given trial , the maximum adaptation occurs along the actual movement direction . ( 4 ) In our data analysis a few grossly irregular trials were excluded . This included movements that were extremely fast ( peak velocity >0 . 55 m/s ) or extremely slow ( peak velocity <0 . 2 m/s ) , as well as trials with extremely fast ( <75 ms ) or extremely slow ( >2 . 5 sec ) reaction times . This insured that subjects did not initiate movements too quickly , without correctly identifying the location of the target , or too late , indicating that they might have not been attending to the task . For Experiment 1 , application of these two criteria resulted in the inclusion of 98 . 2% of the trials in the 270° group , 96 . 8% of the trials in the first 60° group ( 6∶8 CW to CCW FF trial ratio ) , and 94 . 9% of the trials in the second 60°s group ( 5∶9 CW to CCW FF trial ratio ) . For Experiment 2 , 94 . 7% of the trials in the STT group , 95 . 2% of the trials in the STT group , and 93 . 4% of the trials in the RST group were included . In order to compare the predicted and experimentally observed generalization patterns in Experiment 1 , we computed the correlation coefficient between them as well as the p value and F-statistic associated with the slope of the corresponding linear regression . We assessed the significance of the difference in the adaptation between the peaks of the generalization patterns using one-sided paired t-tests . In Experiment 2 , differences between learning rates for the three training paradigms ( STT , LST , and RST ) were assessed with one-way ANOVAs both early ( first 3 EC trials ) and late ( last 3 EC trials ) in training . When significant differences arose , post-hoc comparisons were performed using one-sided t-tests .
|
Einstein once said: “Insanity is doing the same thing over and over again and expecting different results” . However , task repetition is generally the default procedure for training a motor skill . This can work because motor learning ensures that repetition of the same motor task will lead to actions that are different , as errors are reduced and motor skill improves . However , here we show that task repetition , although not “insane” , is inefficient . The machine learning algorithms used to control motion in robotics adapt the movement that was actually made rather than the planned movement in order to assure stable learning . In contrast , it had been widely assumed that neural motor systems adapt based on the planned rather than the actual movement . If this were the case , task repetition would be an efficient training procedure . Here we studied the mechanisms for motor adaptation in humans and found that , like in robotic learning , the adaptation that we experience is associated with the actual movement . This finding led to the design of an improved training procedure that avoids task repetition . Instead , this procedure continually adjusts the movement goal in order to drive participants to experience the correct movement , even if initially by accident , leading to an over 50% improvement in the motor adaptation rate .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"motor",
"systems",
"medicine",
"computational",
"neuroscience",
"biology",
"neuroscience",
"learning",
"and",
"memory",
"physiotherapy",
"and",
"rehabilitation"
] |
2011
|
The Binding of Learning to Action in Motor Adaptation
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Comprehensive , longitudinal field studies that monitor both disease and vector populations for dengue viruses are urgently needed as a pre-requisite for developing locally adaptable prevention programs or to appropriately test and license new vaccines . We report the results from such a study spanning 5 years in the Amazonian city of Iquitos , Peru where DENV infection was monitored serologically among ∼2 , 400 members of a neighborhood-based cohort and through school-based absenteeism surveillance for active febrile illness among a subset of this cohort . At baseline , 80% of the study population had DENV antibodies , seroprevalence increased with age , and significant geographic variation was observed , with neighborhood-specific age-adjusted rates ranging from 67 . 1 to 89 . 9% . During the first 15 months , when DENV-1 and DENV-2 were co-circulating , population-based incidence rates ranged from 2–3 infections/100 person-years ( p-years ) . The introduction of DENV-3 during the last half of 2001 was characterized by 3 distinct periods: amplification over at least 5–6 months , replacement of previously circulating serotypes , and epidemic transmission when incidence peaked at 89 infections/100 p-years . Neighborhood-specific baseline seroprevalence rates were not predictive of geographic incidence patterns prior to the DENV-3 introduction , but were closely mirrored during the invasion of this serotype . Transmission varied geographically , with peak incidence occurring at different times among the 8 geographic zones in ∼16 km2 of the city . The lag from novel serotype introduction to epidemic transmission and knowledge of spatially explicit areas of elevated risk should be considered for more effective application of limited resources for dengue prevention .
Dengue viruses ( DENV ) are major re-emerging pathogens that have increased geographically from only 9 countries 60 years ago to more than 100 today . An estimated 2 . 5–3 . 0 billion people worldwide are at risk , with 50–100 million cases of dengue fever ( DF ) and 250 , 000–500 , 000 of the more severe dengue hemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) each year . Incidence of severe disease ( DHF/DSS ) has been increasing consistently since the 1950's [1] , [2] , [3] , [4] , [5] . DENV exists as four closely-related , antigenically distinct single-stranded RNA viruses ( DENV-1 , DENV-2 , DENV-3 and DENV-4 ) in the genus Flavivirus , family Flaviridae . Immunity induced by infection with one serotype is protective and affords transient cross-protection against the other serotypes; hence sequential infections with different serotypes are possible . The etiology of serious illness is not completely understood , but secondary infection and/or variation in virus virulence have often been implicated [1] , [4] , [5] , [6] , [7] , [8] . Without a vaccine , dengue prevention relies on virological surveillance and vector control . Mounting evidence indicates that accounting for variation in the ecology and epidemiology of dengue will be important for development of more effective , locally adapted control programs [9] , [10] . Such programs , along with future phase III vaccine trials , will require an improved understanding of region-specific transmission dynamics . This information is best obtained from comprehensive , longitudinal field studies designed to provide a detailed understanding of fundamental processes in virus transmission , epidemiology and disease control [9] . In Latin America , successful control efforts that ended in the 1960's were followed by reinvasion of the mosquito vector Aedes aegypti and the emergence of dengue as a leading public health problem throughout the continent . Wide-spread urbanization contributed to spread of the vector , creating conditions that enhanced DENV transmission . This is exemplified by numerous recent and dramatic regional outbreaks . Here we report results from longitudinal studies in Iquitos , Peru , an Amazonian city with a history of dengue virus transmission that has been well documented by the US Naval Medical Research Center Detachment ( NMRCD ) since the early 1990's , when DENV was presumably reintroduced into Peru [11] . Iquitos experienced epidemics of febrile disease caused by sequential invasions of DENV: DENV-1 in 1990—1991 , an American strain of DENV-2 in 1995 [7] , [12] , DENV-3 in 2001 [13] , an Asian strain of DENV-2 in 2002 , and DENV-4 in 2008 [14] . The long-term goal of our research in Iquitos was to acquire a detailed understanding of dynamics in DENV transmission and their relationship to entomological parameters that will inform vector control programs and improve disease prevention . Our approach was to monitor virus transmission and Ae . aegypti population densities simultaneously in the homes and neighborhoods of a longitudinal cohort representing 20% of the most populated areas of the city . Ae . aegypti abundance and production patterns were previously described [15] , [16] , [17] , [18] . Herein , we examine spatial and temporal patterns of transmission dynamics for 3 DENV serotypes before and during the invasion of a locally novel virus . Our results are based on a prospective cohort study conducted between 1999–2005 , during which time there was active transmission of DENV-1 and DENV-2 and invasion of DENV-3 , which caused a significant epidemic of febrile disease .
The study protocol was approved by the University of California , Davis ( Protocol 2220210788-4 ( 994054 ) , Instituto Nacional de Salud , and Naval Medical Research Center ( Protocol #NMRCD . 2001 . 0008 [DoD 31574] ) Institutional Review Boards in compliance with all Federal regulations governing the protection of human subjects . Our study was conducted in Iquitos , an urban community located in the Amazon Basin of northeastern Peru ( 73 . 2°W , 3 . 7°S , 120 m above sea level ) in the Department of Loreto . The Amazon , Nanay , and Itaya Rivers surround it on 3 sides . The population in the city has grown since its last published census of 350 , 000 people [19] . The more common industries are small business , fishing , oil , lumber , tourism and agriculture . The climate is tropical , with an average daily temperature of 25 . 8°C ( average minimum 21 . 9°C and maximum 32 . 4°C ) and an average annual precipitation of 3 . 4 meters ( range 2 . 7—4 . 4 meters ) during our study . Precipitation occurs throughout the year , on about half the days ( 51 . 6% ) . Iquitos is described in detail in earlier reports [7] , [12] , [15] , [16] , [17] , [18] . Iquitos is comprised of 4 districts: San Juan , Maynas , and Punchana running from South to North and Belen on the East ( See Figure 1 in [17] ) . We restricted our study to an area of ≈16 km2 in the districts of Maynas , Punchana and small portions of Belen and San Juan , which we divided into 8 geographic zones ( described in detail in [17] ) based on known neighborhoods served by distinct health centers . In brief , the 3 most northern zones – Punchana ( PU ) , Maynas ( MY ) , and San Antonio ( SA ) – and the 5 remaining southern zones – Putumayo ( PT ) , Iquitos ( IQ ) , Morona Cocha ( MC ) , Bagazan ( BG ) and Tupac Amaru ( TA ) – belong to the districts of Punchana and Maynas , respectively; each has its own local government and services . The zones of Tupac Amaru ( TA ) , Bagazan ( BG ) , and Putumayo ( PT ) each have areas where houses are flooded seasonally . We monitored a cohort of ∼2 , 400 study participants longitudinally from January 1999 through August 2003 at ∼6-month intervals for serological evidence of DENV infection . In addition , serological monitoring continued until February 2005 in a subset of school age cohort participants who were also monitored for active dengue disease based on attendance at school ( see sub-section febrile surveillance below ) . To obtain a geographically and temporally stratified sample of study participants , each of the 8 study zones was sub-divided into approximately 5 equal areas . Three blocks from each area were randomly selected for a total of 15 sample blocks in each zone . Recruitment focused on school-aged children ( between 5 and 20 years ) . To obtain a more stratified cross-section of the population , participation was also offered to other members of the household after a school-aged participant was enrolled . If the residents agreed to participate , the consent and assent forms were signed before samples were obtained . Written informed consent was obtained from participants older than 17 years , and from parents of participants younger than 18 . In addition , assent was obtained from participants 8–17 years of age . If participants were unable to read and sign the consent form , oral consent was obtained and documented in the presence of a witness . Fifty school-aged participants and 10 family members older than 20 were recruited from each zone each month , repeating the process each month until a base cohort of 2 , 400 individuals was obtained ( Table S1; see Figure 1 in [20] ) . After the base cohort was established , follow-up visits for individual participants were carried out at approximately 6-month intervals . Participants were considered lost to follow up after a full year had passed since their previous blood draw , despite repeated attempts to locate the participant , or if there was a verifiable reason for dropping them from the study ( direct request from the participant , movement from the study area , or death ) . New participants were enrolled to replace those lost to follow up , with preference toward people from the same geographic zones as those lost , in order to maintain an active cohort of ∼2 , 400 individuals . In some cases participants would re-enroll in the study after returning from an extended trip or regaining interest in the study . In June 2000 , a subset of 1 , 100 cohort members ( ages 5—20 ) who were attending morning sessions at one of the 29 participating public schools in the city were recruited for surveillance of febrile disease [20] . In addition to participating in the longitudinal components of the study ( 6-month blood samples and entomological surveys ) this sub-population was monitored for symptomatic DENV cases . Phlebotomists visited schools daily , checked attendance , and visited the homes of participating absentees . If the absence was caused by febrile illness , acute and convalescent blood samples were obtained and daily medical exams carried out for each participant . During school vacation , children were visited weekly at their homes . New participants were enrolled to replace those who had dropped out of school , graduated , or changed schools . After entomological surveillance ended in August 2003 , the school surveillance study continued for participants who remained in monitored schools . At the beginning of the 2004 school year , additional participants were recruited from previously enrolled families ( siblings or other relatives ) or from households participating in a community-based cohort study [20] . Monitoring of absences ended in December 2004 , and final blood samples were obtained from children up to March 2005 . Clinical aspects of this study will be reported in subsequent publications . All blood samples from the longitudinal arm of the study were assayed by plaque reduction neutralization test ( PRNT ) using 70% reduction for the cut-offs ( PRNT70 ) . PRNT70 were carried out at final serum dilutions of 1∶60 and 1∶120 ( after addition of virus ) for DENV-1 and DENV-2 and 1∶30 and 1∶60 for DENV-3 . For DENV-2 linear regression models were fit to estimate the percent reduction at cut-off dilution of 1∶80 , whereas a cutoff dilution of 1∶60 was used for DENV-1 and DENV-3 . Because DENV-3 was not detected in Iquitos until December 2001 , routine screening of samples for DENV-3 neutralizing antibody ( NtAb ) was not initiated for sera collected until after January 2002 . For participants who may have seroconverted to DENV-3 before 2002 , prior samples were tested for DENV-3 sequentially until a sample negative for DENV-3 was observed . DENV-4 circulation was not detected in Iquitos between 1993 through the end of this cohort study [14] . We did not , therefore , test for DENV-4 antibody . PRNTs were performed as described by Sangkawibha [21] and Graham [22] , with a modified protocol for semi-micro methods with baby hamster kidney ( BHK-21 ) cells ( clone 15 ) in 12 or 24 well plates [23] . Modifications are described in detail by Kochel et al . [8] and Comach et al . [24] . Briefly , 0 . 2 mL of diluted test sera were mixed with 0 . 2 mL diluted media ( Earle's minimal essential medium [E-MEM] , 2% fetal bovine serum [FBS] , and antibiotic/antimycotic ) containing 40–80 PFU of assay virus and then incubated at 4°C for 15 hours . Unless otherwise stated , DENV strains utilized in the PRNT were two viruses isolated in Thailand in 1974 from DHF cases [25] , DENV-1 16007 and DENV-2 16681 , as well as a 2001 Peruvian isolate ( from a DF case ) of DENV-3 , IQD1728 . Prior to use in the assay , viruses were amplified in Ae . albopictus C6/36 cell culture and aliquots frozen at −70°C to ensure consistency in testing throughout the study . Sera were heat-inactivated at 56°C for 30 minutes before the PRNT . In triplicate , 0 . 1 mL of virus-serum mixture ( dengue-1: 16007; dengue-2: 16681; dengue-3: IQT1728 ) was added to 0 . 5 mL media containing 1 . 5×105 BHK21 cells and then added to a well of a 24 well tissue culture plate and incubated at 37°C with 5% CO2 for 3 hrs . The cells were then overlaid with 0 . 5 mL of overlay media ( 0 . 6% carboxymethyl Cellulose , MEM w/o phenol Red , 10% FBS , 0 . 075% NaHCO3 and antibiotic/antimycotic ) and incubated at 37°C with 5% CO2 for 5 days . The media was removed , and the cells rinsed with H2O and stained with 0 . 5 mL/well stain solution ( 0 . 1% ( w/v ) Naphthol Blue Black , 1 . 36% ( w/v ) Sodium Acetate , and 6% ( v/v ) Glacial Acetic Acid ) for 30 min . Stain was removed and plaques were counted . Results were expressed as the serum dilution , determined by linear regression analysis , that reduced the number of plaques by 70% compared to that of normal human serum at the same dilution . Positive and negative human control sera were included with every batch of sera tested . Cutoff values , established based on the ability of the assay to maximize serotype sensitivity and specificity , were 1∶60 for DENV-1 and DENV-3 and 1∶80 for DENV-2 ( Minnick SL , unpublished data ) . If a study participant was observed to have a febrile illness during school-based surveillance activities , acute and convalescent blood samples were collected . Acute-phase serum samples were tested for DENV infection either by virus isolation in cell culture or by detection of viral RNA by reverse transcription polymerase chain reaction ( RT-PCR ) . Both acute- and convalescent-phase samples were screened for anti-DENV IgM antibody by IgM-capture enzyme-linked immunosorbant assay ( ELISA ) . In addition , a subset of acute and convalescent samples ( those collected prior to 2002 and a subset of those collected during 2004 ) was screened for anti-DENV IgG antibodies by ELISA . Febrile episodes were classified as DENV infections based on the isolation of virus , RT-PCR , IgM serology ( elevated IgM antibody titers [≥1∶100] in the acute sample , convalescent sample , or both ) , or IgG antibody serology ( four-fold rise in titers between acute and convalescent samples ) . Infections identified by viral detection , IgM seroconversion or an elevated IgM titer of ≥1∶400 were counted as seroconversions in all incidence calculations , even if pre- and/or post-sample PRNT data was not available or discordant , whereas individuals with an elevated IgM titer of 1∶100 or IgG serology were only included for incidence calculations if confirmatory PRNT data was available . Proportions were compared using a chi-square test using the FREQ procedure in SAS ( SAS Version 8 , 1999 , SAS Institute Inc . , Cary , NC . ) with statistical significance assessed at an alpha level of 0 . 05 . We calculated seroprevalence rates from serostatus in the first blood sample of all participants enrolled through September 1999 when an active cohort of 2 , 400 participants was achieved as interpreted through examination of complete serological profiles ( see Interpretation of PRNT results above ) .
Although the time interval between blood samples was relatively long ( ∼6 mo ) , we observed cross-reactive antibody in the first and sometimes second post-conversion blood samples , which became specific in subsequent samples ( e . g . [N-N-D12-D1-D1] , see Table 1 for sequence abbreviations ) . The rate of apparent cross-reaction was similar for primary ( 18 . 8% , 29/154 ) and secondary infections ( 23 . 9% , 33/138 ) . Correction factors for all observed infection sequences are shown in Table 1 . Of the 22 participants whose baseline PRNT sample showed evidence of monotypic infection with DENV-3 followed by an apparent infection to either DENV-1 or DENV-2 , none had supportive PRNT results in subsequent samples ( correction factor = 0 in Table 1 ) . Overall , profiles indicating seroconversion were reliable for new DENV-3 infections ( correction factor>0 . 90 ) , but less so for new DENV-1 and DENV-2 infections ( correction factor<0 . 37 ) . Correction factors decreased for participants with a polytypic pre-conversion status , but these seroconversions held up >50% of the time for new DENV-3 infections . In a number of cases , pre-conversion blood samples presented a % reduction that was between 1 and 10% below the cutoff , followed by a 20% increase in the subsequent sample . In other instances , serology suggested infection with DENV-3 , only to fall off in subsequent samples . According to our criteria , none of these cases would be considered unequivocal seroconversions; although they could be true seroconversions our assay was not sensitive enough to detect them . Estimates of incidence with and without these possible seroconversions did not have any significant effect on the outcome ( see Tables S3 , S4 , S5 ) . Overall , we were able to interpret baseline serostatus for 99 . 9% of study participants . For all participants enrolled before October 1999 ( n = 2 , 527 ) only 19 . 7% ( n = 498 ) did not have serological evidence of prior dengue infection . Approximately half the participants had DENV NtAbs against both DENV-1 and DENV-2 ( 1 , 292 , 51 . 1% ) at the baseline sampling period of the study; 366 ( 14 . 5% ) and 368 ( 14 . 6% ) had monotypic NtAbs to DENV-1 and DENV-2 , respectively . DENV NtAbs were observed at the same rate in participants providing a single ( 286 of 350 , 81 . 7% ) or multiple ( 1 , 740 of 2 , 174 , 80 . 0% ) blood draws ( P = 0 . 4642 ) . Prevalence of DENV NtAbs was higher among females ( 1 , 170 of 1 , 424 , 82 . 2% ) than males ( 856 of 1 , 100 , 77 . 8%; P = 0 . 0065 ) and increased with age . Of adults ( ≥18 years ) prevalence was 91 . 2% ( 423 of 464 ) , while among children ( <18 ) it was 77 . 8% ( 1 , 603 of 2 , 060; P<0 . 0001 ) . Over half of the youngest group ( 5 year-olds ) presented evidence of DENV NtAbs ( 56 . 1% ) and this proportion increased steadily with age with more than 87% of the participants ≥14 year-olds showing a history of infection ( Figure 3 , P<0 . 0001 ) . DENV seroprevalence rates varied geographically ( Figure 4 ) . Age-adjusted rates ranged from 67 . 1 to 89 . 9% ( P<0 . 0001 ) with lower rates in zones ( BG , PT , TA ) where some households were located in river-front areas that are seasonally flooded . Of the 3 , 903 participants who provided at least 2 blood samples , 2 , 542 ( 65 . 1% ) had no change in their serostatus compared to baseline over the observation period ( Table 3 ) . A total of 1 , 414 DENV seroconversions occurred during the study period among 1 , 347 participants ( 34 . 5% of the cohort ) ; 1 , 281 participants showed evidence of infection with a single serotype , 65 to 2 serotypes , and 1 to 3 serotypes . Of these 1 , 414 seroconversions , 585 ( 41 . 4% ) were in the final blood sample , which cannot be conclusively determined to be true seroconversions , and 62 ( 1 . 6% ) were ambiguous ( see PRNT , above ) ( Table 4 and S2 ) . Complete result profiles from 14 participants were excluded because they could not be interpreted . The majority of infections were secondary ( 75 . 7% , Table 4 ) . Prior to invasion by DENV-3 the ratio of the incidence of primary to secondary infections was less than two –fold , then increased to more than five-fold through the peak of epidemic transmission ( Table 5 ) . By 2004 , the rate of dengue infection in primary and secondary infections was nearly equal . DENV-3 was responsible for 58 . 4% of infections and most likely responsible for an additional 18 . 8% ( cross reactive responses that included DENV-3 antibody ) of all the DENV infections observed ( Table 4 ) . The exact infecting serotype could not be identified in 24 . 9% of the primary infections ( see Table 4; N-D12 , N-D13 , N-D23 , N-D123 ) and 17 . 9% of the secondary infections ( see Table 4; D3-D123 , D2-D123 , D1-D123 ) . Among the febrile children captured through active school absence monitoring , 73 confirmed DENV infections were identified; 20 by virus isolation , 14 by RT-PCR , and 39 by IgM serology ( Table 6 ) . Another 42 participants had symptoms consistent with DF and PRNT evidence for a seroconversion during the monitoring interval of their febrile episode . Of these 11 with an IgM antibody titer of 1∶100 and 3 with a 4-fold rise in anti-DENV IgG antibody titer between acute and convalescent samples were included in the calculation of symptomatic dengue infections . In total , we identified 11 DENV-1 cases ( 7 primary , 4 monotypic to polytypic ) , 2 DENV-2 cases ( 1 primary , 1 monotypic to polytypic ) , and 74 DENV-3 cases ( 22 primary , 20 monotypic to polytypic , 32 DENV-1/-2 to DENV-1/-2/-3 ) . For 5 primary and 23 secondary infections the infecting serotype could not be identified . During the first 15 months of the study , DENV-1 and DENV-2 co-circulated at low infection rates , with approximately 2 and 6 infections per 100 people per year in the entire population and susceptible population , respectively ( Table 7 ) . DENV-3 appears to have been introduced into Iquitos sometime in 2001 . The first DENV-3 isolate was recovered from a DF case on December 7 , 2001 [13] ( TJK , unpublished results ) . We detected only 2 clinical DENV-3 cases in our school surveillance study prior to January 2002 . In addition , 2 febrile participants detected in July 2001 had no detectable IgM response , but did show a 4-fold rise in IgG titer . Both had NtAb to DENV-3 in 2002 PRNT results . Examination of PRNT antibody results indicates possible DENV-3 transmission at least 5–6 months earlier than the first isolate . First evidence of DENV-3 transmission was in a sample taken from a 5 year-old female whose last PRNT sample taken on May 27 , 2001 indicated seroconversion to this serotype , but no subsequent samples were taken to confirm this result . The next earliest indications of transmission were in samples taken July 24 from a 12 year-old male and July 27 from a 20 year-old female . The previous blood samples taken at the end of 2000 from both of these participants were negative for DENV antibody , thus indicating that infection took place sometime between December 2000 and late July 2001 . During the first trimester of 2002 we observed increased DENV-1 and DENV-3 transmission , with a steady increase in the incidence of DENV-3 infection from12 . 9 to 29 . 6 seroconversions per 100 p-years at risk between the first and last trimester of 2002 , corresponding to a major dengue outbreak in Iquitos . Transmission rates of DENV-3 decreased steadily through the end of August 2003 when our community-based study ended . A subset of our study cohort was followed in school surveillance from September 2003 through February 2005 . In this later group seroconversion rates were consistent with the May–August 2003 trimester through May 2004 but increased dramatically in the last half of 2004 , which corresponded to another dengue outbreak . When seroconversion rates were calculated for the subset of the longitudinal cohort enrolled in the school surveillance program , they were similar to those for the entire cohort , except for May to December 2002 when seroconversion rates appeared about 10 SCs per100 p-years less in the school cohort when adjusted for the susceptibility patterns of the population ( Table 8 , Figure 5 , pink line compared to green ) . Population-based seroconversion rates were nearly identical . Incidence of symptomatic DF cases in the school cohort was significantly lower than the seroconversion rate in the sample population , ranging from 0 . 6 to 13 cases per 100 p-years ( Figure 5 ) . The inapparent to apparent dengue case ratio varied over time ( Figure 5 ) . Prior to June 2001 , when only DENV-1 and -2 were circulating , the ratio was low ( approximately 1∶1 ) , but increased rather dramatically in the last half of 2001 , when DENV-3 was most likely introduced . In 2002 , during the height of transmission , ratios ranged from 2 . 6–5 . 3∶1 . Rates of DENV seroconversion varied significantly among geographical zones of the city ( Figure 6 ) . When calculated for the entire study period seroconversions ( SCs ) were highest in the two northeastern zones ( MY and PU ) and one central zone ( MC ) ( mean 28 . 6–31 . 5 SCs per 100 p-years at risk ) , followed by another central zone of IQ ( 24 . 5 SCs per 100 p-years at risk ) . Based on baseline seroprevalence rates , SA had less activity ( 20 . 5 SCs per 100 p-years at risk ) and TA had more DENV activity than expected ( 20 . 4 SC per100 p-years at risk ) . DENV infection rates were lowest in BG and PT . Temporal patterns of transmission varied by zone . For example , for the period from January 1999 through March 2000 SC rates were similar between DENV-1 and -2 , with more activity in TA ( 13 . 2 SCs per100 p-years ) than in the other zones ( 1 . 5–5 . 8 SCs per100 p-years ) . The first observed seroconversions to DENV-3 occurred in IQ , MY , and TA ( Figure 4 ) , suggesting virus spread rapidly throughout the city . Indeed , early infections of DENV-3 were distributed across all 8 geographic zones , although most cases occurred in MY and IQ during the first trimester of 2002 . Transmission lagged in MC and PU , with SC rates peaking during the September–December 2002 trimester . BG lagged even further , with the highest transmission rates observed during the January–April 2003 time period .
We estimate that DENV-3 was introduced into Iquitos between May–July 2001 , and then transmission was amplified during the last trimester of that year when unusually high Ae . aegypti population densities were observed [17] . We identified 27 participants who had evidence of a DENV infection between May and December 2001 and 2 febrile participants were detected in July 2001 . DENV-1 was also circulating at this time and could have been the cause of some of these seroconversions , all of which appeared polytypic . None of these individuals showed evidence of DENV-3 NtAbs prior to this time and subsequent blood samples indicated consistent positive DENV-3 results . Together these cases point to low level serotype 3 activity occurring at least 5–6 month and up to a year prior to our first isolation of DENV-3 during December 2001 , despite rigorous virological surveillance . Gubler [38] described a latent period of this duration from introduction to detection of overt disease , but to the best of our knowledge this is the first empirical evidence for this phenomenon . Virus isolation data from clinics in Iquitos indicated that DENV transmission started to increase in late 2001 and early 2002 , but was predominantly DENV-1 . By March 2002 , DENV-3 virus isolates predominated . In addition there were 2 isolates of the Asian serotype of DENV-2 in early 2002 ( Kochel et al . unpublished ) and presentation of the first clear clinical cases of DF were observed in our school cohort . The initial increase in DENV-1 transmission supports the idea that entomological and environmental conditions had become favorable for virus transmission . DENV-3 had been present in northwestern Peru since 2000 and Guayaquil , Ecuador since 1999 [13] . There was also virological evidence of DENV-3 circulation in 2001 in Pucallpa and Yarinacocha , both cities located on a major route of river travel and commerce to the south of Iquitos in the Amazon Basin and connected by road to Lima , the capital of Peru [39] . We speculate that the relatively rapid replacement of DENV-1 with DENV-3 and the failure to see continued transmission of Asian DENV-2 can be explained by short term cross protection to heterologous serotypes by recent DENV infections . Nearly the entire Iquitos population was susceptible to DENV-3 , whereas seroprevalence rates indicated high rates of previous infection with DENV-1 and DENV-2 , and upon introduction of DENV-3 the low percentage of people susceptible to DENV-1 or DENV-2 would be even further reduced by the cross-protective response following DENV-3 infection . A similar pattern was observed recently with the 2008 introduction of DENV-4 into Iquitos [14] . Infection rates steadily increased throughout 2002 to a peak level of 89 SC per 100 p-years , and rates of clinically apparent disease increased from 13 cases/100 p-years to 19 cases/100 p-years at risk . Transmission decreased dramatically after a city-wide vector intervention based around household ultra low volume adulticide applications and larvicide treatment of Ae . aegypti-producing containers ( Morrison et al unpublished ) . In 2004 , another outbreak of DF and the first documented cases ( 7 ) of DHF were observed . Although no DHF cases were observed in our school surveillance study , reports from local health officials indicated that severe cases of severe illness were observed only during periods of intense transmission at the end of 2002 and again in 2004 ( Sihuincha , personal communication ) indicating that , in Iquitos , slowing the force of infection with an effective vector control program had beneficial public health consequences . The spatial evolution of the epidemic illustrated that dengue viruses spread rapidly and moved through different areas of the city at different times . Clinically apparent illness providing the earliest evidence of transmission indicated that infected people were scattered across the city , implicating human movement in the dispersal of virus because flying infected Ae . aegypti would not move those distances in that short a time period [40] . Within geographic zones , patterns appeared to reflect the suitability of local conditions for transmission of pathogen . For instance , the timing of peak transmission varied from as early as the January–April 2002 trimester to as late as the same trimester in 2003 . In addition , the neighborhood whose peak transmission occurred last in 2003 also had the lowest seroprevalence rates at baseline , which we attribute to consistently lower Ae . aegypti indices [17] . Spatial patterns in seroincidence did not follow seroprevalence patterns prior to 2001 , but did afterwards . This indicates these areas were of high risk , but herd immunity probably prevented higher transmission rates . An important observation is the fluctuation in ratio of apparent to inapparent case rates observed over the course of the study . Prior to June 2001 the apparent to inapparent ratio was comparable to that reported by Endy et al . [36] in Thailand , before increasing to 5∶1 during the initiation of the DENV-3 outbreak ( Figure 5 ) . In 2003 the ratio increased steadily to 40∶1 for the period between September 2003–May 2004 and 24∶1 during the last half of 2004 when significant clinical disease was observed . This higher ratio of asymptomatic or subclinical cases after May 2003 may in part be due to a decrease in force of infection owing to vector control interventions . After August 2003 , additional individuals were recruited into the school surveillance study and the community-based part of the study was discontinued . It is possible that our methodological shift affected the efficiency of our surveillance , but the dramatic shift in incidence of apparent dengue cases is difficult to explain except through positive impact of herd immunity . Independent of the time interval monitored , infection rates among individuals who had been previously infected with DENV ( secondary infections ) always exceeded rates of primary infections . The higher incidence of secondary versus primary infections was least dramatic prior to the introduction of DENV-3 and during June 2004–February 2005 after that serotype had been circulating for 2 years . It was during these periods that our observations were consistent with previously published studies [21] , [22] , [33] , [35] . During epidemic transmission in 2002 , however , the secondary infection rate exceeded that of primary by 5–8 fold , decreasing in 2003 to 3–5-fold until June 2004 when the rates of primary and secondary infections were nearly equal . This indicates that many people in the population have consistent exposure over time and that seroprevalence patterns can be informative for establishing surveillance zones of consistently low versus high entomological risk . For example , in the geographic zone MY , seroprevalence patterns indicated high rates of historical transmission which also corresponded to some of the highest entomological indices in Iquitos [17] . Although pre-invasion seroincidence rates were relatively low in this zone , post-invasion incidence patterns indicate that transmission increased rapidly here prior to other areas of the city . It is worth noting that the higher incidence rates among participants who had been previously infected by a heterologous serotype , reliance on a serologically naïve population for longitudinal studies would underestimate overall DENV activity and would , thus , be inappropriate for early detection ( i . e . , sentinels ) . A serious impediment to large population-based longitudinal studies on dengue has been logistical and technological barriers associated with serological testing [41] . In our hands , the PRNT , although expensive and labor intensive , provided a reliable way to measure seroconversion rates in a large population . For long term studies the PRNT permits monitoring of cohort participants at longer time intervals ( 6 months to 1 year ) than alternative ELISA or HAI-based assays that require shorter intervals ( <3 months ) to identify seroconversions . The PRNT provides serotype-specific information , which the other assays do not , that is critical for assessing the risk of populations for epidemic transmission of a novel serotype . PRNT results become more informative as the number of blood samples per participant increases because multiple samples ( preferably >3 ) provide a “serological profile” that can corroborate observations made from a single monitoring interval . For example , when at least one sample was available after the interval where a seroconversion occurred , the infecting serotype could be determined in 75 . 1% ( 127/169 ) of primary and 77 . 4% ( 345/446 ) of secondary infections . For individuals who had no change , 80% of the intervals monitored had consistent PRNT results; 11 . 7% and 8 . 3% showed transitory “false” positive and “false” negative results , respectively . Moreover , data from the entire study population can be used to derive correction factors to improve estimates of seroconversions in persons who have no samples taken after the seroconversion interval . We recommend that cutoff values be established empirically using serum samples with known infection histories . Responses may vary based on the local history of DENV serotype circulation and the genetic background of the resident populations [8] , [24] . Four serial dilutions rather than the two used in this study improves interpretation of the portion of the human population with ambiguous responses [24] . A challenge to the application of PRNT results in a population-based study is that there remain important knowledge gaps in expected NtAb responses to virus challenges with homologous serotypes and related Flavivirus species , cross-reaction patterns among the four DENV serotypes , and how long NtAb titers remain above cutoff values for what proportion of the population . Our results show a strong similarity between baseline seroprevalence patterns and seroincidence patterns the year following the introduction of a novel dengue serotype . Herd immunity clearly has a profound impact on seroincidence patterns under periods of endemic transmission . In locations like Iquitos , where there was not hyperendemic transmission of all 4 serotypes , surveillance and control programs must consider separate strategies for ( 1 ) managing transmission of existing serotypes and ( 2 ) preparing for and dealing with either the introduction of novel serotype/genotype or analogous situations where immunity in the human population is low . The major challenge remains early detection of and effective response to novel virus activity . Our experience in Iquitos indicates that establishment of a new serotype requires a short period where environmental conditions are favorable for amplification of virus ( high adult Ae . aegypti populations and ambient temperatures ) and that these characteristics can be incorporated into early warning systems . The lag time from introduction to epidemic transmission that we estimated for DENV-3 in Iquitos ( 6–12 months ) and knowledge of spatially explicit areas of elevated risk ( Maynas ) should be considered for targeting more effective application of limited resources for dengue prevention .
|
To develop prevention ( including vaccines ) and control programs for dengue fever , a significant mosquito-borne disease in the tropics , there is an urgent need for comprehensive long term field epidemiological studies . We report results from a study that monitored ∼2 , 400 school children and some adult family members for dengue infection at 6 month intervals from 1999 to 2005 , in the Amazonian city of Iquitos , Peru . At enrollment , ∼80% of the participants had a previous infection with DENV serotypes 1 and 2 or both . During the first 15 months , about 3 new infections for every 100 participants were observed among the study participants . In 2001 , DENV-3 , a serotype not previously observed in the region , invaded Iquitos in a process characterized by 3 distinct periods: amplification over at least a 5–6 month period , replacement of previously circulating serotypes , and epidemic transmission when incidence peaked . Incidence patterns of new infections were geographically distinct from baseline prevalence rates prior to arrival of DENV-3 , but closely mirrored them during the invasion . DENV transmission varied geographically corresponding to elevated mosquito densities . The invasion of a novel serotype is often characterized by 5–6 months of silent transmission before traditional surveillance programs detect the virus . This article sets the stage for subsequent publications on dengue epidemiology .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
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"infectious",
"diseases",
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2010
|
Epidemiology of Dengue Virus in Iquitos, Peru 1999 to 2005: Interepidemic and Epidemic Patterns of Transmission
|
Human α-Synuclein ( αSyn ) is a natively unfolded protein whose aggregation into amyloid fibrils is involved in the pathology of Parkinson disease . A full comprehension of the structure and dynamics of early intermediates leading to the aggregated states is an unsolved problem of essential importance to researchers attempting to decipher the molecular mechanisms of αSyn aggregation and formation of fibrils . Traditional bulk techniques used so far to solve this problem point to a direct correlation between αSyn's unique conformational properties and its propensity to aggregate , but these techniques can only provide ensemble-averaged information for monomers and oligomers alike . They therefore cannot characterize the full complexity of the conformational equilibria that trigger the aggregation process . We applied atomic force microscopy–based single-molecule mechanical unfolding methodology to study the conformational equilibrium of human wild-type and mutant αSyn . The conformational heterogeneity of monomeric αSyn was characterized at the single-molecule level . Three main classes of conformations , including disordered and “β-like” structures , were directly observed and quantified without any interference from oligomeric soluble forms . The relative abundance of the “β-like” structures significantly increased in different conditions promoting the aggregation of αSyn: the presence of Cu2+ , the pathogenic A30P mutation , and high ionic strength . This methodology can explore the full conformational space of a protein at the single-molecule level , detecting even poorly populated conformers and measuring their distribution in a variety of biologically important conditions . To the best of our knowledge , we present for the first time evidence of a conformational equilibrium that controls the population of a specific class of monomeric αSyn conformers , positively correlated with conditions known to promote the formation of aggregates . A new tool is thus made available to test directly the influence of mutations and pharmacological strategies on the conformational equilibrium of monomeric αSyn .
A significant fraction ( possibly as much as 30% ) of proteins and segments of proteins in eukaryotic proteomes has been found to lack , at least partially , a well-defined three-dimensional structure . Proteins belonging to this class are usually called natively unfolded proteins ( NUPs ) [1] . NUPs have been found to play key roles in a wide range of biological processes like transcriptional and translational regulation , signal transduction , protein phosphorylation , and the folding of RNA and other proteins [2] . The conformational heterogeneity of NUPs allows them to adopt conformations that trigger pathogenic aggregation processes . In fact , NUPs are involved in the pathogenesis of some of the most widespread and socially relevant neurodegenerative diseases , such as Alzheimer and Parkinson [3–5] . Despite intensive research , the folding and the aggregation mechanisms of NUPs remain a major unsolved problem . Theoretical studies depict the apparent structural disorder of NUPs as the result of the coexistence of a complex ensemble of conformers ensuing from a rugged energy landscape [6] . Five clusters of conformations , each with its own characteristic tertiary structure , were identified by molecular dynamics studies on the Alzheimer β peptide [7] . Traditional bulk experiments and spectroscopies have recently been providing experimental evidence of the conformational diversity of these proteins [3 , 5] . Because of their inherent ensemble averaging , however , these methodologies cannot reveal the full complexity of the conformational equilibria of NUPs . Single-molecule methodologies can single out the structures adopted by individual molecules within a complex conformational equilibrium [8–14] . We decided to approach the problem of the characterization of the conformers of α-synuclein ( αSyn ) , which is a prototype of this class of proteins . αSyn is a 140–amino acid ( aa ) protein expressed primarily at the presynaptic terminals in the central nervous system , and it is thought to be physiologically involved in endoplasmic reticulum–Golgi vesicle trafficking [15] . αSyn is involved in the pathogenesis of several neurodegenerative diseases , called synucleopathies . Intracellular proteinaceous aggregates ( Lewy bodies and Lewy neurites ) of αSyn are hallmarks of Parkinson disease [16] and multiple system atrophy [17] . Three naturally occurring mutations in the αSyn protein sequence—A30P , A53T , and E46K—have been identified so far in human families affected by familial Parkinsonism [18–20] . These mutant proteins display an increased tendency to form nonfibrillar aggregates [21] and Lewy bodies–like fibrils in vitro [22] . The fibrils spontaneously formed by αSyn by a nucleation-dependent mechanism are rich in β structure [23 , 24] . The transition from the natively unfolded monomeric state to fibril is therefore a process of acquiring structure . This process is still under strong debate . Evidence is accumulating that the monomeric αSyn , under in vitro physiological conditions , populates an ensemble of conformations including extended conformers and structures that are more compact than expected for a completely unfolded chain [25–32] . The marked differences between the scenarios depicted in those studies are mostly determined by the different time scales of the ensemble averaging of the different methods used . Moreover , it is difficult for bulk methodologies to single out the monomeric state in the presence of soluble oligomers when they form quickly in solution [33] . On the contrary , the single-molecule force spectroscopy ( SMFS ) approach reported here describes , by design , the conformational equilibrium of the monomeric form . The different structures assumed by αSyn have been commonly investigated by adding to its buffer solution different chemicals , such as methanol or trifluoroethanol [34] , metal cations like Cu2+ and Al3+ [35 , 36] , or sodium dodecyl-sulfate ( SDS ) micelles [37–39] in order to shift the conformational equilibrium toward the form under investigation . A previous force spectroscopy experiment showed that a relevant 12-aa segment of αSyn is conformationally heterogeneous [40] . The approach we report can span the full conformational space of the whole protein and also identify poorly populated conformers of the monomeric αSyn in in vitro physiological conditions . Three distinct classes of structures in equilibrium were identified: random coil , a mechanically weak fold , and “β-like . ” Their populations were also monitored under conditions known to influence aggregation , such as the presence of Cu2+ , high buffer concentration and , most importantly , the pathogenic mutation A30P .
In the class of traces depicted in Figure 1B , the force curve exhibits ( from left to right ) a long initial region , without any significant deviation from the worm-like chain ( WLC ) behavior [45] , followed by a saw-tooth pattern with six consecutive unfolding events , in addition to the last one that corresponds to the final detachment of the molecule from the tip . The initial region corresponds to the extension of a chain that occurs at low force and without significant energy barriers limiting its extensibility . The six unfolding peaks are spaced by ∼28 nm . This spacing between the peaks corresponds to an 89-aa chain ( 0 . 36 nm per amino acid [46] ) , i . e . , to the increase in length of the protein after the unfolding of one I27 domain . These six unfolding peaks correspond to the characteristic fingerprint of the mechanical unfolding of the I27 modules [41] . We can therefore infer that , in this case , the AFM tip picked up the 3S3 construct molecules at the His-tag terminus , while the other end was tethered to the gold surface by the C-terminal cysteines . The location of the first unfolding peak of I27 , corresponding to the contour length of the construct molecules prior to any unfolding event , proves that the preceding featureless part of the trace can be unambiguously assigned to the αSyn chain . In fact , the measured contour length that fits this peak is 77 ± 4 nm . Subtracting the length of the six , still folded , I27 domains from this value ( 4 . 5 nm each [47] ) , a value of 48 ± 4 nm is obtained . This length corresponds to the chain of 140 aa of the αSyn . Therefore , this featureless initial part is the signature of αSyn conformers with the mechanical properties of a random coil . Their average persistence length was estimated by fitting the WLC model at 0 . 36 ± 0 . 05 nm . About 38% of the molecules showed this mechanical behavior in Tris/HCl buffer 10 mM ( Figure 2 ) . A significant proportion of force curves with seven regularly spaced unfolding peaks in the 200-pN range ( in addition to the last one corresponding to the final detachment ) ( Figure 1C ) was also recorded . The presence of a number of unfolding events greater than that of the I27 modules in the construct cannot be ascribed to a possible simultaneous pulling of more than one 3S3 molecule , because pulling two multidomain constructs at the same time would not likely lead to a uniform separation between the I27 unfolding events . Moreover , we never obtained a significant and uniform set of reproducible curves with eight or more unfolding peaks with 28-nm separation . Curves with seven unfolding events were well reproducible , and their statistics were unambiguously modulated by conditions able to trigger aggregation: e . g . , ionic strength , the presence of Cu2+ ions and , most importantly , pathogenic mutations ( see below ) . The appearance of seven unfolding events cannot come from a construct accidentally expressed with seven , instead of six , I27 domains because of the cloning strategy ( see Materials and Methods ) . The occurrence of a seventh peak due to the stretching of 3S3 dimers can be also ruled out . Dimers could form in solution via disulfide bonds between the terminal cysteines , but those bonds tend to dissociate into thiols in the presence of gold , because the gold–sulfur bond is more stable than the sulfur–sulfur bond [48] . Each monomer contains two terminal cysteines: one of them could be involved in the dimerization and the other could bind to the gold surface . Even in this unlikely event , the length of the tethered chains extending from the surface is the same as that of a nondimerized construct . Therefore , also in the case of a dimer tethered to the surface , more than six I27 unfolding peaks with the same separation cannot be recorded . We nevertheless tested the sample using dithiothreitol ( DTT ) to avoid any disulfide-bonded dimer formation . Under these conditions , the statistics of different populations was comparable to those in the standard buffer , and we still recorded a significant proportion ( ∼10% ) of seven-peaked curves . Because of the previous considerations , we therefore assign one of the seven peaks to the unfolding of the αSyn moiety . The length ( 95 aa ) of this αSyn β-like folded section accidentally coincides with that of the I27 domain . This coincidence hinders the possibility to discriminate the peak of the αSyn from the six of the I27 domains . Nevertheless , the assignment of these curves to the unfolding of the αSyn moiety is confirmed by the position of the first unfolding peak , i . e . , by the contour length of the construct molecules prior to any unfolding event . As shown in Figure 3 , the position values correspond to a chain composed of the six I27 folded modules , plus the αSyn moiety with its C-terminal segment of 50 aa fully unfolded , and the remaining 95 amino acids folded into a structure with the same contour length as a folded I27 domain ( solid line ) . The low propensity to fold of the 50 aa of the very acidic αSyn C-terminal tail has been extensively documented [38 , 39 , 49] . Segments of the remaining 95 amino acids are instead known to fold under different conditions into an α helix [38 , 39] or , in the amyloid , into a β sheet structure [31] . It must be noted that in about 40% of the molecules , the contour length of the same folded section is larger than that corresponding to 95 aa . The αSyn structural diversity therefore includes also β-like chain portions with different lengths . This interpretation is confirmed by comparing the variance of the folded section of seven-peaked curves with that of I27 modules ( Figure 3 ) . The 200-pN unfolding force of all the seven peaks indicates that the folded section of αSyn has the same mechanical properties of the I27 β-sandwich structure . At the moment , without any independent structural characterization , we consider and label this folded structure of the αSyn moiety just as β-like , in accordance with its mechanical behavior . Nevertheless , its mechanical behavior is in agreement with a β sheet content in the β-like class of conformers . It is unlikely that the α-helical content we observed by means of circular dichroism ( CD ) ( see below ) correlates with the “β-like” conformers . In fact , whereas β-structures , like those of titin modules , such as I27 [41 , 50] , or tenascin [51] , unfold at forces in the range of 100–300 pN ( at loading rates of the order of 10−5 N/s ) , the α helix domains , in the same conditions , are always observed to unfold at forces almost one order of magnitude smaller [52–55] . In conclusion , these curve profiles provide clear evidence that in 10 mM Tris/HCl buffer , about 7% of the molecules ( Figure 2 ) contain a segment of the αSyn chain of about 95 aa folded into a structure with the mechanical property of the I27 β-sandwich structure . This percentage of the β-like structures , as we will see below , can be related with conditions leading to pathogenic aggregation . The remaining force spectroscopy curves ( Figure 1D ) show single or multiple small peaks ( sometimes with a plateau- or dome-like appearance ) superimposed on the purely entropic WLC behavior of the trace preceding the six saw-tooth–like peaks . The geometry of our construct made it possible to exclude that those small peaks might correspond to the rupture of aspecific αSyn-gold interactions . In fact , if the unstructured αSyn was adsorbed on the surface , upon pulling the construct , we would have recorded the first event at a distance from the tip contact point corresponding to the length of the three I27 modules ( ∼13 . 5 nm ) . The mechanically weak events we observed instead took place at an average distance from the contact point of 60 ± 26 nm with no events below 20 nm . They are therefore not compatible with αSyn-gold interactions . We assign these signals to the rupture of mechanically weak interactions placed at short and long distances along the chain . The average forces of those single or multiple small peaks of the profiles are in the 64 ± 30–pN range ( well above the noise level ) , without a defined hierarchy; often stronger peaks precede weaker ones , hinting topologically “nested” interactions . From the difference between the contour length estimated at those small peaks and that at the first I27 unfolding peak , one can measure the size of the topological loop enclosed by the interactions whose rupture is monitored by the different peaks . The resulting broad distribution of these distances monitors the ample multiplicity of these interactions as discussed in Protocol S1 . More than 50% of the molecules showed short- and long-distance mechanically weak interactions in 10 mM Tris/HCl buffer ( Figure 2 ) . These interactions were also monitored by ensemble-averaged fluorescence spectroscopy . The fluorescence comes from the tryptophan residues of the I27 domains which are absent in αSyn . The fluorescence spectra reported in Figure 4A prove that interactions between the I27 handles and tracts of the αSyn moiety do take place , as shown by the broadening of the spectrum of 1S1 with respect to that of the 3T construct ( See Figure 1A and Materials and Methods section for constructs description ) and by the 5-nm shift of the λmax . The possibility of partial I27 unfolding leading to Trp exposure and broadening of the spectrum is ruled out by the CD data and by our force curves , which show that I27 domains are as tightly folded in the 3S3 construct as in an I27 homopolymer . A broadening due to subtle conformational effects on the I27 domain that expose the I27 Trp residue is possible , but even in this case , the fact that this broadening happens only when the αSyn moiety is inserted in the construct proves that direct interaction is taking place . CD spectra of 1S1 and 3T were recorded in which 1S1 shows some α-helical content in the αSyn moiety ( Figure 4B ) . . Subtraction of the contribution of the I27 linkers ( 2/3 of the CD of 3T recorded in the same 10 mM Tris/HCl buffer ) from the CD spectrum of 1S1 reveals a profile that is different from that of αSyn in the same buffer condition ( Figure 4C ) but similar to that of the same protein in the α helix structure induced by the addition of SDS [33] . This α-helical content might be induced by the interactions between the αSyn moiety and the I27 domains as discussed below and in Protocol S1 . It is well known that multivalent metal cations like Cu2+ can accelerate αSyn aggregation [35 , 36] . To validate our approach and to investigate how metal cations influence the conformer equilibrium of αSyn , we performed SMFS experiments on the 3S3 construct in 10 mM Tris/HCl buffer in the presence of 1 μM CuCl2 . The low concentration of copper was chosen to target the His 50 specific copper binding site of αSyn ( dissociation constant Kd = 0 . 1 μM ) [36] . The presence of 1-μM Cu2+ moderately , but significantly ( χ2 statistical significance p < 0 . 01 ) , alters the relative distribution of the αSyn conformers with respect to plain 10 mM Tris/HCl ( see Figure 2 ) . In particular , the relative population of the β-like conformers more than doubles ( from 7 . 2% to almost 18% ) , with a parallel decrease of the signals coming from mechanically weak interactions . An increase ( from 38% to 47% ) of random coil-like curves is also observed . The A30P mutation is a pathogenic , naturally occurring human αSyn variant , that correlates with familial Parkinsonism [19] . The mutant protein displays an increased rate of oligomerization [56] and impaired degradation by chaperone-mediated autophagy [57] . We tested the 3S3 αSyn-A30P construct to evaluate the capability of our methodology to probe different conformational propensities in mutants of the same protein . We found that the A30P mutation induces a striking shift in the conformational equilibrium of αSyn with β-like curves being around 37% of the sample and again , a corresponding decrease of signals coming from mechanically weak interactions ( Figure 2 ) . In contrast with wild-type αSyn incubated with Cu2+ , the A30P mutant does not induce an increase of random coil curves that are exactly in the same proportion observed in wild-type αSyn . Another condition known to speed up αSyn aggregation is high ionic strength [26 , 28] . SMFS experiments on the 3S3 wild type construct were performed also in 500 mM Tris/HCl buffer . As reported in Figure 2 , the frequency of the three types of profiles radically changed in different ionic strength conditions . The most remarkable result is , again , the significant increase in the population of the β-like structures with buffer concentration ( up to about 28% ) and the parallel decrease of the percentage of the mechanically weak structures . An increase of random coil curves is also observed , as occurs in the presence of Cu2+ , but unlike the case of the A30P mutant .
We observed a marked increase of the population of “β-like” conformers under three very different conditions known to accelerate αSyn aggregation . This result links the population of those αSyn monomeric conformers to the process of αSyn aggregation . The first condition is the presence of a μM concentration of Cu2+ . Our results in this condition agree with the observation of a metal-induced partially folded intermediate by Uversky , Li , and Fink [35] . Also Rasia et al . suggested a compact set of metal-induced conformations , noticing that the specific binding of Cu2+ to the αSyn N terminus requires the formation of a metal-binding interface ( pivoted on His 50 ) , which possibly involves residues that are widely separated in the primary amino acid sequence [36] . The second condition is the A30P mutation . Nuclear magnetic resonance ( NMR ) experiments have observed a much more flexible average conformation of the αSyn mutants A30P and A53T . The increased average flexibility of αSyn allows the protein to sample a larger conformational space . [58] . Interestingly , the mean hydrodynamic radius of αSyn is not affected by the A30P and A53T mutations [21 , 59] , thus showing that the increased flexibility is compatible with the population of compact folded structures like those singled out by our experiments . The third condition is a radical increase of the ionic strength . Our results in 500 mM Tris/HCl can be reconciled with the model proposed by Hoyer et al . [26] and by Bernado et al . [28] to explain the well-documented phenomenon of the increased αSyn fibril formation with increasing ionic strength . According to that model , the increased fibril formation is explained just on the basis on an increased freedom of the fibrillogenic NAC region caused by the release of its interaction with the negatively charged C-terminal tail . The increased ionic strength of the buffer leads to a more efficient charge shielding of the strongly acidic C-terminal tail , thus relieving its electrostatic self-repulsion . This in turn leads to the lowering of the protein-excluded volume and increases its flexibility . According to our data in Figure 2 , we should add to this model a shift of the conformational equilibrium toward the β-like structures that takes place on increasing the charge shielding . Any assignment of force spectroscopy signals to a definite secondary canonical structure must be supported by independent structural data . We have labeled as β-like those conformers with a mechanical behavior closely matching those of structures rich in β sheets . The correlation of the population of these structures with aggregation conditions , which enrich β sheet content in αSyn , supports this labeling . Evidence of some β sheet content in the monomeric state of αSyn was previously reported in the literature . Most recently by means of NMR spectroscopy in supercooled water at minus 15 °C , it was found that the αSyn chain , cold-denatured to an hydrodynamic radius equivalent to that displayed by the same protein in 8-M urea , retains a surprising amount of unpacked β strand content that correlates with the amyloid fibril β structure [32] . The packing of these β strands into compact structures like those observed by us is thus likely to occur in nondenaturing conditions and at physiological temperatures . This NMR result supports our observation of β-like conformers in the monomeric state of αSyn and links them to the amyloid β structure . The presence of β sheet structures was indicated also by Raman spectra of this protein in aqueous solution[33] . In the same investigation , CD spectroscopy proved unable to detect any β content . Correspondingly , the CD spectra of αSyn recorded by us in 10 mM and 500 mM Tris were practically superimposable . We conclude that CD is not a technique sensitive enough to detect partial β-sheet content in the αSyn sample . A fraction of β-sheet/extended structure of about 19% was also detected , again not by CD , but by Fourier transform infrared ( FTIR ) spectroscopy in dried films of αSyn [60] . This fraction is much larger than that estimated by our experiments in 10 mM Tris/HCl buffer ( see Figure 2 ) . However , the conditions of the SMFS and FTIR experiments were markedly different , and in the latter case , some template-mediated formation of β structures due to the packing of the αSyn molecules in the dried films required by the FTIR measurements cannot be ruled out . In conclusion , despite the fact that force spectroscopy data cannot directly assign a specific secondary structure to the conformers we have labeled as β-like , it is most likely that they have significant β sheet content . By now , any structural characterization of the mechanically weak interactions events monitored by the small peaks in force curves as in Figure 1D ( right panels ) is at best tentative and falls outside the focus of the present work . A more detailed characterization of these events is , however , within the range of capabilities of the techniques proposed here and is being currently addressed in our laboratory ( see Protocol S1 for preliminary measurements ) . A plausible explanation of the short- and long-distance mechanically weak interactions we observed cannot exclude the interaction between positively charged residues on the αSyn N terminal and the negatively charged surface of I27 modules [61] . It has been documented that αSyn in contact with negatively charged surfaces assumes an α helix structure [37–39 , 62 , 63] . We might expect a similar structural transition in the αSyn moiety also from the contact with the I27 modules within the 3S3 or 1S1 constructs ( see Protocol S1 ) . This transition is indicated by the CD spectra of the 1S1 construct in 10 mM Tris/HCl ( see Figure 4B ) . We propose that the small peaks like those shown in Figure 1D and assigned to the mechanically weak interactions can be the signature of the interaction between αSyn , possibly in α helical form , and the flanking I27 modules . It is not surprising that more than one of those signals are present in the same force curves , because multiple interactions of this type can occur at the same time in the same molecule . It should be noted that the same transition does not take place when free αSyn is mixed in solution with I27 modules of the 3T construct ( see Protocol S1 ) . An electrostatic model , based on the interaction lengths calculated from the positions of the small peaks in the force curves like those displayed in Figure 1D ( right panels ) , is proposed in Protocol S1 . Notably , these short- and long-distance mechanically weak interactions are observed to be in equilibrium with the β-like structures . The population of the former always decreases while that of the latter increases . This result is in accord with the observation by Zhu et al . that a driving force to α helical structures inhibits αSyn fibril formation [60] and also rule out any template-mediated β sheet imprinting by the I27 linkers . This conclusion is confirmed by the data on 500 mM Tris/HCl buffered solutions showing that when electrostatic interactions between the αSyn moiety and the flanking I27 linkers are decreased , the population of β-like conformers increase . We can also expect entropic effects due to the presence of the flanking I27 domains to drive the protein toward more extended conformations rather than compact conformations [64 , 65] . These considerations indicate that the design and use of alternative linkers or experimental strategies may prove useful in the future to further discriminate the effective conformational distribution of αSyn from alterations due to the interaction with the linkers . For the first time , to our knowledge , we applied the AFM-based single-molecule mechanical unfolding methodology to a multimodular protein containing the αSyn moiety . This approach brings into play three main methodological capabilities inaccessible to the bulk ensemble–averaged spectroscopies previously applied to study the structure of αSyn and other natively unstructured proteins . The first is the possibility to work strictly at the single-molecule level , thus ensuring that the conformer distribution of the monomeric αSyn is detected and quantified without interference from oligomeric soluble forms of the protein and therefore of any possible intermolecular imprinting toward the amyloidogenic β structures . The second capability is that of spanning the conformational space of the protein under investigation and of directly catching and quantifying all of its conformers with a lifetime longer than 10−3 s . These conformers , because of their longer life time , might be the most biologically relevant . Three classes of the monomeric αSyn conformations , including random coil , mechanically weakly folded and β-like , were characterized by our experiments . They could be detected even in low concentration without the necessity of selectively enhancing one of them by adding specific agents to unbalance the conformational equilibrium , as most commonly done so far with bulk ensemble–averaged experiments . The third capability is that of following shifts in the population of these classes of conformers in response to changing the solution conditions or the protein sequence and to detect them even if scarcely populated . In the case of αSyn , conditions known to promote oligomerization and aggregation—like the presence of Cu2+ , the A30P mutation , or a radical increase of ionic strength—markedly shift its conformational equilibrium toward the β-like form at the expense of other structures . These results indicate that the β-like curves contain the signature of the structural precursor to αSyn oligomerization . We suggest that the different aggregation propensities and , ultimately , the pathogenicity displayed by αSyn under different environmental conditions or point mutations can be triggered by unbalancing the delicate equilibria among αSyn conformers . These capabilities suggest that in the near future , single-molecule methodologies will play a crucial role in studies of the folding equilibria of the NUP monomers and , in particular , in the detection and quantification of the conformers that can lead to aggregation of those proteins . Our results suggest the feasibility of single-molecule approaches to the testing of novel pharmacological or biophysical therapies for pathologies involving the conformational equilibria of NUPs .
We followed the protein construct design proposed by J . Fernandez for the study of the random coiled titin N2B segment [41] . Chimeric polyproteins were obtained starting from pAFM1–4 , pAFM5–8 , and pAFM ( I27 ) 3mer vectors , kindly provided by Professor Jane Clarke ( Cambridge University , United Kingdom ) and constructed according to [43] . αSyn or its A30P mutated sequences were amplified by PCR using two different pairs of primers , each containing unique restriction sites . A first pair contained KpnI and XbaI sites , and a second one contained SacI and BssHII sites . The original eight I27 module plasmid was reconstituted from pAFM1–4 and pAFM5–8 , obtaining the pAFM8m vector . pAFM8m was then digested with KpnI and XbaI and ligated to the amplified αSyn sequence , then cleaved by the same enzymes in substitution of the two central titin modules to give the pAFM3s3 vector ( see Protocol S1 ) . By a similar strategy , the pAFM ( I27 ) 3mer vector was digested with SacI and BssHII , and the central titin module replaced by αSyn sequence , obtaining the pAFM1s1 vector . The obtained expression plasmids , pAFM3s3 and pAFM1s1 , code for two chimeric polyproteins composed of a single αSyn module flanked on either side by three tandem I27 domains or by just one , named 3S3 and 1S1 , respectively . The two pAFM8m and pAFM ( I27 ) 3mer vectors ( coding for two recombinant poly ( I27 ) proteins named 8T and 3T ) were transformed into Escherichia coli C41 ( DE3 ) cells [66] ( obtained from Professor John E . Walker [Medical Research Council–Dunn Human Nutrition Unit , Cambridge , United Kingdom] with the agreement of the Medical Research Council center of Cambridge ) . The cells were grown and the expression of proteins was induced as described in [43] . Recombinant proteins were purified by Ni2+-affinity chromatography in 20 mM sodium phosphate buffer pH 8 , 500 mM NaCl; the elution from the resin was obtained with 20 mM imidazole . After dialysis , proteins were kept at −80 °C in phosphate buffered saline ( PBS ) with 15% glycerol . The purification gel is shown in Protocol S1 . CD measurements were carried out on a JASCO J-715 spectropolarimeter interfaced with a personal computer . The CD spectra were acquired and processed using the J-700 program for Windows . All experiments were carried out at room temperature using HELLMA quartz cells with Suprasil windows and an optical path length of 0 . 1 cm . Spectra were recorded in the 190–260 nm wavelength range using a bandwidth of 2 nm and a time constant of 2 s at a scan speed of 50 nm/min . The signal-to-noise ratio was improved by accumulating at least four scans . All spectra are reported in terms of mean residue molar ellipticity [Θ]R ( deg cm2 dmol−1 ) . Fluorescence emission spectra were recorded on a Perkin-Elmer LS 50 spectrofluorimeter equipped with a thermostated cell compartment and interfaced with a personal computer using the FL-WinLab program for Windows . Sample measurements were carried out using a HELLMA ultra-micro cell with Suprasil windows and an optical path length of 10 × 2 mm . Fluorescence spectra were obtained at 25 °C using an excitation wavelength of 288 nm , with an excitation bandwidth of 4 nm and emission bandwidth of 4 nm . Emission spectra were recorded between 290–380 nm at a scan rate of 60 nm/min . Due to the well-known structuring effects of divalent metal ions on αSyn [35] , an accurate elemental analysis of the buffer was performed to exclude artifacts in our results due to metal contamination . The high concentration Tris-buffer solution ( 500 mM ) was analyzed for metal contents by atomic absorption spectroscopies . The measured concentrations were Cu = 0 . 2 ± 0 . 1 nM , Zn = 3 . 5 ± 0 . 1 nM , Fe = 0 . 9 ± 0 . 1 nM , and Ca = 22 . 5 ± 0 . 1 nM . These values are two orders of magnitude lower than the concentration required to induce structural effects on αSyn [67] . Gold ( Alfa Aesar , 99 . 99% ) was deposited onto freshly cleaved mica substrates ( Mica New York Corp . , clear ruby muscovite ) in a high-vacuum evaporator ( Denton Vacuum , model DV502-A ) at 10−5 Torr . Before deposition , the mica was preheated to 350 °C by a heating stage mounted behind the mica to enhance the formation of terraced Au ( 111 ) domains . The typical evaporation rate was 3 Å/s , and the thickness of the gold films ranged around 300 nm . The mica temperature was maintained at 350 °C for 2 h after deposition for annealing . This method produced samples with flat Au ( 111 ) terraces . These films were fixed to a glass substrate with an epoxy ( EPO-TEK 377 , Epoxy Tech . ) . They were then separated at the gold–mica interface by peeling immediately before functionalization with the desired molecules . This procedure produced gold substrates with a flat surface morphology due to the templating effect of the atomically flat mica surface [68 , 69] . For each experiment , a 20 μl drop of 3S3 construct solution ( 160 μg/ml ) was deposited on the freshly peeled gold surface for about 20 min . SMFS experiments were performed using a commercially available AFM system: Picoforce AFM with Nanoscope IIIa controller ( Digital Instruments ) using V-shaped silicon nitride cantilevers ( NP; Digital Instruments ) with a spring constant calibrated by the thermal noise method [70] . The pulling speed was 2 . 18 μm/s for all experiments . The buffer used was Tris/HCl ( 10 mM or 500 mM , pH 7 . 5; the 10 mM buffer was obtained by diluting the 500 mM buffer with milliQ ultrapure water ) . For CuCl2 experiments , the protein was deposited in a drop with the addition of a final concentration of 1 μM CuCl2 and left on the surface for about 20 min , and the experiments were carried out in 10 mM Tris/HCl with 1 μM CuCl2 . Control experiments in DTT were made in 50 mM DTT Tris/HCl buffer . The force curves were analyzed using the commercially available software from Digital Instrument ( Nanoscope v6 . 12r2 ) , custom Origin scripts and Hooke , a Python-based home coded force spectroscopy data analysis program ( M . Sandal , unpublished work ) . Force curves were analyzed fitting each peak with a simple WLC force versus extension model [45] with two free parameters: the contour length L and the persistence length p ( Equation 1 ) . The I27 modules were characterized in terms of the length of the polypeptide chain extended after each unfolding event . To assess the statistical validity of the comparison between data obtained in 10 mM Tris/HCl buffer and those obtained in other conditions , standard chi square tests were performed . The differences between the 10 mM Tris data set and the other data sets are significant , with p < 0 . 01 .
The UniProt KB ( http://www . ebi . ac . uk/trembl/index . html ) accession number for α-synuclein ( αSyn ) is P37840 ( SYUA_HUMAN ) . The Protein Data Bank ( http://www . pdb . org ) entry for the I27 domains included in the polyprotein is 1tit . The Online Mendelian Inheritance in Man ( OMIM ) reference number for familial Parkinsonism is 163890 . 0002 .
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Natively unstructured proteins defy the classical “one sequence–one structure” paradigm of protein science . In pathological conditions , monomers of these proteins can aggregate in the cell , a process that underlies neurodegenerative diseases such as Alzheimer and Parkinson . A key step in the aggregation process—the formation of misfolded intermediates—remains obscure . To shed light on this process , we characterized the folding and conformational diversity of αSyn , a natively unstructured protein involved in Parkinson disease , by mechanically stretching single molecules of this protein and recording their mechanical properties . These experiments permitted us to observe directly and quantify three main classes of conformations that , under in vitro physiological conditions , exist simultaneously in the αSyn sample . We found that one class of conformations , “β-like” structures , is directly related to αSyn aggregation . In fact , their relative abundance increases drastically in three different conditions known to promote the formation of αSyn fibrils . We expect that a critical concentration of αSyn with a “β-like” structure must be reached to trigger fibril formation . This critical concentration is therefore controlled by a chemical equilibrium . Novel pharmacological strategies can now be tailored to act upstream , before the aggregation process ensues , by targeting this equilibrium . To this end , single-molecule force spectroscopy can be an effective tool to tailor and test new pharmacological agents .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
] |
[
"biochemistry",
"neurological",
"disorders",
"biophysics"
] |
2008
|
Conformational Equilibria in Monomeric α-Synuclein at the Single-Molecule Level
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The ability to switch between yeast and filamentous forms is central to Candida albicans biology . The yeast-hyphal transition is implicated in adherence , tissue invasion , biofilm formation , phagocyte escape , and pathogenesis . A second form of morphological plasticity in C . albicans involves epigenetic switching between white and opaque forms , and these two states exhibit marked differences in their ability to undergo filamentation . In particular , filamentous growth in white cells occurs in response to a number of environmental conditions , including serum , high temperature , neutral pH , and nutrient starvation , whereas none of these stimuli induce opaque filamentation . Significantly , however , we demonstrate that opaque cells can undergo efficient filamentation but do so in response to distinct environmental cues from those that elicit filamentous growth in white cells . Growth of opaque cells in several environments , including low phosphate medium and sorbitol medium , induced extensive filamentous growth , while white cells did not form filaments under these conditions . Furthermore , while white cell filamentation is often enhanced at elevated temperatures such as 37°C , opaque cell filamentation was optimal at 25°C and was inhibited by higher temperatures . Genetic dissection of the opaque filamentation pathway revealed overlapping regulation with the filamentous program in white cells , including key roles for the transcription factors EFG1 , UME6 , NRG1 and RFG1 . Gene expression profiles of filamentous white and opaque cells were also compared and revealed only limited overlap between these programs , although UME6 was induced in both white and opaque cells consistent with its role as master regulator of filamentation . Taken together , these studies establish that a program of filamentation exists in opaque cells . Furthermore , this program regulates a distinct set of genes and is under different environmental controls from those operating in white cells .
Morphological plasticity is key to the lifestyle of fungal pathogens such as Candida albicans , the most frequently isolated human fungal pathogen . The best-studied morphological switch in C . albicans is the transition between yeast and true hyphae or pseudohyphae ( filamentous forms ) . Pseudohyphal cells are highly branched and consist of ellipsoidal cells with constrictions at the septa . In contrast , hyphal cells are less branched , have parallel sides , and lack constrictions at the septa [1] , [2] . The yeast-hyphal switch regulates C . albicans pathogenesis , as hyphal forms adhere to and invade epithelial cells during mucosal infections , resulting in extensive damage to host cells [2] . The switch to hyphae is also induced upon phagocytosis by macrophages , allowing pathogen evasion from immune capture [3] , [4] . Furthermore , the hyphal form is important for virulence in systemic models of disease , although it is not clear if the hyphal morphology per se or genes co-regulated with the morphological transition are critical for virulence [2] . The yeast-hyphal transition in C . albicans is induced in response to a wide variety of environmental stimuli including serum , neutral pH , nutrient limitation , high CO2 concentrations , and embedded conditions [2] , [5] . The transcriptional regulation of filamentation is complex , but many stimuli act via two major signaling pathways: a cyclic AMP-dependent pathway that depends on the Efg1 transcription factor , and a mitogen-activated protein kinase ( MAPK ) pathway that activates the Cph1 transcription factor [6] , [7] . In addition , most filamentation-inducing conditions require a temperature of 37°C ( or higher ) for efficient filamentous growth [2] , [5] . The temperature requirement appears to be mediated by Hsp90 , as compromising Hsp90 activity promotes filamentation in response to serum at 30°C [8] . A second morphological switch involves the interconversion between white and opaque forms of C . albicans . This is an epigenetic switch that allows rapid and reversible switching between white cells that are round and opaque cells that are ellipsoidal [9] . Genes present at the Mating-Type Like ( MTL ) locus strictly regulate the white-opaque switch , so that only MTLa or MTLα strains can undergo the transition to the opaque form , while MTLa/α strains are permanently locked in the white state [10] . Opaque cells are the mating-competent form of C . albicans , undergoing mating approximately a million times more efficiently than white cells [10] . In addition to mating , the white-opaque switch also regulates multiple other facets of C . albicans biology . White cells are more virulent in systemic infections than opaque cells , while conversely opaque cells are better colonizers in skin infections than white cells [11] , [12] . The two cell states also interact differently with immune cells , as opaque cells are less susceptible to phagocytosis by macrophages [13] . It is therefore apparent that white and opaque cells are differentially programmed in many aspects of their behavior , including their interaction with the host . In this work , we compare the ability of the two phenotypic states , white and opaque , to undergo filamentation . Studies on filamentation have almost exclusively utilized cells in the white state . In this form , cells are readily induced to form hyphae or pseudohyphae in response to diverse stimuli . In contrast , opaque cells do not undergo filamentation in response to these stimuli and typically grow only in the budding yeast form [14] . One indication that opaques may form filaments under specialized conditions came from studies using a perfusion chamber; opaque cells attached to the wall of the chamber formed hyphae , but did not do so when grown in suspension [15] . Additionally , opaque cells can undergo polarized growth in response to mating pheromones , establishing that these cell types are competent for filament-like growth [16]–[18] . Here , we establish that opaque cells fail to form filaments under a variety of environmental conditions that induce efficient hyphal growth in white cells including serum , neutral pH , and nutrient limitation . In contrast , we show that opaque cells undergo efficient filamentation under certain conditions in liquid and solid media , but these conditions are different from those that induce filamentation of white cells . In particular , growth of opaque cells in certain nutrient-poor media or in the presence of the sugar sorbitol efficiently induced filamentous growth , whereas white cells continued to grow as budding yeast under these conditions . Both hyphal-like and pseudohyphal-like cells were observed in opaque cells , but the ratio depended on the inducing signal . In addition , thermal regulation of filamentation in opaque cells is reversed relative to that in white cells . While filamentation in white cells is optimal at 37°C under most conditions , opaque cells underwent filamentation efficiently at 25°C , and elevated temperatures suppressed filamentous growth . Genetic analysis and transcriptional profiling of filamentous opaque cells revealed both similarities and differences between the filamentation programs in opaque and white cells . These studies establish nutrient-controlled filamentous growth in C . albicans opaque cells and indicate fundamental differences between the environmental cues regulating filamentation in white and opaque cells .
Synthetic complete dextrose medium ( SCD ) and yeast extract peptone dextrose medium ( YPD ) were made as described previously [19] . YPD plates containing 200 µg/ml nourseothricin were used for selection of strains that were resistant to nourseothricin ( Werner Bioagents , Jena , Germany ) as previously described [20] . SCD low phosphate ( LP ) medium was made with yeast nutrient base ( YNB ) w/o phosphate ( cat . #CYN6701 , Formedium LTD , Hunstanton , England ) and KH2PO4 was supplemented to a final concentration of 10 µM . LP medium was adjusted to pH 4 . 7 with 3 M HCl before autoclaving . Synthetic low ammonium dextrose ( SLAD ) medium was made as described previously [21]; 2% agar was washed five times with distilled water , autoclaved with 1 . 7 g/L YNB w/o ammonia , and then supplemented with ammonium sulfate and dextrose to final concentrations of 50 µM and 2% , respectively . Sorbitol medium ( SOR ) was SCD supplemented with 1 M sorbitol . Minimal ( MIN ) medium consisted of 7 g/L YNB and 2% dextrose . Spider medium contained 1 . 35% agar , 1% nutrient broth , 0 . 4% potassium phosphate , and 2% mannitol ( pH 7 . 2 ) . N-acetyl glucosamine containing medium ( GlcNAc ) was modified Lee's medium [22] without glucose but supplemented with 1 . 25% N-acetyl glucosamine ( Sigma ) . Neutral pH medium was made by buffering SCD with 150 mM HEPES ( pH 7 . 0 ) . Calcofluor white stain was obtained from Fluka Biochemika and geldanamycin from A . G . Scientific , Inc . C . albicans strains used in this study are listed in Table 1 and oligonucleotides in Table S1 . All strains are derived from SC5314 unless stated otherwise . To construct the pACT1-WOR1 plasmid , the promoter of the ACT1 gene was PCR amplified with oligonucleotides 610 and 611 and the WOR1 ORF with oligonucleotides 2421 and 2422 . The two fragments were then combined in a single fusion PCR reaction using oligos 610 and 2422 and cloned into plasmid pSFS2A [20] between ApaI and XhoI restriction sites . A modified pSFS2A plasmid was also constructed in which the SAT1 gene was replaced with the gene for hygromycin B resistance . A region of plasmid pSFS2A was PCR amplified with oligos 1669 and 1652 and the gene for hygromycin B resistance amplified from plasmid pYM70 [23] with oligos 1651/1653 . The two PCR products were combined by fusion PCR using oligos 1669/1651 and cloned between HindIII and PstI restriction sites in the pSFS2A backbone . The resulting pACT1-WOR1 plasmids ( either with SAT1 or HYG markers ) were digested with BglII to linearize in the pACT1 region and integrated into the endogenous ACT1 locus to obtain constitutive WOR1 expression . Correct integration was confirmed by PCR . To target RFG1 for deletion , a fusion PCR product was created as described previously [24] . Briefly , oligos 984/992 and 985/993 were used to PCR the 5′ and 3′ homologous flanks of RFG1 and these flanks combined with a selectable marker ( LEU2 ) by fusion PCR [24] . The pSFS2 ( rfg1::SAT1 flipper ) plasmid [25] ( a gift from David Kadosh , University of Texas San Antonio ) was used to delete the second copy of RFG1 and generate rfg1::LEU2/rfg1::SAT1 double deletion mutants , as previously described [25] . The SAT1 marker was subsequently excised by growth on maltose medium [20] to obtain strain CAY2214 . Fusion PCR was also used to delete CPH1 or EFG1 from CAY2214 , using the selectable markers HIS1 and ARG4 to generate rfg1/cph1 and rfg1/efg1 double mutants . Oligos 982/990 and 983/991 were used to PCR amplify the 5′ and 3′ flanks of the CPH1 gene , and oligos 1215/2425 and 1216/1217 used to amplify the 5′ and 3′ flanks of the EFG1 gene . The MTLα locus was also deleted from efg1 , czf1 and cph2 mutant strains acquired from the Fungal Genetics Stock Center ( strains originally generated by Homann et al . [26] ) using the plasmid pRB102 , as previously described [27] . The SAT1 marker was subsequently excised by growth on maltose medium [20] to generate MTLa/MTLαΔ::FRT strains . Correct integration of constructs was verified by PCR across 5′ and 3′ disruption junctions , and loss of the ORF was confirmed with primers internal to the open reading frame . Auxotrophic strains were also transformed with C . albicans LEU2 , HIS1 , or ARG4 , PCR amplified by oligos 2490/2491 , 2492/2493 or 2494/2495 , respectively , to restore prototrophy to these strains . White- and opaque-specific reporter constructs were generated as follows . For the opaque-reporter , the SAT1 gene was PCR amplified from pSFS2A [20] using primers 169/170 and cloned into pCR-Blunt II-TOPO ( Invitrogen ) between XhoI and XbaI restriction sites . A triple mCherry reporter was next integrated into this vector by PCR amplifying three copies of the mCherry gene from plasmid pADH77 [28] with oligonucleotides 1846/1847 , 1848/1849 and 1850/1386 . The three mCherry PCR products were stitched together using BamHI/AflII , AflII/StuI , and StuI/SalI restriction sites and integrated between BamHI and XhoI sites in the vector backbone to generate plasmid pRB224 . The C . albicans OP4 promoter was then PCR amplified using oligos 1974/1975 and cloned between KpnI and SacI restriction sites in pRB224 to generate the final reporter construct pRB227 . This plasmid was linearized in the OP4 gene with BsgI and transformed into C . albicans as an opaque-specific reporter . For the white-cell reporter , the C . albicans WH11 gene promoter was PCR amplified with oligos 1384/1396 and the GFP gene amplified from pADH76 [28] with oligos 1385/1386 . These PCR products were fused by PCR with oligos 1384 and 1386 , digested with ApaI/SalI and cloned into pSFS2A to generate pRB168 . This plasmid was linearized within the WH11 gene with AatII and transformed into C . albicans to generate a white-specific reporter . An EFG1 complementation plasmid was constructed by PCR amplification of EFG1 using oligos 1838/1839 , and cloning between ApaI and KpnI sites in the modified pSFS2A for hygromycin B resistance . The resulting EFG1 addback plasmid pRB326 was linearized by HpaI and integrated into the endogenous EFG1 locus in the efg1 mutant strain CAY3292 . Similarly , oligos 1832/1833 were used to PCR amplify UME6 and the PCR product cloned between ApaI and KpnI sites in pSFS2A ( hygromycin B ) to generate plasmid pRB328 . This plasmid was linearized by SmaI and integrated into the endogenous UME6 locus in the opaque ume6 mutant strain CAY1571 to obtain strain CAY3697 . Construction of a UME6 overexpressing strain was achieved using derivatives of strain MBY208 , a gift of David Kadosh . MBY208 contains a construct expressing high levels of an Escherichia coli tet repressor-Saccharomyces cerevisiae Hap4 activation domain fusion protein , as well as a second construct expressing the UME6 gene under the control of the E . coli tet operator [29] . The MTLα locus was deleted in MBY208 using a derivative of pRB102 ( contains hygromycin B marker ) to generate white ( CAY4504 ) and opaque ( CAY4502 ) strains . Single opaque or white colonies were inoculated into liquid LP and SOR media at room temperature . Cells were harvested by centrifugation after 12 hours ( LP ) or 16 hours ( SOR ) , and pellets frozen in liquid nitrogen . Total RNA was extracted from cell pellets ( 8–10 OD ) following the RiboPure-Yeast Kit protocol ( Applied Biosystems , Bedford , MA ) . RNA was treated with DNaseI ( Applied Biosystems ) to eliminate DNA contamination and re-extracted with phenol/chloroform . For quality control , RNA was analyzed using an Agilent 2100 Bioanalyzer to check RNA integrity . Aminoallyl-labeled cDNA synthesis and hybridization to microarrays was previously described by Tuch et al . [30] . Arrays were scanned on a GenePix 4000 scanner ( Axon Instruments ) , data quantified using GENEPIX PRO version 3 . 0 and normalized using Goulphar ( http://transcriptome . ens . fr/goulphar ) . Pairwise average linkage clustering analysis was performed using CLUSTER and visualized by TREEVIEW [31] . Significance Analysis of Microarrays ( SAM , http://www-stat . stanford . edu/~tibs/SAM/ ) [32] and R ( Ver 2 . 15 . 2 , http://www . r-project . org/ ) were used to screen the statistically significant genes induced in filamentous opaque cells in LP or SOR medium versus SCD medium ( four replicas each ) . The parameters used for screening and SAM results are provided in the supplemental data ( see tables in Text S1 and Text S2 ) . The Candida genome database ( www . candidagenome . org ) and the Yeast Genome Database ( http://www . yeastgenome . org/ ) were used to facilitate further analysis . Array data has been uploaded to GEO ( accession number GSE42963 ) . White and opaque cells were streaked onto thin agar plates . After 22 hours of growth , a small square ( ∼1 cm2 ) was cut from the plate and stained with calcofluor white . Digital images of cells were collected with Infinity analyzer software and an Infinity 2 digital camera ( Lumenera Corporation , Ottawa , Canada ) . DIC and fluorescent images were collected with a Zeiss Inverted Microscope ( Axio Observer . Z1 ) fitted with an AxioCam HR . Images were processed with AxioVision Rel . 4 . 8 ( Zeiss , Germany ) . C . albicans opaque cells were grown on solid SOR or LP media at 25°C for 22 hours . C . albicans white cells were grown in liquid YPD + 10% serum at 37°C for 2 hours . Cells were resuspended in water and attached to poly-L-lysine coated-coverslips . Cells were fixed with 2 . 5% ( w/v ) glutaraldehyde in 0 . 1 M Na-cacodylate buffer , pH 7 . 4 at 4°C , and washed with 0 . 1 M Na-cacodylate buffer , pH 7 . 4 . The cells were postfixed with 1% aqueous osmium tetroxide in 0 . 1 M Na-cacodylate buffer , pH 7 . 4 at 25°C for 90 minutes , and washed with 0 . 1 M Na-cacodylate buffer , pH 7 . 4 . Following fixation , cells were dehydrated gradually using a gradient ethanol series and subsequently dried in a critical point dryer . The samples were then coated with 20 nm gold palladium ( 60∶40 ) in an Emitech K550 sputter coater . Cells were imaged with a Hitachi S-2700 scanning electronic microscopy and collected with Quartz PCI software .
Filamentation of C . albicans white cells occurs in response to a wide variety of environmental stimuli , including neutral pH , serum , nutrient limitation , and CO2 [2] , [5] . We first addressed whether C . albicans opaque cells can undergo filamentous growth under any of the established conditions that promote white cell filamentation . As opaque cells are unstable at 37°C in vitro [9] , MTLa/a strains were locked in the opaque state by expressing the WOR1 gene under a constitutive promoter . WOR1 is the master regulator of the white-opaque switch , and constitutive expression of WOR1 ensures that cells are stably maintained in the opaque state [33]–[35] . As shown in Figure 1 , white cells efficiently formed hyphae when grown in YPD medium supplemented with serum at 37°C , in low-nutrient Spider medium at 30° or 37°C , or in neutral pH Lee's medium at 37°C . Growth in YPD medium at 37°C was also sufficient to induce filamentous growth in white cells , as previously observed [36] ( Figure 1B ) . In contrast , opaque cells did not undergo efficient filamentation under any of these culture conditions , and instead continued to grow predominantly as budding yeast cells ( Figure 1 ) . For example , > 99% of white cells grown in YPD+serum medium underwent filamentation , while less than 5% of opaque cells formed filaments under the same conditions . These results establish that C . albicans opaque cells do not form filaments efficiently under many of the standard in vitro conditions that induce white cell filamentation . We next screened a series of in vitro culture conditions to identify environments that induce robust filamentous growth in opaque cells . Opaque-locked strains ( overexpressing WOR1 ) or natural opaque strains were cultured on a variety of media , and colony and cell morphologies examined for evidence of filamentation . At least three distinct environmental cues were found to activate a program of opaque cell filamentation . First , growth of opaque cells on medium containing the sugar sorbitol ( synthetic complete dextrose medium supplemented with 1 M sorbitol; SOR medium ) produced highly wrinkled colonies containing cells that were highly filamentous ( Figure 2A ) . Filamentation was induced within 24 hours , and colony and cell morphologies are shown at 4 days of growth at 25°C . Staining of the cell walls with calcofluor white revealed that filaments consisted of cells with parallel sides and no constrictions at the septa , similar to true hyphae formed by white cells ( Figure 2A ) . Second , growth on minimal medium ( MIN medium; synthetic medium which lacks amino acids ) induced filamentous growth producing highly wrinkled colonies ( Figure 2B ) . Colony phenotypes were even more marked in minimal medium lacking nitrogen ( SLAD medium ) or minimal medium containing low phosphate concentrations ( LP medium ) , as these conditions induced extensive peripheral filamentation around the edges of the colonies ( Figure 2C and D ) . Filamentation was induced within 24 hours and colonies are shown after 4 days of growth at 25°C . Filaments from MIN , SLAD , and LP resembled pseudohyphae rather than true hyphae , as cells were highly elongated and branched , with slight constrictions at each of the branch points ( Figure 2B–D ) . Third , growth of opaque cells in the presence of the carbon source N-acetyl glucosamine ( GlcNAc medium; Lee's medium containing 1 . 25% GlcNAc ) induced efficient filamentation . Filamenting opaque cells resembled pseudohyphal cells with constrictions present at the septa ( Figure 2E , cells shown after 24 hours and colonies after 4 days at 25°C ) . GlcNAc and SLAD have previously been reported to be activators of hyphal growth in white cells [37] , [38] , but we found that this required extended incubation for 5 days or longer at 37°C . In fact , white cells did not undergo filamentation under any of the conditions that efficiently induce opaque cell filamentation ( compare white and opaque cell and colony morphologies in Figure 2 ) . Filamentous phenotypes were similar when using either natural opaque cells or those locked into the opaque state by constitutive WOR1 expression ( compare Figure 2 and Figure S1 ) . In addition , removal of cells from the filamenting opaque colonies gave rise to regular opaque colonies when incubated on SCD medium ( data not shown ) . This result indicates that opaque cells did not switch to the white state when grown under filament-inducing conditions but were stably maintained as opaque cells . The results described above were achieved using culture on solid media , but filamentation was also observed in liquid culture . For example , growth of opaque cells in liquid SOR or LP medium was found to efficiently induce filamentous growth ( Figure S2B and C ) . In contrast filamentation in MIN or SLAD media was considerably reduced compared to that on solid media ( Figure S2D and E ) . We therefore establish that opaque cells can filament in response to different environmental cues , and can do so when grown both in liquid culture and on agar plates . The structure of filamentous opaque cells was further analyzed by scanning electron microscopy and compared to that of hyphal white cells ( Figure 3 ) . These images confirmed that opaque cells grown on SOR medium resembled hyphal cells ( parallel sides with no constrictions ) while those grown on LP medium resembled pseudohyphal cells ( constrictions between elongated buds ) . These results establish that opaque cells can undergo a program of filamentous growth including either pseudohyphal-like or hyphal-like cells . Furthermore , we show that the environmental cues regulating filamentation in white and opaque cells are distinct , with different cues inducing filamentous growth in the two phenotypic states . The yeast-hyphal switch in C . albicans white cells is strongly influenced by temperature , with most filamentation-inducing conditions occurring at elevated temperatures ( e . g . serum induction of hyphae is increased at 37°C compared to 30°C [8] ) , although white cells also undergo filamentation at 25°C in response to certain cues [39] , [40] . To determine if opaque cell filamentation is temperature-dependent , filament-inducing conditions were tested at 25°C , 30°C , and 37°C . Opaque filamentation was most efficient at 25°C , with decreased filamentation observed at 30°C , and even less filamentation at 37°C ( Figure 4 ) . We confirmed that the lack of filamentation at 37°C was not due to opaque cells switching back to white by using opaque-locked strains that have constitutive WOR1 expression . The thermal regulation of filamentation was similar when compared for SOR , MIN , SLAD and LP media , indicating that elevated temperatures generally inhibit opaque cell filamentation ( Figure 4B–E ) . Again , white cells did not filament efficiently under any of the tested media conditions even when incubated at 37°C ( Figure 4A–E ) , further demonstrating the specificity of the program of opaque filamentation . The heat shock protein Hsp90 has been implicated as a key regulator of temperature sensing in C . albicans . Elevated temperatures compromise Hsp90's functional capacity and this is thought to promote filamentation of white cells [8] . Pharmacological inhibition of Hsp90 ( e . g . using the drug geldanamycin ) therefore stimulates filamentation in white cells [8] . We used geldanamycin ( GdA ) to test if loss of Hsp90 also affects filamentation in opaque cells . White and opaque cultures were treated with geldanamycin in liquid YPD medium at 25°C and 30°C . Inhibition of Hsp90 caused efficient induction of filamentation in white cells at 30°C , but only a very limited morphological response in opaque cells ( Figure S3 ) . In fact , opaque cells were more susceptible to geldanamycin treatment than white cells , with the drug causing cell death in a large proportion of the population . Surviving opaque cells exhibited mixed morphologies , indicating that Hsp90 does not play as dominant a role in regulating opaque cell filamentation as it does in white cells . Multiple signaling pathways regulate filamentation in C . albicans white cells , including the MAPK pathway and the cAMP pathway that mediate filamentation in response to serum , temperature , CO2 , and starvation [2] , [5] . The C . albicans MAPK pathway consists of a series of conserved kinases including Cst20 , Hst7 , and Cek1 that are homologous to S . cerevisiae Ste20 , Ste7 , and Kss1 , respectively . The terminal Cek1 kinase activates Cph1 , a transcription factor ( homologous to ScSte12 ) responsible for inducing filamentous growth [6] . The cAMP pathway is similarly activated in response to multiple environmental cues and mediates filamentation via the transcription factor , Efg1 [7] . Together , Cph1 and Efg1 can be regarded as master regulators of C . albicans filamentation , and mutants lacking both Cph1 and Efg1 are highly defective in filamentation in white cells [41] . To evaluate the role of the MAPK and cAMP pathways in opaque filamentation , we constructed MTLa/a Δcph1/Δcph1 and Δefg1/Δefg1 mutants to analyze MAPK and cAMP signaling , respectively . Construction in an MTL homozygous strain was necessary to allow for strains to switch between white and opaque . Loss of Cph1 had little , if any , effect on opaque filamentation under any of the inducing conditions ( SOR , MIN , LP , or GlcNAc media ) , indicating that the MAPK pathway does not play a major role in opaque filamentation . As shown in Figure 5 , opaque Δcph1/Δcph1 colonies appeared similar to wildtype colonies; they exhibited extensive peripheral filamentation and consisted of pseudohyphal or hyphal cells , depending on the inducing medium . We also found that deletion of CPH1 had only a very modest effect on filamentation phenotypes in white cells ( either MTLa/α or MTLa/a strains , see Figure S4 ) , indicating that the MAPK pathway plays a relatively minor role in regulating filamentation compared to that of the cAMP pathway , consistent with previous observations [41] . The role of EFG1 in white and opaque cell filamentation was also addressed . We note that loss of EFG1 has been shown to increase switching from white to opaque [42]–[44] , and that switching in efg1 mutants is regulated by pH [45] . We observed distinct white and opaque efg1 colonies on SOR , SLAD , and LP media , and loss of Efg1 resulted in decreased opaque cell filamentation under each of these culture conditions . For example , Δefg1/Δefg1 mutants were unable to form hyphal-like cells when grown on SOR medium and instead grew as chains of opaque cells ( Figure 5A ) . Similarly , whereas wildtype opaque cells formed highly elongated pseudohyphae when grown on SLAD or LP media , efg1 mutants formed chains of normal looking opaque cells on these media ( Figure 5B and C ) . Δefg1/Δefg1 mutants also produced smoother colonies compared to filamentous wildtype colonies ( Figure 5A–C ) . Reintegration of the EFG1 gene into the mutant background restored filamentation , confirming that the mutant phenotype was due to the loss of this gene ( Figure 5A–C ) . Loss of Efg1 was also shown to inhibit filamentation in white MTLa cells ( Figure S4 ) , consistent with its role in white a/α cells [7] . These results establish that Efg1 is a master regulator of filamentation in both C . albicans white and opaque cells . Several negative regulators of filamentation play prominent roles in controlling the yeast-hyphal transition in white cells . These include the transcription factors Nrg1 and Rfg1 , as well as the global repressor of gene transcription , Tup1 . Loss of any one of these regulators results in white cells growing as hyphae or pseudohyphae under conditions that normally support yeast cell growth [36] , [46]–[49] . Somewhat paradoxically , rfg1 mutants display a defect in hyphae formation under nutrient-limiting conditions [48] , while overexpression of RFG1 promotes pseudohyphal growth [50] . We constructed mutants in each of these factors in switching-competent ( MTL homozygous ) strains to define their role in opaque filamentation . As shown in Figure 6 , loss of Nrg1 or Tup1 resulted in filamentation of opaque cells even when cultured on SCD medium , a medium that normally does not induce filamentous growth . Wor1 was constitutively expressed in these cells to drive formation of opaque cells , as tup1 mutants do not undergo stochastic switching to the opaque state , at least in the WO-1 strain background [51] , [52] . Opaque colonies were extremely wrinkled in nrg1 and tup1 mutant strains , and consisted mostly of elongated pseudohyphal-like cells with constrictions at sites of cell division ( Figure 6A and B ) . Since the highly filamentous phenotype of the tup1 mutant is similar between white and opaque cells , we also constructed fluorescence reporters to confirm the phenotypic state of these cells . pOP4-GFP and pWh11-mCherry constructs were employed to indicate the opaque or white state , respectively . As expected , the pOP4-GFP signal was higher in opaque tup1 cells than in white cells , although basal OP4 expression was evident in white cells ( Figure S5B ) , consistent with previous observations [51] . Conversely , the pWh11-mCherry reporter was expressed in white cells but not opaque cells ( Figure S5A ) . These results establish that opaque tup1 mutants are maintained in the opaque state and are as filamentous as white tup1 mutants . In contrast to Nrg1 and Tup1 , loss of Rfg1 reduced filamentation in opaque cells under the conditions tested . For example , growth of rfg1 mutants on SOR , SLAD or LP media produced very weak filamentation compared to the wildtype strain ( Figure 6C–E ) . In general , rfg1 mutants grew as chains of opaque cells but cell shape was no longer extremely elongated and was reminiscent of the phenotype of efg1 mutants ( compare Figure 6C–E and Figure 5A–C ) . We further examined the role of Rfg1 in the context of Cph1 and Efg1 that act in the MAPK and cAMP pathways , respectively . Opaque Δcph1/Δcph1 Δrfg1/Δrfg1 double mutants exhibited a similar phenotype to rfg1 mutants , indicating that Cph1 has no effect on opaque filamentation in the rfg1 background ( Figure 6C–E ) . In addition , opaque efg1/rfg1 double mutants were analyzed , as both efg1 and rfg1 mutants exhibit reduced filamentation . It appeared that efg1/rfg1 opaque cells resembled efg1 and rfg1 single mutants , growing as chains of cells , and that some filamentation was still evident even in the absence of both transcription factors ( Figure 6C–E ) . Together , these results establish roles for Nrg1 , Tup1 , and Rfg1 in both white and opaque filamentation programs . Loss of Nrg1 or Tup1 transcriptional repressors results in activation of the program of filamentous growth in white and opaque cells . However , whereas Rfg1 is both a positive and negative regulator of filamentation in white cells depending on the conditions , we observe only a positive role for Rfg1 in promoting opaque cell filamentation . Recent studies have uncovered Ume6 as a key transcriptional regulator of hyphal and pseudohyphal growth in C . albicans white cells . In particular , it was shown that high levels of UME6 expression drive hyphal formation ( and increase virulence ) , whereas intermediate levels of UME6 expression resulted in pseudohyphal growth [29] . The cyclin-related protein Hgc1 is an important downstream target of Ume6 and mediates agar invasion , hyphal extension , and formation of true septa [53] . Hgc1 functions acts as part of a Hgc1/Cdc28 complex that promotes filamentation by phosphorylating Rga2 , a Cdc42 GAP protein , which in turn activates Cdc42 and drives actin polymerization [54] . In addition , both UME6 and HGC1 are negatively regulated by the Nrg1 and Tup1 transcription factors discussed above [25] , [55] . Mutants in UME6 and HGC1 were constructed and tested in MTLa/a strain backgrounds to determine their contribution to opaque filamentation phenotypes . Deletion of UME6 resulted in a marked decrease in opaque cell filamentation under each of the tested media conditions . Thus , growth of opaque ume6 mutants on SOR , LP , or MIN media generated chains of cells but cells were no longer highly elongated and no hyphal-like cells were observed . In fact , opaque ume6 cells resembled those of efg1 and rfg1 mutants ( compare Figure 7 and Figure 6C–D ) . These results show strong parallels between white and opaque cells , as deletion of UME6 also compromises white cell filamentation [25] , [55] . In white cells , UME6 overexpression also drives cells into the hyphal form [29] . We therefore addressed whether UME6 overexpression is sufficient to induce opaque cell filamentation by using a strain in which UME6 is under the control of the Escherichia coli tet operator ( tetO ) . The constructed strain also expresses an E . coli tet repressor-Saccharomyces cerevisiae Hap4 activation domain fusion protein . As a result , the UME6 gene is turned off in the presence of doxycycline , but is highly induced in the absence of doxycycline [29] . We found that overexpression of UME6 induced filamentous growth in both white and opaque cells , establishing UME6 as a master regulator of filamentation in both phenotypic states ( Figure S6 ) . The role of HGC1 in opaque cell filamentation was also examined . These mutants were constructed in the P37005 background of C . albicans that is a natural MTLa/a isolate . The wildtype P37005 opaque cells underwent filamentation in response to inducing conditions , including on SOR and LP media . In contrast , P37005 Δhgc1/Δhgc1 mutants showed a marked defect in opaque filamentation , as these strains were unable to form filaments when grown on SOR or LP media ( Figure 7E and F ) . The defect in hgc1 mutant filamentation was restored by complementation with the wildtype HGC1 gene ( Figure 7E and F ) . Together , these results indicate a shared role for UME6 and HGC1 in promoting filamentation in both white and opaque cells . The Ras1 protein plays a prominent role in white cell filamentation and acts upstream of both the MAPK and cAMP pathways . Mutants in ras1 show a severe defect in hyphal growth in white cells under multiple conditions , while strains expressing a dominant active Ras1 mutation ( Ras1G13V ) show enhanced hyphal formation [56] . Here , we tested the phenotype of Δras1/Δras1 mutants in opaque cells and observed a defect in opaque filamentation on SOR and LP media ( Figure S7 ) , consistent with cAMP signaling being necessary for filamentous growth ( compare to the efg1 mutant phenotype , Figure 5 ) . Curiously , expression of the constitutively active Ras1G13V allele also partially suppressed filamentation on LP and SOR media ( Figure S7 ) . Thus , either loss of RAS1 function or hyperactive Ras1 activity appears to decrease filamentation of opaque cells . Other pathways that regulate white cell filamentation include the Cph2/Tec1 pathway . Both of these factors are transcription factors and Cph2 is necessary for Tec1 expression , which in turn upregulates genes involved in hyphal development [57] , [58] . Mutants in cph2 and tec1 were examined in the opaque phase but these mutants had no significant effect on filamentous growth under any of the tested conditions ( Figure S8 ) . White cells are also induced to undergo filamentation when embedded in soft agar , and this program is dependent on the Czf1 transcription factor [59] . Opaque cells similarly underwent increased filamentation when cultured under embedded conditions ( data not shown ) . Embedded conditions therefore represent an environmental cue that is conducive to inducing filamentation in both white and opaque cells . Interestingly , embedded growth is also one of the few filament-inducing conditions that is effective at 25°C for white cells [59] . As Czf1 promotes white filamentation under embedded conditions , opaque czf1 mutants were analyzed . This required overexpression of WOR1 to generate opaque cells , as czf1 mutants exhibit very low rates of white-to-opaque switching [44] . Opaque czf1 mutants produced an unusual ‘hyper-branching’ phenotype when grown on the surface on several media , including SLAD , LP and SOR medium ( Figure S9 ) . czf1 mutant opaque cells grew as highly branched chains that were clearly distinguishable from all other filamentation phenotypes . Czf1 is a therefore a regulator of filamentation in opaque cells , even when grown under non-embedded conditions . Gene expression of filamentous white cells has been defined during growth in serum medium at 37°C [36] , [60] . We set out to similarly define the transcriptional profile ( s ) of filamentous opaque cells , and to compare these profiles to those of filamentous white cells . Two medium conditions were used to induce opaque cell filamentation: low phosphate ( LP ) medium at 12 hours and sorbitol ( SOR ) medium at 16 hours ( both at 25°C ) , as robust filamentation was observed at these time points . Expression was compared to that in SCD medium ( non-filament inducing ) . We also compared gene expression profiles between white and opaque cells under these culture conditions . First of all , we note that the master transcriptional regulators of the phenotypic switch were differentially expressed between white and opaque phenotypes under these culture conditions . Thus , WOR1 , WOR2 , and CZF1 were expressed at higher levels in opaque cells , while EFG1 was expressed at a higher level in white cells ( data not shown ) . These results establish that cells are exhibiting the classical opaque transcriptional pattern when cultured in LP or SOR medium . Comparison of filamenting and non-filamenting opaque cells revealed that 1188 genes were differentially expressed ( by SAM ) between LP and SCD medium , while 341 genes were differentially regulated between SOR and SCD medium . More specifically , 445 genes were induced and 743 genes repressed ( >3-fold ) in LP medium , while 143 genes were induced and 198 genes repressed in SOR medium . Presumably , these expression changes include many genes that are regulated by the nutritional change , as well as genes that directly mediate the transition from yeast to filamentous growth . Comparison of gene expression profiles revealed that a core set of 48 genes was induced during filamentous growth of opaque cells in both SOR and LP medium ( see Table S2 ) . These genes were compared to those induced during white cell filamentation in serum at 37°C ( Figure 8 ) . In general , overlap between white and opaque filamentation profiles was limited , with most genes specific to one program or the other . Thus , many of the genes characteristic of hyphal formation in white cells , including ALS3 , HYR1 , PHR1 , and SAP5 , were not induced in filamentous opaque cells ( Figure 8B ) . In fact , of the 55 genes induced in white cell hyphae , only 11 were induced in filamentous opaque cells in LP and SOR media . Significantly , one gene that was highly induced during both white and opaque filamentation was the key transcriptional regulator UME6 . This result is consistent with the genetic requirement for this factor for filamentous growth in both white and opaque cells . In general , most hyphal-specific genes in white cells were not induced in filamenting opaque cells , and conversely opaque cells expressed filamentation genes not induced in white cells . In fact , several genes induced during opaque filamentation were repressed in white hyphal cells , including MNN22 , OSM2 , PCK1 , and SAP98 ( Figure 8B ) , while data for a number of opaque filamentation genes was not present in the white expression data set . Several opaque-specific filamentation genes are of interest , including PGA30 and PGA31 that encode putative GPI-anchored cell wall proteins . Upregulation of these genes suggests that filamentous opaque cells could exhibit altered adherence compared to yeast cells . A similar phenomenon has been observed in white cells , where expression of hyphal-specific surface proteins ( e . g . ALS3 , ALS10 , ECE1 , and HWP1 ) mediates increased adhesion of hyphae to host cells and promotes biofilm formation [61] , [62] . We note that HWP1 expression was increased in both white and opaque filamentous cells ( Figure 8B ) and was also induced in opaque cells forming polarized mating projections [16] , consistent with the model that HWP1 expression is directly regulated by actin dynamics during the morphological transition from yeast to polarized growth [63] . Together , these analyses reveal that the gene expression profiles of filamentous white and opaque cells are distinct , with only a limited number of factors induced in both programs of filamentation . However , the induction of UME6 in both gene profiles is consistent with a key role for this transcription factor in regulating filamentation in both white and opaque cell types .
Our results demonstrate that the white-opaque switch plays a key role in regulating the yeast-hyphal transition in C . albicans . Thus , two of the best-studied programs regulating morphogenesis in C . albicans are interconnected , indicating overlap of the regulatory mechanisms involved in these programs . We show that opaque cells can form filamentous cells and do so in response to different environmental cues than those that induce filamentous growth in white cells . Thus , whereas serum , neutral pH , nutrient deprivation and high temperature are signals that induce filamentation in white cells , these stimuli do not induce filamentation in opaque cells ( Figure 1 ) . In contrast , opaque cells undergo filamentation in response to distinct cues , including sorbitol or low phosphate medium that do not induce filamentous growth in white cells ( Figure 2 ) . These results establish that white and opaque states are differentially programmed with respect to the integration of environmental signals for filamentous growth . The regulation of filamentous growth in C . albicans white cells has been the subject of extensive studies ( reviewed in [2] , [5] ) . We examined whether the regulatory pathways controlling white cell filamentation , including MAPK and cAMP pathways , also function to regulate opaque cell filamentation . We show that Efg1 , the terminal transcription factor in the cAMP pathway , and Ume6 , a transcription factor that acts downstream of Efg1 [29] , [53] , [55] , are key regulators of filamentation in opaque cells . Furthermore , overexpression of UME6 was sufficient to induce opaque cell filamentation , as it is in white cells [29] . We also observed roles for the positive regulator Hgc1 and the negative regulators Nrg1-Tup1 in opaque cell filamentation , similar to their established roles in white cell filamentation . Filamentous growth in white cells can be either positively or negatively regulated by Rfg1 depending on the conditions [48] , and we found that rfg1 mutants were defective for filamentation in opaque cells . Thus , this transcription factor also plays a role in regulating filamentation in both white and opaque cells . We also examined several other regulators of white cell filamentation for potential roles in opaque cell filamentation . Cph1 , the master regulator of the MAPK pathway , did not influence opaque cell filamentation and we also observed that this factor has only a subtle role in white cell filamentation in the wildtype strain background . Similarly , Cph2 and Tec1 did not affect opaque filamentation , while deletion of Czf1 resulted in a hyper-branching phenotype specifically in opaque cells at 25°C . Taken together , our findings indicate that white and opaque filamentation occurs in response to different environmental stimuli , and generates different transcriptional responses ( discussed below ) , but that genetic regulation of these programs involves many of the same signaling pathways in both phenotypic states ( see model in Figure 9 ) . Filamentous growth in white cells is often dependent upon elevated temperatures , with several hyphal-inducing conditions requiring a temperature of 37°C for efficient filamentation [2] , [5] . We found that filamentous growth in opaque cells exhibited the opposite dependence on temperature; opaque cells underwent filamentation efficiently at 25°C while filamentation was reduced at 30°C or 37°C . This was not due to opaque cells switching back to the white form at 37°C , as opaque cells were stably maintained by constitutive expression of WOR1 , the master regulator of the opaque state [33]–[35] . In white cells , the molecular chaperone Hsp90 has recently been implicated in thermal regulation of the yeast-hyphal transition , as raised temperatures compromised Hsp90-mediated repression of hyphal formation [8] . In contrast to white cells , we found that inhibition of Hsp90 failed to elicit a large-scale change in morphology , although this could be due , at least in part , to the increased sensitivity of opaque cells to geldanamycin . These results reveal that elevated temperatures and/or decreased Hsp90 activity typically promote hyphal growth in white cells , while lower temperatures promote filamentation in opaque cells . Our findings have direct implications for pathogenicity by white/opaque cells , as they are likely to filament in different niches in response to different environmental cues , as discussed below . While this study revealed a novel program of filamentous growth in opaque cells , it is noted that polarized growth in opaque cells is not a new phenomenon . Opaque cells are the mating-competent form of C . albicans and respond to pheromones by forming mating projections that can be many times the length of the original opaque cell [16] , [17] . Furthermore , opaque mating projections contain a Spitzenkörper-like structure in which the myosin regulatory light chain protein , Mlc1 , localizes to a characteristic ball at the growing tip of the cell [18] , [64] . The fact that opaque cells can form a Spitzenkörper indicates that these cell types undergo highly polarized growth by a mechanism similar to that in truly filamentous fungi . The discovery of environmental conditions that induces opaque filamentation now opens the door for further exploration of the regulation of opaque filamentation . Gene expression profiles of filamenting white and opaque cells were distinct , with only 11 genes induced in both programs , while a further 44 genes were induced specifically in white hyphal cells and 37 genes induced only in opaque filamentous cells ( Figure 8 ) . One gene that was highly induced in both white and opaque states was the transcription factor UME6 . As discussed above , UME6 is a critical regulator of filamentous growth in opaque cells , similar to its established role in white cells , and expression profiling confirms that this gene is co-regulated by morphogenesis in both cell types . Interestingly , comparison of white-opaque regulated genes reveals that several regulators of filamentation are differentially expressed between white and opaque cells . For example , UME6 , HGC1 , NRG1 , and EFG1 , are white-opaque regulated genes; the first two genes are expressed at significantly higher levels in opaque cells , while the latter two genes show elevated expression in white cells ( see Figure S10 ) . EFG1 is itself a master regulator both of the white-opaque phenotypic switch and of filamentous growth [3] , [43] , [44] . It is therefore apparent that the transcriptional regulation of white/opaque phenotypes and that of filamentous growth are highly integrated , as previously suggested [65] . Presumably , the differential expression of these key regulators is at least partially responsible for the different propensities of white and opaque cells to filament in response to different environmental cues . The yeast-hyphal transition is critical for infection by C . albicans white cells , where hyphae are more adherent and invasive than yeast-form cells . Despite this , questions remain as to the exact role of the hyphal structure during infection , and whether it is the genes that are co-regulated with the morphological switch that are critical for virulence or the hyphal structure itself [2] . In contrast , little is known about the potential for opaque cell filaments to promote pathogenesis . Early studies indicated that opaque cells are more effective at causing skin infections than white cells , and that opaque filamentation was induced on human skin epithelium [11] , [12] , [15] . Mating between opaque cells has also been shown to occur in this niche , indicating that the skin may represent a natural site for opaque colonization [66] . It is also possible that opaque filamentation occurs in other environmental niches , including those not associated with colonization and infection of the mammalian host . Future studies will examine both in vitro and in vivo conditions to determine those capable of inducing the program of filamentation in opaque cells . It is therefore an open question as to the role of the filamentous program in C . albicans opaque cells in infection and disease , and whether this program parallels that in white cells in promoting tissue destruction and pathogenesis .
|
Candida albicans is the most common human fungal pathogen , capable of growing as a commensal organism or as an opportunistic pathogen . Perhaps the best-studied aspect of C . albicans biology is the transition between the single-celled yeast form and the multicellular filamentous form . This transition is necessary for virulence , as cells locked in either state are avirulent . Here , we demonstrate that the yeast-filament transition is tightly regulated by another morphological switch , the white-opaque phenotypic switch . White cells undergo filamentation in response to a wide range of established physiological cues , while opaque cells do not . We further show that opaque cells can indeed undergo filamentation , but that they do so in response to different environmental cues than those of white cells . We define the genetic regulation of filamentous growth in opaque cells , as well as the transcriptional profile of these cell types , and contrast them with the established program of filamentation in white cells . Our results reveal a close relationship between the white-opaque switch and the yeast-hyphal transition , and provide further evidence of the morphological plasticity of this pathogen . They also establish that epigenetic switching allows two fungal cell types with identical genomes to respond differently to environmental cues .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"genome",
"expression",
"analysis",
"infectious",
"diseases",
"gene",
"expression",
"genetics",
"microbial",
"pathogens",
"molecular",
"genetics",
"biology",
"genomics",
"microbiology",
"host-pathogen",
"interaction",
"fungal",
"diseases",
"genetics",
"and",
"genomics",
"pathogenesis"
] |
2013
|
Candida albicans White and Opaque Cells Undergo Distinct Programs of Filamentous Growth
|
For the most part metazoan genomes are highly methylated and harbor only small regions with low or absent methylation . In contrast , partially methylated domains ( PMDs ) , recently discovered in a variety of cell lines and tissues , do not fit this paradigm as they show partial methylation for large portions ( 20%–40% ) of the genome . While in PMDs methylation levels are reduced on average , we found that at single CpG resolution , they show extensive variability along the genome outside of CpG islands and DNase I hypersensitive sites ( DHS ) . Methylation levels range from 0% to 100% in a roughly uniform fashion with only little similarity between neighboring CpGs . A comparison of various PMD-containing methylomes showed that these seemingly disordered states of methylation are strongly conserved across cell types for virtually every PMD . Comparative sequence analysis suggests that DNA sequence is a major determinant of these methylation states . This is further substantiated by a purely sequence based model which can predict 31% ( R2 ) of the variation in methylation . The model revealed CpG density as the main driving feature promoting methylation , opposite to what has been shown for CpG islands , followed by various dinucleotides immediately flanking the CpG and a minor contribution from sequence preferences reflecting nucleosome positioning . Taken together we provide a reinterpretation for the nucleotide-specific methylation levels observed in PMDs , demonstrate their conservation across tissues and suggest that they are mainly determined by specific DNA sequence features .
DNA methylation in metazoan genomes occurs mainly in the context of CpG dinucleotides and for the most part genomes are highly methylated and harbor only small regions with low or absent methylation [1] . These include CpG islands [1] , CpG island shores [2] and distal regulatory regions [3] , [4] . Partially methylated domains ( PMDs ) depart from this notion . They were initially discovered through whole-genome bisulfite sequencing in human fibroblasts [5] and correspond to large genomic domains ( mean = 153 kb ) with average methylation levels of less than 70% , covering almost 40% of the genome . Interestingly these domains were not detected in H1 human embryonic stem cells . The PMDs in IMR90 were shown to harbor genes with reduced expression , to correlate with repressive and to anti-correlate with active histone marks . In a subsequent study PMDs were detected in three additional cell types , namely foreskin fibroblasts ( FF ) , adipose-derived stem cells ( ADS ) and adipocytes ( ADS-adipose ) [6] . While every cell type showed specific patterns of PMD localization , overall a high fraction of PMDs co-localized in all cell types . On induction of pluripotency , the presence of PMDs was strongly reduced in all the cell types examined . In later studies , PMDs were found in SH-SY5Y neuronal cells [7] , human mammary epithelial cells [8] as well as colorectal cancer samples where they were shown to coincide with late replicating and nuclear lamina associated regions [9] . Very recently and importantly , human placenta was shown to contain PMDs , representing the first known uncultured and non-cancer tissue type with PMDs [10] . Understanding methylation levels in PMDs is of great interest as they represent a widespread methylation pattern that is distinct from the reduced methylation state of CpG islands [1] and at DHS [3] , [4] , [11] , which are indicative of transcription factor binding [11] , or the fully-methylated state in most of the genome . Additionally , PMDs may be directly linked to hypomethylation that is frequently observed in cancer [8] , [12] , [13] . Studies on PMDs so far focused mainly on their macroscopic properties , their genomic locations and conditions in which they are formed or abolished . In this study we set our main focus on the microscopic aspects of PMDs , investigating how this phenomenon manifests at the level of single nucleotides . We show that single-CpG methylation levels in PMDs , in contrast to the remainder of the genome , are roughly uniformly distributed , spanning the entire range from 0 to 100 percent methylation , and display a seemingly disordered pattern along the genome . Surprisingly , these patterns are conserved across PMD-containing cell types and sequence analysis suggests that the DNA sequence itself is a major determinant of their methylation states . A position-specific dinucleotide model reveals CpG density and various dinucleotides immediately flanking the CpG as the main drivers of methylation levels in PMDs . Importantly these sequence features are specific to PMDs as they contribute only little to the methylation levels in the remainder of the genome .
So far , the main feature that has been used to characterize and localize PMDs is reduced mean methylation calculated over large windows containing many CpGs [5] , [6] , [7] , [9] . To investigate the phenomenon on the single nucleotide level , we visualized the methylation status of single CpGs in one genomic region covering 10% of chromosome 10 ( Figure 1a ) . In human embryonic stem cells ( H1 ) , high methylation is predominant with a relatively small number of hypomethylated regions . In IMR90 fibroblasts however , the situation is dramatically different . There exist distinct domains with clear boundaries that display aberrant methylation levels . On average these domains exhibit decreased ( partial ) methylation levels . Importantly however this decrease in average methylation is not a result of a constant overall reduction at all CpGs . It is mainly attributed to an extensive increase in the variability in methylation along the genome . Single CpG methylation levels in PMDs span the full range from 0% to 100% in a roughly uniform fashion ( Figure 1a ) . Due to this high variability PMDs appear as domains of highly disordered methylation . However this is not a reflection of a random process at the single cell level . The methylation level for a given CpG refers to the percentage of alleles carrying the methyl group in a population of cells , thus a randomly established methylation state at the single cell level would average out to a methylation level of 50% for each CpG . This is not in accordance with the data and therefore strongly argues that a mechanism must exist which acts in individual cells and determines the likelihood of methylation . In particular this likelihood needs to be CpG-dependent . It is well known that DNA methylation is generally reduced at CpG islands [1] . More recently this has also been shown to be the case for DHS outside of CpG islands [3] , [4] . To investigate to what extent the variability of methylation levels in PMDs in IMR90 can be attributed to the presence of these regions , we determined the genome-wide distribution of methylation levels for CpGs within CpG islands , DHS and the remaining ones within PMDs . As a contrast , we performed the same analysis for CpGs outside of PMDs , which display the typical well-known patterns of mammalian methylomes ( Figure 1a ) . The majority of CpG islands and DHS in PMDs show reduced methylation , similar to what is observed outside of PMDs ( Figure 1b and c ) . The remaining CpGs , which constitute the large majority of CpGs in PMDs , however , display a roughly uniform distribution in methylation levels . This is in stark contrast to the situation outside of PMDs , where the large majority of CpGs outside of islands and DHS are fully methylated . It thus appears that unlike outside of PMDs , most of the variability in methylation within PMDs cannot be attributed to the presence of CpG islands nor DHS . For all the subsequent analyses , we therefore considered only the CpGs outside of CpG islands and , if available , outside of DHS . PMDs have been shown to be located predominantly in heterochromatic , gene-poor regions [5] , which are rich in repeat elements . To investigate the relationship between the presence of particular repeat elements and methylation levels in PMDs , we grouped CpGs according to repeat annotation and monitored their distribution of methylation levels ( Figure 1d ) . In contrast to repeat elements outside of PMDs , which are generally fully methylated , repeats inside PMDs do not maintain the fully-methylated state and show markedly increased variability , comparable to non-repeat regions . SVAs ( composite unit of SINE , VNTR and Alu [14] ) , which make up only a small fraction of the genome , constitute an exception as they partially maintain their fully methylated state . SVAs are known to be active and are generally methylated [15] , [16] , but the functional significance of this methylation is still unclear[17] . We conclude that the variability of methylation levels in PMDs cannot simply be explained by the preferential methylation levels of particular repeat types and their distribution along the genome . To gain more insight into methylation levels in PMDs , we compared single CpG methylation levels in PMDs across different PMD-containing tissues . To identify PMDs across methylomes , we previously developed an algorithm which makes direct use of the uniformity of methylation levels in PMDs , a functionality which we provide as part of the MethylSeekR R package [18] . We identified high-confidence ( see Materials and Methods ) PMDs in IMR90 ( covering 1 . 32 Gb ) and separately in FF ( 1 . 36 Gb ) and selected PMDs common to both cell types ( 1 . 22 Gb ) . A comparison of methylation levels along the genome revealed a strong agreement between the two cell types at the single CpG level ( Figure 2a ) . To globally assess this trend , we quantified , for every PMD , the similarity between single CpG methylation levels in IMR90 and FF using the pearson correlation coefficient . Strikingly , methylation levels are conserved across the two cell types with correlation coefficients ranging from 0 . 7 to 0 . 9 ( Figure 2b ) . This high similarity between the methylation at individual CpGs within PMDs is not confined to fibroblast tissues . A comparison of IMR90 to human mammary epithelial cells ( HMEC , 1 . 20 Gb of PMDs , 1 . 03 Gb in common with IMR90 ) [8] revealed high correlations ranging from 0 . 5 to 0 . 7 ( Figure 2c ) . Interestingly , the same holds true in a comparison of IMR90 to colorectal cancer cells ( 1 . 39 Gb of PMDs , 1 . 06 Gb in common with IMR90 ) , which were shown to contain PMDs [9] ( Figure 2c ) . It is important to note that in these cancer cells , it appears that CpG islands within PMDs tend to have increased rather than reduced methylation as in healthy tissue [9] . This , however , does not influence our analysis as we exclude CpGs overlapping CpG islands from our analysis . The surprising similarity of methylation levels between PMD-containing cell-types substantiates the requirement for a mechanism that accounts for the specificity of methylation at the single CpG level . Since methylation is binary on an allele level , the process that sets the methylation mark must be stochastic in nature . For example for a CpG with a methylation level of 20% in the population , the probability of obtaining a methyl mark for a single allele must be 0 . 2 . Where does the information come from that is required by each cell to determine this probability for every single CpG within a PMD ? One likely candidate is the underlying DNA sequence or its chromatin state . From a bird's eye view as illustrated in Figure 1a , it seems that there is no dependence of methylation levels between consecutive CpGs . However this might be solely due to the fact that CpGs are scarce and irregularly spaced . In order to account for this , we calculated correlations for consecutive CpGs in a distance–dependent manner ( Figure 2d ) . This analysis revealed that CpGs in very close proximity ( <15 nt ) correlate very well ( r = 0 . 8 ) but that this correlation deteriorates rapidly with distance . CpGs at the average spacing of 109 bp show only little spatial coupling ( r = 0 . 23 ) . Furthermore we identified a weaker signal with a periodicity of 10 bp , which is likely related to the turn of the DNA helix wrapped around the nucleosome [19] . Finally we detected a local maximum at about 180 bp [20] corresponding to typical distances between nucleosomes . Taken together this suggests that nucleosomes might be involved in the process that determines methylation levels for individual CpGs . It is known that positioning of nucleosomes is at least partially dependent on the underlying DNA sequence [21] . We therefore investigated if DNA sequence or nucleosome positioning can explain the methylation levels of single CpGs . To ask if DNA sequence has the potential to predict methylation levels of single CpGs in PMDs of fibroblasts , we performed two types of analyses , one based on a comparative sequence approach and one based on a nucleotide model . Firstly we addressed the question whether methylation levels tend to be conserved in CpGs with identical surrounding sequence . To do so , for each CpG in a PMD , we extracted the surrounding sequence ( centered around the CpG ) and grouped all the CpGs based on that sequence . This allowed us to calculate correlation coefficients from pairs of CpGs with identical flanking sequences . We performed this procedure for various sequence lengths ranging from 10 bp to a maximum of 140 bp . Longer sequences could not be studied due to the limited read lengths of 87 bp provided in the methylome . At a sequence context of 140 bp ( with the CpG in the center ) , at most 17 bp remain to uniquely map the bisulfite read as it also needs to overlap the central CpG to quantify the methylation level . The analysis revealed that with increasing sequence context , correlation of methylation increases dramatically from r = 0 . 28 at 10 bp to r = 0 . 86 at 140 bp . ( Figure 3a ) . This suggests that the DNA sequence plays a key role in determining exact methylation levels in PMDs . One limit of this analytic approach is that with increasing context length , preferentially those pairs of CpGs that reside within repetitive parts of the genome remain for quantification . These regions might be atypical and thus introduce a bias . To investigate this issue , we partitioned the CpG pairs according to their UCSC repeat annotation . To retain a sufficient number of pairs per category , we selected the flanking sequences of length 80 bp , which corresponds to 12K pairs of CpGs and a correlation of 0 . 69 ( Figure 3a ) . Whereas , as expected , the large majority of CpG pairs overlap with repeat elements , there still remain 16 . 4% of CpG pairs that lie outside of repeats which we analyzed separately . This revealed that even within each annotation class , similar sequences exhibit similar methylation levels . In particular , there is no substantial difference between repeat and non-repeat sequences ( Figure 3b ) . To characterize the role of sequence features beyond identical sequence contexts , we created a purely sequence based DNA methylation predictor which can be applied to all the CpGs within PMDs . To this end , we generated a position specific linear dinucleotide model ( see Materials and Methods ) and trained it on 100K ( out of 6 . 1M ) single CpG methylation data points . The accuracy of the model on an independent set of 100K data points was 31% , corresponding to a correlation of r = 0 . 56 between the predicted and the actual methylation data ( Figure 4a ) . While this is less striking than the correlation of r = 0 . 8 between IMR90 and FF , it still provides additional evidence that DNA sequence most likely provides the information required to set the methylation status of a given CpG . This signal is PMD-dependent , as performing the same analysis in H1 which contains virtually no PMDs results only in a correlation of r = 0 . 20 . The model furthermore allows us to infer the relative importance of the various sequence features ( Figure 4b ) . The most informative feature is CpG content +−20 bp around the central CpG . This means that the methylation status of a CpG depends on the presence of other CpGs in its environment . The two CpGs immediately surrounding the central CpG have an exceptionally high impact on methylation . Outside of the +−20 bp band the impact of CpGs decreases substantially . The level of methylation generally rises with increasing numbers of CpGs . Interestingly this is the opposite of what occurs in CpG islands where high CpG density coincides with strongly reduced methylation levels [1] . Furthermore , various dinucleotides immediately flanking the CpG show positive as well as negative contributions . While TT and AA negatively influence the methylation levels on both sides , TG , CT , CA and AG have opposite contributions on either side of the CpG . Finally , various dinucleotides show a more subtle but clearly periodic signal of 10 bp reminiscent of the DNA helix turn . This again suggests that the nucleosome might play a role in determining methylation levels in PMDs . From this analysis it is however unclear to what extent methylation levels can be explained by positioned nucleosomes . To investigate whether similar sequence preferences exist in other PMD-containing tissues , we trained our dinucleotide model on HMEC and colorectal cancer cells ( Figure 4c ) . This revealed consistent sequence features across all PMD-containing methylomes . Additionally , applying the model to the CpGs outside of PMDs showed that these sequence features are PMD-specific . As a control , we trained the model on H1 using the IMR90 PMD annotation ( as the tissue itself does not contain PMDs ) . This analysis did not reveal any clear sequence preferences ( Figure 4c ) . In accordance with these findings , the respective models can predict methylation levels within PMDs with good accuracy , but fail to do so outside of PMDs and in methylomes without PMDs ( Figure 4d ) . We conclude that the high variability of methylation levels in PMDs are , to a significant extent , determined by specific DNA sequence features that are conserved across diverse cell types . Allele-specific analysis provides a powerful tool to validate our inferred dinucleotide features . The dinucleotide model suggested that the change of a particular dinucleotide in the vicinity of a CpG can influence its methylation level . Heterozygous SNPs close to a homozygous CpG should therefore result in differences in methylation between the CpGs of the two alleles . Bisulfite reads which overlap both the SNP and the CpG in the vicinity can be uniquely assigned to one allele . This does not only allow us to calculate methylation levels for the CpG on both alleles separately , but also to compare the difference in methylation to the nucleotide variation at the heterozygous SNP ( see Material and Methods ) . Starting from a total set of 647K heterozygous SNPs in IMR90 , we found 56K CpGs in PMDs which were present on both alleles , had exactly one single SNP within a window of +−40 bp ( see Materials and Methods ) and showed a coverage of at least 10 bisulfite reads for both alleles . From this dataset we inferred the contribution of each dinucleotide , at each distance , to the measured methylation difference between the two alleles ( see Materials and Methods ) . Figure 4e displays the results in the same fashion as the previous non-allele specific analysis ( Figure 4b ) . Side by side comparison shows that the sequence features obtained from the two different analytical approaches are highly similar , in particular the central role of the CpG density and the various dinucleotides immediately flanking the central CpG . The periodic 10 bp signal is not as clear as before but this is likely due to the limited number of data points available for allele-specific analysis . In order to relate methylation levels to positioned nucleosomes , we created a high coverage nucleosome map in IMR90 by Micrococcal nuclease ( MNase ) treatment and sequencing of mononucleosomal DNA . We obtained a total of 517 million uniquely mapping reads , which is comparable to the amount generated in previous high coverage nucleosome studies [20] . To determine if nucleosome positioning shows methylation dependent patterns in PMDs , we stratified the CpGs according to their methylation levels into 5 equally spaced bins and created composite MNase profiles for each individual bin ( Figure 5 ) . The profiles show that methylated CpGs within PMDs overlap less frequently with positioned nucleosomes ( 180 bp periodicity ) and tend to lie on a single side of the double helix ( 10 bp periodicity ) . Others have previously analyzed the relationship of methylation and nucleosome positioning outside of PMDs and have found either decreased [22] or increased methylation at positioned nucleosomes [19] . Although our data gives support to the former finding , Figure 5 also shows that the MNase enrichments ( compared to the average over all CpGs ) are very small ( 1 . 2 fold ) . This suggests that nucleosome positioning may have only little predictive power . To test this we performed a linear regression using positional MNase read counts ( centered around the CpGs ) as predictors and methylation levels as the response variable . This resulted in a correlation of only r = 0 . 15 , substantially less than the performance of the nucleotide model ( r = 0 . 56 ) . The correlation of 0 . 15 is an upper bound to the potential explanatory power of the MNase signal and might even be substantially lower due to possible confounding factors such as the cutting preferences of the MNase enzyme or sequencing biases [23] . We thus conclude that nucleosome positioning plays only a minor role in determining the exact methylation levels in PMDs . Sequence features , in particular the local CpG density provides substantially more information about the methylation status .
PMDs have been characterized as long domains ( mean = 153 kb ) with decreased average methylation [5] . In this study , we showed that in IMR90 , this overall decrease is accompanied by a strong increase in the variability along the genome , with methylation levels ranging from 0% to 100% in a roughly uniform fashion . These seemingly disordered methylation patterns however are conserved across cell types at the single CpG level suggesting a mechanism that provides high local specificity . Using comparative sequence analysis as well as a positional dinucleotide model , we showed that outside of CpG islands and DHS specific DNA sequence features contribute to the methylation levels observed in PMDs . These include CpG density as the main driving feature followed by various dinucleotides immediately flanking the CpG , as well as a minor contribution from sequence preferences reminiscent of the sequence preference of positioned nucleosomes . CpG density , in particular , showed the strongest contribution in a +−20 bp band , with an exceptionally high impact of the two CpGs immediately surrounding the central CpG . This may suggest cooperative behavior on two distinct levels . In the case of two adjacent CpGs , the DNA methyltransferase ( DNMT ) depositing the methylation mark on the first CpG might directly methylate the neighboring CpG whereas in the case of a wider distance , cooperativity might be caused by a DNMT sliding along the DNA , or the recruitment of an a additional DNMT depositing the second mark . Importantly , CpG density is positively correlated with methylation levels and thus promotes methylation . An early indication of this finding , without a link to PMDs , has been given by [24] and is , interestingly , the opposite to what has been shown for CpG islands [25] . We additionally investigated the role of nucleosome positioning by creating a high coverage map in IMR90 . While we provide evidence for a slight decrease in methylation at positioned nucleosomes , we concluded that nucleosome positioning plays only a minor role in determining the exact methylation levels in PMDs . Sequence features , in particular the local CpG density provided substantially more information about the methylation status . In embryonic stem cells , the methylation machinery maintains a fully methylated state in most of the genome . In fibroblasts however , large domains covering 35% of the genome show reduced levels of methylation ( PMDs ) . This suggests that the DNMTs are impaired in their capacity of maintaining the fully methylated state at those domains . Interestingly this loss in methylation , on the level of single CpGs , reveals a methylation pattern that is not apparent in all cell types . We speculate that this could be caused by a reduced access of the methylation machinery in PMDs . As this would lead to a reduced effective concentration , there might be a critical point where the concentration of the DNA methylation machinery becomes rate limiting for the process of methylation . In this case , the intrinsic sequence preferences might surface whereas in a normal configuration , these patterns would be overwritten and converted into a fully methylated state . Our dinucleotide model provides indirect evidence for this hypothesis , as it shows that the methylation status of a CpG in PMDs depends on the presence of other CpGs in the vicinity . This cooperative aspect of the methylation machinery would result in increased methylation levels in genomic regions with higher CpG density . A recent experimental study in a mouse lung-cancer model [26] suggests that the de novo methyltransferase Dnmt3a may be an important player in this process as it is required to maintain the fully-methylated state in active regions . It could thus be hypothesized that it is the reduced access of Dnmt3a to PMDs which leads to the reduction of methylation . The relationship between methylation and DNA sequence features has been extensively investigated in the past . These studies were either performed on limited sets of CpGs for which methylation measurements were available [27] , [28] or with a particular focus on CpG islands . The latter methods were initially based on sequence alone [25] , [29] and were later augmented with epigenetic data [30] , [31] , [32] . The analysis presented here departs from these earlier studies by focusing on the large majority of CpGs that lie outside of CpG islands and by explaining the previously unknown and unexplained extensive variability in methylation levels within PMDs . The relationship between methylation levels outside of CpG islands and specific sequence features irrespective of PMDs has been investigated in a recent study about allele-specific methylation in mouse [33] . The authors identified sequence motifs enriched in the immediate vicinity ( +−2 bp ) of CpGs methylated in an allele-specific fashion . Although the reported sequence features are comparable to our findings , they differ in the extent of the sequence environment ( +−2 bp compared to +−20 bp ) and thus differently assess the contribution of the CpG dinucleotide in the wider sequence context . It is however intriguing that when projecting the inferred motifs to various human methylomes , they see highest agreement in the tissues containing PMDs . This supports our finding that sequence features are much more predictive of methylation levels inside versus outside of PMDs . Due to the ever-decreasing costs in sequencing , researchers can now generate large numbers of single-base methylomes in a variety of conditions . PMDs occur in only a subset of conditions but if present can cover up to 40% of the genome . This will inevitably lead to a large number of differentially methylated CpGs . Here we provide a sequence-based explanation for the methylation differences in PMDs and suggest that these should be treated separately from the changes outside of PMDs . This study adds to the growing body of literature that aims at disentangling the wealth of information contained in base-pair resolution methylation maps and should thus be of great importance for the interpretation of the large number of methylomes that will be generated in the nearby future .
Unless stated differently , all methylomes in this study have been processed as follows: Methylaton levels from both strands of a given CpG were combined . CpGs in CpG islands ( www . genome . ucsc . edu ) were removed after extending the CpG islands by 100 bp . CpGs overlapping DHS were removed as well for the cell types H1 and IMR90 for which DHS datasets were available . Only autosomal CpGs with a minimum coverage of 10 were considered . All CpGs overlapping a SNP from dbSNP ( 09 . 11 . 10 ) were removed to ensure that the methylation levels are not cofounded by polymorphisms . We used the software MethylSeekR [18] to detect PMDs in the various methylomes and created a high confidence set by considering only the PMDs with a length of least 200 kb . CpGs outside PMDs were defined as the CpGs , which did not overlap any PMD . For Figure 1 , PMD coordinates from the original publication [5] were used to avoid circular inference of the uniform distribution of methylation levels in PMDs . MethylSeekR makes explicit use of the distribution of methylation levels while in the original publication PMDs were only defined by a reduction in mean methylation . Classification of the CpGs into repeat classes was done based on UCSC ( genome . uscsc . edu ) repeat annotation using the repClass column in the rmsk table . All analyses throughout the manuscript were performed in R ( www . r-project . org ) using core packages from bioconductor [34] . To predict methylation levels from DNA sequence , we created a linear regression using dinucleotide features as predictors and the measured methylation level as the response . For each CpG in consideration , the sequence environment ( +−78 bp ) was extracted and split into non-overlapping blocks of dinucleotides . Each dinucleotide was interpreted as a categorical variable with 16 states ( 15 dummy variables in the regression ) . Thus in total the regression contained 78*15 = 1170 variables . For visualization as a heatmap , the left out dinucleotide was introduced back ( as a zero ) into the list of coefficients . These were then normalized to mean = 0 . Training and testing was performed on separate sets of 100K data points selected randomly . To create an allele specific methylation map for the IMR90 cell lines , we downloaded the mapped reads , a list of SNPs in IMR90 ( see datasets ) and realigned the reads using the bioconductor package QuasR ( www . bioconductor . org/packages/2 . 12/bioc/html/QuasR . html ) . From the list of SNPs , two separate genomes were produced ( Reference and Alternative ) . After C to T conversion of the reads and the genomes , alignments were performed using bowtie [35] . Only unique mappers with at most three mismatches were considered . The two separate alignments were combined into one , by flagging reads mapping equally well to both genomes as “Unknown” and reads mapping better to one genome than to the other as “Reference” or “Alternative” . Using this flag , methylation levels for CpGs were calculated separately for the Reference and the Alternative allele . Only CpGs covered by at least 10 bisulfite reads in both alleles were considered for further analysis . From a total set of 647K heterozygous SNPs in IMR90 , we found 56K CpGs in PMDs which were present on both alleles and had exactly one single SNP within a window of +−40 bp . From this dataset , we inferred the contribution of each dinucleotide to the difference in the methylation between the alternative allele and the reference allele ( dm = m_A-m_R ) . Since each CpG contains only one SNP in its environment , inference can be performed for each position independently . However since a change in sequence involves two dinucleotides ( one on each allele ) , the values for delta methylation dm need to be interpreted as the change in methylation caused by converting a particular dinucleotide to another one . This relationship is described by the equation dm = s_d ( A ) –s_d ( R ) , where s_d ( A ) represents the contribution of the dinucleotide present in the alternative genome and s_d ( R ) represents the contribution for the dinucleotide in the reference genome . Since there are multiple data points , this leads to a linear regression ( no constant term ) with 16 variables and as many equations as there are SNPs at a particular position ( between 538 and 1942 in this dataset ) . This regression however is singular as an arbitrary constant can be added to the coefficients without having any effect on the quality of the fit . We thus set the dinucleotide TT arbitrarily to zero and used it as a reference . Thus the final regression consisted of 15 variables and was performed independently for each position . For visualization as a heatmap , the coefficients ( including a value of zero for TT ) were mean normalized . Chromatin isolation from IMR90 was performed under native conditions as described [36] . MNase treatment was performed with 5 U ( Roche Nuclease S7 , catalog number 10107921001 ) at 37°C for 30 min per 1 million cells . Digestion was tested on 2% agarose gel resulting in the highest proportion of mononucleosomal fraction . Mononucleosomal fraction was then extracted from the agarose gel using QIAquick Gel Extraction Kit ( Qiagen ) . Library preparation was performed using Illumina Genomic DNA Sample Preparation Guide starting with 2 µg of mononucleosomal fraction DNA . Single End Genomic Adapter Oligo Mix was used and the library was amplified for 10 cycles using Illumina PCR primers 1 . 1 and 2 . 1 . Final PCR product was purified using Agencourt AMPure XP beads ( Beckman Coulter ) . Quality of the libraries and template size distribution were assessed by running an aliquot of the library on an Agilent 2100 Bioanalyzer ( Agilent Technologies ) . A total of 697M single-end reads were mapped to hg18 using bowtie [35] . 517M uniquely mapping reads were considered for further analysis . Given the footprint of 147 bp of the nucleosome , the reads were shifted by +/−74 bp dependent on the strand . Reads starting exactly within the interrogated CpGs ( before shifting ) were not considered as we observed strong discontinuities of the MNase signal when creating average profiles with respect to a central CpG . We assumed that this is caused by sequence specific biases of the MNase treatment and thus decided to remove the critical alignments . The raw data as well as processed files were submitted to GEO ( www . ncbi . nlm . gov/geo ) and can be retrieved using the accession GSE44985 . Methylomes for H1 , IMR90 , IMR90-iPSC and FF as well as the mapped reads from IMR90 ( used for the allele specific analysis ) were downloaded from http://neomorph . salk . edu/ips_methylomes/data . html . The Colorectal cancer methylome was downloaded from http://epigenome . usc . edu/publicationdata/berman20101101/ . IMR90 SNPs were downloaded from http://www . genboree . org/EdaccData/SBS-SNPs/IMR90 . Methyl-C . Hg18 . SNPs/ . The HMEC methylome was downloaded from GEO ( accession number GSM721195 ) . The DNase I hypersensitive sites for IMR90 and H1 were downloaded from the ENCODE Consortium data repository at genome . ucsc . edu/ENCODE ( narrowPeak lists ) . All datasets were provided in coordinates for the hg18 human genome assembly .
|
Methylation is an essential DNA modification , which is attracting a lot of attention as a regulator of gene expression . Recent technological advances have allowed the genome-wide measurement of methylation at single-nucleotide resolution , leading to the discovery of several new types of methylation patterns . One prominent example are partially methylated domains ( PMDs ) , which are regions with reduced average methylation , covering up to 40% of the genome . PMDs are found in only a subset of cell types , particularly in differentiated and cancer cells . An outstanding question is how methylation levels in PMDs are determined and how they can be interpreted at the single-nucleotide level . Here we provide a new model of methylation in PMDs . Single-nucleotide methylation levels in PMDs , albeit reduced on average , are highly variable along the genome . Furthermore , they are precisely set and can be predicted using DNA sequence features , establishing a new link between methylation and the underlying genetic information . This results in a high correlation of methylation levels in PMDs across different cell types . Our findings suggest that in any comparative analyses , PMDs should be analyzed as entities , strongly reducing the complexity of high-resolution DNA methylation analyses .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"sequence",
"analysis",
"genome",
"complexity",
"genomics",
"dna",
"modification",
"genetics",
"epigenetics",
"biology",
"epigenomics",
"computational",
"biology",
"chromatin"
] |
2014
|
DNA Sequence Explains Seemingly Disordered Methylation Levels in Partially Methylated Domains of Mammalian Genomes
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An inherited polyneuropathy ( PN ) observed in Leonberger dogs has clinical similarities to a genetically heterogeneous group of peripheral neuropathies termed Charcot-Marie-Tooth ( CMT ) disease in humans . The Leonberger disorder is a severe , juvenile-onset , chronic , progressive , and mixed PN , characterized by exercise intolerance , gait abnormalities and muscle atrophy of the pelvic limbs , as well as inspiratory stridor and dyspnea . We mapped a PN locus in Leonbergers to a 250 kb region on canine chromosome 16 ( Praw = 1 . 16×10−10 , Pgenome , corrected = 0 . 006 ) utilizing a high-density SNP array . Within this interval is the ARHGEF10 gene , a member of the rho family of GTPases known to be involved in neuronal growth and axonal migration , and implicated in human hypomyelination . ARHGEF10 sequencing identified a 10 bp deletion in affected dogs that removes four nucleotides from the 3′-end of exon 17 and six nucleotides from the 5′-end of intron 17 ( c . 1955_1958+6delCACGGTGAGC ) . This eliminates the 3′-splice junction of exon 17 , creates an alternate splice site immediately downstream in which the processed mRNA contains a frame shift , and generates a premature stop codon predicted to truncate approximately 50% of the protein . Homozygosity for the deletion was highly associated with the severe juvenile-onset PN phenotype in both Leonberger and Saint Bernard dogs . The overall clinical picture of PN in these breeds , and the effects of sex and heterozygosity of the ARHGEF10 deletion , are less clear due to the likely presence of other forms of PN with variable ages of onset and severity of clinical signs . This is the first documented severe polyneuropathy associated with a mutation in ARHGEF10 in any species .
Charcot-Marie-Tooth ( CMT ) disease is a heterogeneous mix of hereditary motor and sensory neuropathies in humans , for which mutations in more than thirty genes have already been described ( Inherited Peripheral Neuropathies Mutation Database ) . CMT disease is the most common inherited disorder of the peripheral nervous system in humans , affecting an estimated 8 to 41 per 100 , 000 globally [1] . Classification of CMT cases has historically been based on clinical and electrophysiological findings , histopathology , and the pattern of inheritance , but is now increasingly based on the underlying genetic defect [2] . Unfortunately , the molecular basis of many rare CMT forms remains unsolved . With no curative therapy for human patients with CMT , suitable animal models for development of therapeutic agents and other treatment modalities are highly desirable [3] . Many of the described muscle and peripheral nerve diseases in dogs are suspected to be inherited [4] , and it has been suggested that many canine inherited neuropathies strongly resemble forms of CMT disease [5] , [6] . The recent identification of mutations in the NDRG1 ( ENSCAFG00000001141 ) gene in Greyhounds [7] and Alaskan Malamutes [8] with early-onset polyneuropathy supports this hypothesis . An inherited polyneuropathy ( PN ) in Leonberger dogs [9] also demonstrates striking similarities to an intermediate form of CMT disease . Leonbergers are a large-bodied breed , reaching adult weights of 45 to 77 kilograms . PN in this breed is characterized by generalized weakness , hypotonia , and muscle atrophy secondary to denervation , particularly of the pelvic limbs [9] . Affected dogs frequently present with a high-stepping pelvic limb gait ( pseudo-hypermetria of the hock ) [6] , decreased or absent tendon reflexes , and changes associated with degeneration of the recurrent laryngeal nerve , including inspiratory stridor resulting from laryngeal paralysis . The age-of-onset of clinical signs can vary from <1 year up to 11 years of age; however , the juvenile-onset patients typically have a more severe and rapidly progressing course of disease . Peroneal nerve biopsies show decreased myelinated fiber density resulting from axonal degeneration and endoneurial fibrosis indicative of chronic nerve fiber loss . Cranial tibial muscle biopsies demonstrate neurogenic atrophy and fatty replacement of muscle fibers indicative of chronic denervation [9] . There is no effective direct therapy for these pathologies , although some relief can be achieved for laryngeal problems by arytenoid cartilage lateralization ( called a “tieback” surgery ) . A predominance of affected males led to the initial suspicion that PN in Leonbergers was an X-linked disorder , although autosomal recessive inheritance could not be ruled out [9] . A more recent multi-generation pedigree analysis also concluded an X-linked mode of inheritance , with possible influence by other loci that impact age-of-onset and severity of signs [10] . Nevertheless , numerous affected Leonberger females with juvenile-onset PN , coupled with increasing recognition of a wide range in ages of onset and clinical severity of PN , indicate that the underlying genetic mechanisms responsible for PN in this breed remain to be defined . We therefore performed a genome-wide association study ( GWAS ) with high-density canine SNP arrays in cohorts of Leonberger PN cases and controls , with the aim of identifying genetic defect ( s ) that contribute to PN . A loss-of-function ARHGEF10 ( ENSCAFG00000013667 ) deletion was identified which , although not explanatory for all Leonberger polyneuropathy , is highly-associated with a juvenile-onset form of the disease .
A cohort of 52 PN cases and 41 controls was genotyped on Illumina CanineHD BeadChips . After pruning for low genotyping rate , low minor allele frequencies , and failure to meet Hardy-Weinberg equilibrium , 101 , 284 SNPs remained for the association analysis , which was initially conducted in PLINK [11] . A highly significant CFA16 locus was identified , with the most significant SNP ( BICF2G630820235 , at bp position 57 , 375 , 008 , using CanFam2 positions ) achieving a raw P-value of 1 . 16×10−10 , and a genome-wide P-value of 1 . 00×10−4 after 10 , 000 permutations ( Figure 1A ) . A potential locus was also observed on CFA7 , with the best associated SNP ( BICF2P881479 , at bp position 25 , 815 , 288 ) achieving a raw P-value of 4 . 64×10−7 , and a genome-wide P-value of 0 . 015 after 10 , 000 permutations . A genomic inflation factor ( lambda ) of 1 . 38 indicated the presence of population stratification and possible cryptic relatedness . This was unsurprising , given that Leonbergers are a breed globally small in numbers , and due to this demographic history , the dogs selected for genotyping on the SNP arrays were unavoidably closely related . We therefore performed an association analysis using the mixed model function implemented in GenABEL [12] that resulted in lambda dropping to 1 . 004 . The same CFA16 and CFA7 SNPs achieved the lowest P-values: 1 . 90×10−7 and 5 . 79×10−5 for CFA16 and CFA7 , respectively ( Figure 1B ) . However , with 10 , 000 permutations of the mixed model test , the CFA16 locus remained significant ( Pgenome , corrected = 0 . 006 ) , while the CFA7 SNP achieved a p-value of only 0 . 68 ( Pgenome , corrected ) . The quantile-quantile plot of observed versus expected P-values of this mixed model permuted analysis also supports the effectiveness of the correction for population structure and the significance of the CFA16 locus ( Figure 1C ) . Shared homozygosity in many PN-affected dogs , and absence of homozygosity in controls , identified a 250 kb minimum haplotype at the CFA16 locus . This interval contains only two genes: rho guanine nucleotide exchange factor 10 ( ARHGEF10 ) and myomesin 2 ( MYOM2 , ENSCAFG00000024660 ) ( Figure 2 ) . ARHGEF10 was pursued as a positional and functional candidate gene due to its previous implication in peripheral nerve development and human peripheral hypomyelination [13] . Similar to humans , the canine ARHGEF10 gene has 29 coding exons , with two alternative transcripts that either include or exclude exon 9 , encoding a longer and shorter form of the protein . We first sequenced all coding exons , along with flanking intron boundaries , in four juvenile-onset severely-affected dogs , and four control dogs . This analysis revealed 98 sequence variants in comparison to the canine reference genome sequence ( Table S1 ) . Two SNPs predicted an amino acid change that was observed in both cases and controls , and the other exonic variants were synonymous . Two of the four cases used for sequencing were homozygous for the disease-associated CFA16 haplotype , and both of these dogs possessed a private 10 bp deletion ( c . 1955_1958+6delCACGGTGAGC ) . This deletion removes four nucleotides from the 3′-end of exon 17 ( nts 1955 to 1958 ) and six nucleotides from the 5′-end of intron 17 ( nts 1958+1 to +6 ) ( Figure 3A ) . A dog that is heterozygous for the CFA16 haplotype ( D/N ) was subsequently sequenced and the results were in accord with heterozygosity for the 10 bp deletion ( Figure 3A ) . The effect of the deletion on the ARHGEF10 transcript was investigated with cDNA prepared from the nervous tissue of an early-onset Leonberger PN case homozygous for the CFA16 haplotype ( D/D ) , a CFA16 haplotype heterozygote ( D/N ) Leonberger with no polyneuropathy , and one control dog ( N/N ) . Sequencing of an RT-PCR product containing the exon 17- exon 18 junction showed that mRNA produced in the PN-affected Leonberger , but not the control , lacked 4 bp from the 3′-end of exon 17 , while containing the entirety of exon 18 ( Figure 3B ) . This is consistent with the utilization of an alternate GT splice site immediately downstream from the normal splice site , which is deleted . The resultant mutant transcript contains a frame-shift which predicts a truncation of 50 . 5% of the amino acid sequence from the long form of the protein ( 52% of the short form ) . This predicts deletion of a WD40-like domain and two transmembrane segments . We detected only wild type sequence from the cDNA of the heterozygote , most likely due to poor amplification of the mutant sequence relative to wild type that would result from nonsense-mediated decay . All 93 Leonbergers from the original GWAS cohort were then genotyped for the ARHGEF10 deletion ( Table 1 ) . Eighteen of the 52 ( 35% ) cases were homozygous for the deletion ( D/D ) , 14 ( 27% ) were heterozygous ( D/N ) , and 20 ( 38% ) were homozygous normal ( N/N ) . The 18 D/D dogs represented both sexes and developed clinical signs at or before the age of three years; most did so before the age of two . Three D/N dogs and eight N/N dogs also developed PN signs at ≤3 years of age . None of the original 41 controls were D/D , four out of 41 ( 10% ) were heterozygous D/N , and 37 ( 90% ) were N/N . We next genotyped a larger and independent population of case ( n = 154 ) and control ( n = 160 ) Leonbergers ( “Validation Group” ) ( Table 1 ) , where the association of the ARHGEF10 mutation with PN was confirmed ( genotypic and allelic P-values of 9 . 57×10−6 and 5 . 98×10−7 , respectively ) . When combined , the GWAS and validation populations comprised 206 cases and 201 controls , which increased the significance of the genotypic and allelic frequency differences ( P-values of 5 . 01×10−11 and 2 . 75×10−15 , respectively ) . Overall , 44 . 4% ( 36/81 ) of young age-of-onset ( ≤3 years ) cases had the D/D ARHGEF10 genotype ( Table 1 , Figure 4 ) . Forty-one of the 81 early onset cases had undergone a nerve biopsy that showed pathological changes consistent with PN , and 21 of these ( 51 . 2% ) were homozygous for the ARHGEF10 deletion . In total , 90 of the 206 cases had undergone peripheral nerve biopsy to confirm pathological changes consistent with PN , with 21 of them ( 23 . 3% ) being homozygous for the ARHGEF10 deletion . In the combined population , 30 D/D cases are male and seven D/D cases are female . Affected males outnumber affected females in nearly every category of genotype and age-of-onset ( Table 1 ) . When the data is normalized to the number of samples from each sex , 65% of the submitted males were affected and 34% of the submitted females were affected . The effects of heterozygosity for the ARHGEF10 deletion were also ascertained in the combined population . Sixty percent ( 45 of 75 ) of all D/N dogs were PN cases and 40% ( 30 of 75 ) were controls ( Table 1 ) . D/N dogs accounted for 21 . 8% of all PN cases ( 45 of 206 ) and 14 . 9% of all controls ( 30 of 201 ) , and represent only a slightly significant difference in “D” allele frequency between cases and controls ( P-value = 0 . 009 ) . The average age-of-onset of clinical signs for cases in the combined population was 1 . 73 , 6 . 14 and 5 . 59 years for D/D , D/N and N/N dogs , respectively . Thus heterozygous or homozygous normal PN cases developed clinical signs at a similar later stage in life ( Figure 4 ) . Based on our assessment of medical records , PN cases that were heterozygous for the ARHGEF10 deletion were less likely to develop severe disease than were D/D dogs . A global group of 4 , 074 Leonbergers , primarily originating from North America and Europe , have been submitted to our laboratories since the discovery of the ARHGEF10 mutation . In this non-random sample , 1 . 3% of these dogs were homozygous for the ARHGEF10 mutation , 15 . 4% were heterozygous , and the “D” allele frequency was 9 . 0% . There was no significant difference between numbers of D/D males ( 34/54 ) and D/D females ( 20/54 ) ( P-value = 0 . 077 ) . The Leonberger breed resulted from crossing several breeds , including the Saint Bernard , Newfoundland , and Great Pyrenees , making it possible that the ARHGEF10 mutant allele could be present in these breeds as well . To date we have identified the identical deletion mutation in a homozygous state in four ( 2 males , 2 females ) Saint Bernard dogs with early-onset PN . Of these four dogs , three were confirmed as PN-affected with a nerve biopsy , and all four showed similar clinical signs to those seen in D/D Leonbergers , including laryngeal paralysis , pelvic limb deficits and flaccid paraparesis , and severe neurogenic muscle atrophy . All four were deceased by the age of two years as a result of their disease . A random sample of 383 Saint Bernards found 1 . 8% of them to be heterozygous and 98 . 2% of them to be homozygous normal . ( Note that the four D/D dogs are not included in the random sample , as they were acquired via diagnostic sample submission after showing clinical signs ) . Other related breeds , and breeds with clinical PN , have also been genotyped for the ARHGEF10 deletion and all were N/N ( Table S2 ) . This includes Newfoundlands ( n = 359 , three with PN diagnosed by nerve biopsies ) , Greater Swiss Mountain Dogs ( n = 48 ) , Bernese Mountain Dogs ( n = 2 , both with PN diagnosed by nerve biopsies ) , Great Pyrenees ( n = 7 , two with PN diagnosed by nerve biopsies ) , Great Danes ( n = 4 , all with PN diagnosed by nerve biopsies ) , and a single Golden Retriever ( also with PN diagnosed by nerve biopsy ) . We genotyped one of the most significantly associated CFA7 SNPs ( TIGRP2P93473_rs9189862 ) from the initial GWAS in a large Leonberger population of 193 cases and 174 controls ( Table 1 ) . This resulted in a large decrease of both genotypic and allelic significance for this SNP , indicating that the weaker initial association did not maintain significance .
There are only two early-onset peripheral neuropathies with a known genetic basis in dogs; both being autosomal recessive conditions due to mutations in the NDRG1 gene [7] , [8] . PN in the Leonberger was initially considered to be a single , possibly X-linked , disorder [9] , [10] . However , we now understand that Leonberger dogs affected with PN can display a broad range of age-of-onset , so it is reasonable to conclude that PN in this breed , like CMT disease in humans , may not be a single disease with one underlying genetic mutation . Nevertheless , we were able to utilize a GWAS strategy to identify a highly significantly associated locus on CFA16 . We found a biologically noteworthy positional candidate gene , ARHGEF10 , that harbored a 10 base deletion affecting the coding sequence . We further confirmed the association of this mutation with juvenile-onset PN in a large , independent validation group of Leonbergers , characterized the effects of genotype , age and sex on the mutation's association with PN , and detected it in cases of early-onset PN in Saint Bernard dogs . ARHGEF10 belongs to a large family of Rho guanine nucleotide exchange factors ( GEFs ) , which are activators of Rho-GTPases , molecular switches that participate in the regulation of a large number of signal transduction pathways , some of which influence cell polarity , specifically neuron morphology , including axon , dendrite , and spine growth , and axon guidance [14] . RhoGTPases are also specifically involved in directing the migration of Schwann cell precursors along outgrowing axons [15] . As ARHGEF10 is expressed in multiple tissues , with relatively higher expression in the spinal cord and dorsal root ganglion [13] ( and USCS Genome Browser ) , we hypothesize that the loss of ARHGEF10 leads to the loss of proper signaling for axon ensheathment and myelination in Leonberger PN . Autosomal recessive mutations in the rho GTPase GEF frabin/FGD4 gene have been previously implicated in demyelination of peripheral nerves in cases of CMT type 4H [16] , [17] , and an autosomal dominant point mutation predicting a missense mutation in exon 3 of ARHGEF10 has been identified in a human family experiencing non-clinical slowed nerve-conduction velocities [13] . A nerve biopsy of the human proband in this family revealed numerous thinly myelinated axons , without any gross signs of demyelination or axonal degeneration and regeneration , and none of the affected family members in the study experienced progression of symptoms . Subsequent work examining this human ARHGEF10 mutation concluded that the relatively mild phenotype is due to activation of GEF activity [18] . Conversely , Leonberger PN cases in the present study have decreased nerve fiber density , and chronic nerve fiber loss , resulting from axonal degeneration with progressive clinical signs of weakness and muscle atrophy . Given that the ARHGEF10 deletion in Leonbergers truncates approximately 50% of the protein , and the deleted portions contained the WD40-like domain and both transmembrane segments [19] , it is reasonable to suggest that the severe and progressive clinical and pathological phenotype observed in Leonbergers homozygous for the mutation is due to the loss-of-function of this GEF's activity . This is the first report of an ARHGEF10 mutation in any species resulting in a severe juvenile-onset PN . Our data suggests that LPN1 ( PN due solely to the ARHGEF10 mutation ) is most likely inherited in an autosomal recessive manner and explains approximately one-fifth of all PN cases in Leonbergers . There is a slight excess of D/N heterozygote cases ( compared to controls ) ( Table 1 ) ; this could be due to: 1 ) interaction with another locus , 2 ) the possibility that the deletion is actually semidominant , in which case , all homozygous D/D dogs become affected early in life , while heterozygous D/N dogs have an elevated risk of developing less severe disease later in life compared to truly normal N/N dogs , 3 ) that there are one or several rare additional undetected ARHGEF10 variants , or 4 ) sampling bias in the case population . A semidominant mode of inheritance seems doubtful , due to the fact that we have observed many D/N dogs without clinical signs of PN . Although we favor the recessive model , we cannot , as yet , entirely rule out any of these hypotheses . It has been recognized in several studies [20]–[24] that laryngeal paralysis is a strong indicator of the presence of an underlying ‘silent’ polyneuropathy that may later progress; this is likely the scenario with many of our later-onset Leonberger cases , whose clinical presentation is different than dogs with juvenile-onset . Therefore , we suggest that additional cases of PN could be due to other unidentified PN-causing mutation ( s ) , and may be influenced by age , sex , environmental or other genetic factors . A sex bias in submitted PN cases toward males is evident , as 70% of all cases were male , and 65% of all males in the study were affected , as compared to 37% of all females . Nevertheless , male and female homozygotes for the ARHGEF10 deletion exhibit the same classic clinical signs of peripheral PN , and in the global population of >4 , 000 genotyped Leonbergers there was no statistical difference in sex distribution of D/D cases . The identification of an autosomal PN-causing ARHGEF10 mutation , and no indication of association of PN to SNPs on the X chromosome in our GWAS , contradicts the previously assumed X-linked inheritance [9] , [10] . Hultin Jaderlund et al . argue from a limited pedigree analysis that inherited PN in Leonbergers is likely to be a single disease with an X-linked mode of inheritance , which is genetically different in Europe and the Americas , and descended from a Leonberger female , born in 1943 . However , our study , using to the extent possible the gold standard of peripheral nerve biopsy to confirm a diagnosis of PN , combined with mutation identification and analysis on a large , world-wide sample cohort , indicates that: 1 ) Leonberger PN represents at least two , if not more , distinct inherited diseases , 2 ) the first identified PN mutation is autosomal , and 3 ) it is found worldwide . A possible explanation for the higher number of males affected is that males develop more severe clinical signs at an earlier age because they are typically larger than females , with correspondingly longer peripheral nerves ( those typically more susceptible and affected first in peripheral neuropathies [25] ) . An alternative explanation involves the effects of neuroactive endogenous steroids , such as progesterone , testosterone , and their derivatives on the peripheral nervous system . These steroids originating from both peripheral glands and the nervous system [26] influence neurodegenerative processes as well as signaling pathways involved in neuronal cell death [27] . Neuroactive steroid levels in a rat model of CMT type 1A are sexually dimorphic [28] , and a recent study of rat peripheral nerve cell cultures demonstrated a sex difference in the promotion of Schwann cell proliferation by neuroactive steroids [29] . Finally , it has been suggested that sex differences in DNA methylation patterns may be exerted on the developing nervous system by steroid hormones [26] . It is entirely possible , then , that the overall sex-difference in Leonberger peripheral nerve pathology is a result of sex-specific presence and ability to respond to neuroactive steroids and/or epigenetic changes they introduce . The initially detected suggestive association on CFA7 was a possibility for a second inherited PN locus segregating in the Leonberger breed; however , it was not confirmed in a larger population . Although we might have missed identifying a non-coding regulatory mutation within the associated haplotype , it seems more likely that by random chance the controls selected for the GWAS population had fewer copies of a CFA7 haplotype that is actually quite common in the breed , which in turn misrepresented the allele frequencies and led to the appearance of significant association . While we cannot rule out that this CFA7 locus is involved with a subset of PN cases in Leonbergers , it is likely that a far more powerfully-designed GWAS will be necessary to identify additional PN loci in this breed . Besides apparent cases of PN caused by different genetic mutations , it is possible that some of the non-LPN1 “cases” in our sample collection are not truly PN and might represent misdiagnosed phenocopies . A gold standard for diagnosing PN would include measurement of motor and sensory nerve conduction velocities ( for demyelination ) , amplitude of the compound muscle action potential ( for axonal loss ) , histopathology of peripheral nerve biopsies and an examination conducted by a board-certified veterinary neurologist . However , since many dogs had not undergone sufficient diagnostic testing , we were unable to use these stringent criteria . Diseases such as hip dysplasia , a torn cruciate ligament , or undiagnosed osteosarcoma , for example , can induce gait abnormalities that mimic those seen in PN . Without a complete assessment , owners , and possibly veterinarians , can easily mistake these diseases , at least in the early stages , with PN . PN phenocopies can also result from primary hypothyroidism , which has been associated with peripheral neuropathy in dogs and can create very similar changes to the nerve appearance on biopsy [30] . Hypothyroidism typically affects middle to older-aged dogs , and especially medium to large-sized dogs [31] , [32] . Therefore an older-onset hypothyroid dog could be mistaken for an older-onset PN case . Lastly , a presumptively inherited leukoencephalomyelopathy ( LEMP ) has been reported in Leonberger dogs [33] . While LEMP is a disease of the central nervous system and no lesions are observed in the peripheral nerves , clinical signs such as ataxia and hypermetria are neurologic in origin , and could be mistaken as inherited peripheral PN . Despite these phenotyping challenges , strict case definitions emphasizing the diagnosis of PN from axonal degeneration and chronic nerve fiber loss observed in a peroneal nerve biopsy allowed us to assemble a strong case population for the initial GWAS . The Leonberger breed was created in the mid-1800s by a citizen of Leonberg , Germany , who ostensibly crossed a Landseer Newfoundland female with a St . Bernard male . Other breeds , such as the Pyrenean Mountain dog were likely also introduced [6] . The Leonberger breed experienced severe bottlenecks during and after both World Wars , and it is difficult to ascertain when Saint Bernards ceased to be interbred with Leonbergers . Clinically and histopathologically confirmed PN , similar to that of the Leonberger dogs , occurs in the Saint Bernard , Newfoundland , and Great Pyrenees ( Shelton , unpublished observation ) , but of these three , to date the ARHGEF10 deletion has only been identified in the Saint Bernard . Though it is possible that the ARHGEF10 deletion mutation pre-dates the formation of the Leonberger breed , and was introduced via the Saint Bernard , it is also possible that the mutation is newer , and was introduced through more recent cross-breeding . We believe it is less likely that the mutation arose in the Leonberger breed and was subsequently introduced to Saint Bernards , given the Saint Bernard's longer pedigreed breed establishment . The low sample numbers tested to date in other breeds does not yet effectively rule-out the presence of this deletion in any other breed of dog . In conclusion , we have identified a 10 bp deletion in the canine ARHGEF10 gene as the most likely causative mutation for a juvenile form of PN , now termed LPN1 . This mutation explains 20% of all PN-affected Leonbergers and occurs rarely in PN-affected Saint Bernards . Dog breeders can now select against the defective ARHGEF10 allele by genotyping breeding animals and using targeted mating to achieve a significant reduction of PN incidence; specific breeding recommendations and test result interpretations from our laboratories are included with this manuscript as Supporting Information S1 . This work suggests a critical function of ARHGEF10 in normal development and/or maintenance of peripheral nerves and in so doing we have also defined an excellent animal model for human CMT .
This study was performed using protocols approved by the Institutional Animal Care and Use Committees ( IACUC ) of the University of Minnesota ( UM ) , the University of Bern ( UB ) , and the University of California San Diego ( UCSD ) . Written consent was obtained from all dogs' owners . Leonberger samples were obtained primarily via elective owner submission or collected at breed club shows . Medical records , pedigrees , and a blood sample or cheek swab for DNA extraction were requested from each Leonberger dog submitted to the UM and UB . Following identification of the ARHGEF10 mutation , samples of many more Leonberger dogs were submitted for genotyping . Many of these dogs do not have complete medical information and were used only for a population study . Other dogs represented breeds closely related to the Leonberger ( including the Saint Bernard ) , or had developed signs of clinical polyneuropathy and/or had a nerve biopsy showing pathological changes consistent with PN . Samples submitted to UCSD additionally included nerve and/or muscle biopsies for diagnostic purposes . A frozen Leonberger nerve tissue sample from a PN case , shipped originally to UCSD for diagnostic biopsy purposes , as well as frozen spinal cord tissue from a Leonberger and frozen brain tissue of a Cavalier King Charles Spaniel , both unaffected with polyneuropathy and obtained for purposes of other studies , were used for RNA isolation and cDNA sequencing . Additional DNA samples used to examine frequency of the ARHGEF10 deletion in breed populations ( Newfoundlands , Saint Bernards , and Greater Swiss Mountain Dogs ) were collected for other studies and shared by collaborators ( see acknowledgement section ) . The clinical characterization of LPN , including morphometric , electrophysiological , biopsy and examination findings , has been described elsewhere [9] . As sample acquisition continued after publication of the original study , it became clear that a large spectrum of clinical signs and substantial variety in the available diagnostic information exists among cases . Therefore , the following criteria were established in order to select the best possible cases and controls for SNP array genotyping: Genomic DNA was isolated from blood or tissue using the Gentra PureGene blood kit ( Qiagen , Germantown , USA ) according to standard protocols; for tissue , the modified protocol for DNA purification from paraffin-embedded tissues from the same kit was used . Total RNA was isolated using TRIzol ( Life Technologies , Grand Island , NY , USA ) , according to manufacturer's instructions . Initially , genomic DNA from 52 cases and 41 controls was genotyped on the Illumina CanineHD BeadChip ( Illumina , San Diego , CA , USA ) that contains 173 , 662 SNP markers . SNP genotype data was analyzed in PLINK [11] , and subjected to standard quality control , where SNPs were excluded for poor genotyping rates ( <90% ) , low minor allele frequencies ( <0 . 05 ) , or deviation from Hardy Weinberg equilibrium in controls ( p<0 . 001 ) , and dogs were excluded for low genotyping success ( <90% ) . The data were subjected to chi-square tests of association , and 10 , 000 phenotype label-swapping permutations were utilized to determine genome-wide significance . Population stratification , resulting from close familial relationships , was confirmed by the genomic inflation factor calculated during the chi-square association test . Therefore , we applied a mixed model approach utilizing the GenABEL package [12] , with 100 , 000 permutations , to correct for this population stratification and any cryptic relatedness . Haplotypes around significantly associated loci were constructed using PHASE [34] , [35] . All bp positions used are reported from the May 2005 ( Broad/CanFam2 ) build of the canine genome , in order to be synchronized with the SNP arrays . Primers for the amplification of the coding region of ARHGEF10 were designed with the software Primer 3 after masking repetitive sequences with RepeatMasker . We amplified PCR products ( conditions available upon request ) using MJ Research PTC-100 thermal cyclers ( MJ Research , Inc . , Watertown , MA , USA ) covering exons and flanking intronic regions using AmpliTaqGold360Mastermix ( Life Technologies , Grand Island , NY , USA ) . Re-sequencing of the PCR products was performed after rAPid alkaline phosphatase ( Roche Diagnostics Corporation , Indianapolis , IN , USA ) and exonuclease I ( New England Biolabs ( NEB ) , Ipswich , MA , USA ) treatment using both PCR primers with the ABI BigDye Terminator Sequencing Kit 3 . 1 ( Life Technologies , Grand Island , NY , USA ) on an ABI 3730 genetic analyzer . We analyzed the sequence data with Sequencher 5 . 1 ( GeneCodes , Ann Arbor , MI , USA ) . We used two assays to genotype additional dogs . In the first assay , the following primers were used in PCR ( conditions available upon request ) to produce either a 380 or 390 bp length product: AGCCACTTTCGGGATTCTTC ( F ) and TGTTCCCTTGGTCACAGGAC ( R ) . PCR products were then digested with 3 U ApaLI enzyme ( NEB , Ipswich , MA , USA ) at 37°C for three hours with standard NEB reaction conditions . Digested products were visualized on 2% agarose gel: fragments with the deletion are 354 bp in length and fragments without the deletion are 307 bp in length . In the second assay , fragment size analyses was performed for the genotyping of the ARHGEF10 deletion ( primers: CGGGTCTTCATGCTCAGTG ( F ) and TGTTCCCTTGGTCACAGGAC ( R ) ) on an ABI 3730 capillary sequencer and analyzed with the GeneMapper 4 . 0 software ( Life Technologies , Grand Island , NY , USA ) . The deletion was further confirmed in cDNA prepared from tissue with RT-PCR . Total RNA was isolated under RNase-free conditions from flash frozen brain ( Cavalier King Charles Spaniel , a control dog ) , spinal cord ( Leonberger , an ARHGEF10 D/N dog with no polyneuropathy ) , and peripheral nerve tissue ( Leonberger , one PN affected dog with ARHGEF10 D/D genotype ) using Trizol's ( Life Technologies , Grand Island , NY , USA ) manufacturer instructions . Total RNA was reverse transcribed into cDNA using the Super Script II kit ( Life Technologies , Grand Island , NY , USA ) , following the manufacturer's standard protocol . cDNA was then subjected to PCR and sequenced using intron-spanning primers ( GCGGTCCGACGATATGATAG – F , ACACCTGCTTTCTCCAGCAC – R ) located in exon 17 and 19 . Throughout the manuscript , all ARHGEF10 cDNA numbering refers to accession XM_846774 . 3 and all protein numbering refers to accession XP_851867 . 3 . An RFLP assay specific to a SNP ( TIGRP2P93473_rs9189862 ) tagging the originally significantly associated CFA7 haplotype was developed in order to genotype a larger population . The following primers were used in PCR ( conditions available upon request ) to produce a 290 bp product: AGATTCCCAATCCCTGCTTC ( F ) and AGGCAGGGCTATTCTTTTGG ( R ) . PCR products were then digested with 5 U HaeIII enzyme ( NEB , Ipswich , MA , USA ) at 37°C for three hours with standard NEB reaction conditions and visualized on 2% agarose gels . Inherited Peripheral Neuropathies Mutation Database ( URL: http://www . molgen . ua . ac . be/cmtmutations/home/IPN . cfm ) University of California , Santa Cruz Genome Browser ( URL: www . genome . ucsc . edu ) American Kennel Club Leonberger Breed History ( URL: http://www . akc . org/breeds/leonberger/history . cfm ) Primer 3 ( URL: http://frodo . wi . mit . edu/primer3/ ) RepeatMasker ( URL: http://repeatmasker . genome . washington . edu )
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Leonberger dogs are a breed originally produced by crossing large-bodied dogs , including Saint Bernards and Newfoundlands . A peripheral neuropathy has been described in Leonbergers that is similar to a group of inherited polyneuropathies known as Charcot-Marie-Tooth disease in humans . We collected a cohort of well-characterized Leonberger polyneuropathy cases and controls , conducted a genome-wide association study , and ultimately identified a highly associated and likely causative mutation in the AHGEF10 gene . This sequence variant is a 10-bp deletion encompassing a splice site , which forces use of a downstream splice site to create a processed mRNA with a premature stop codon , and represents a loss-of-function mutation . The identical mutation was also found in several polyneuropathy-affected Saint Bernards . When homozygous , this deletion results in the onset of clinical signs before four years of age . ARHGEF10 has not previously been associated with severe CMT , but comes from a family of genes shown to be involved in neuron morphology . This first-documented severe polyneuropathy associated with an ARHGEF10 mutation in any species provides an opportunity to gain further insights into the pathobiology of diseases associated with this gene . The ARHGEF10 mutation does not , however , by itself account for all cases of polyneuropathy in Leonbergers or Saint Bernards .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"neurology",
"genetics",
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2014
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An ARHGEF10 Deletion Is Highly Associated with a Juvenile-Onset Inherited Polyneuropathy in Leonberger and Saint Bernard Dogs
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Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually . As with most cancers , it is a heterogeneous disease and different breast cancer subtypes are treated differently . Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease . In this work , we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission . We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble . We also find that model scores are highly consistent across multiple independent evaluations . This study serves as the pilot phase of a much larger competition open to the whole research community , with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective , transparent system for assessing prognostic models .
Breast cancer remains the most common malignancy in females , with more than 200 , 000 cases of invasive breast cancer diagnosed in the United States annually [1] . Molecular profiling research in the last decade has revealed breast cancer to be a heterogeneous disease [2]–[4] , motivating the development of molecular classifiers of breast cancer sub-types to influence diagnosis , prognosis , and treatment . In 2002 , a research study reported a molecular predictor of breast cancer survival [5] based on analysis of gene expression profiles from 295 breast cancer patients with 5 year clinical follow-up . Based on these results , two independent companies developed the commercially available MammaPrint [6] and Oncotype DX [7] assays , which have both been promising in augmenting risk prediction compared to models based only on clinical data . However , their role in clinical decision-making is still being debated . Based on the success of these initial molecular profiles , a large number of additional signatures have been proposed to identify markers of breast cancer tumor biology that may affect clinical outcome [8]–[13] . Meta-analyses indicate that many of them perform very similarly in terms of risk prediction , and can often be correlated with markers of cell proliferation [14] , a well-known predictor of patient outcome [15] , especially for ER+ tumors [16] , [17] . Therefore , it is much more challenging to identify signatures that provide additional independent and more specific risk prediction performance once accounting for proliferation and clinical factors . Recent studies have even suggested that most random subsets of genes are significantly associated with breast cancer survival , and that the majority ( 60% ) of 48 published signatures did not perform significantly better than models built from the random subsets of genes [18] . Correcting for the confounding effect of proliferation based on an expression marker of cell proliferation removes most of the signal from the 48 published signatures [18] . The difficulties in reaching community consensus regarding the best breast cancer prognosis signatures illustrates a more intrinsic problem whereby researchers are responsible for both developing a model and comparing its performance against alternatives [19] . This phenomenon has been deemed the “self-assessment trap” , referring to the tendency of researchers to unintentionally or intentionally report results favorable to their model . Such self-assessment bias may arise , for example , by choosing assessment statistics for which their model is likely to perform well , selective reporting of performance in the modeling niche where their method is superior , or increased care or expertise in optimizing performance of their method compared to others . In this work , we explore the use of a research strategy of collaborative competitions as a way to overcome the self-assessment trap . In particular , the competitive component formally separates model development from model evaluation and provides a transparent and objective mechanism for ranking models . The collaborative component allows models to evolve and improve through knowledge sharing , and thereby emphasizes correct and insightful science as the primary objective of the study . The concept of collaborative competitions is not without precedent and is most evident in crowd-sourcing efforts for harnessing the competitive instincts of a community . Netflix [20] and X-Prize [21] were two early successes in online hosting of data challenges . Commercial initiatives such as Kaggle [22] and Innocentive [23] have hosted many successful online modeling competitions in astronomy , insurance , medicine , and other data-rich disciplines . The MAQC-II project [24] employed blinded evaluations and standardized datasets in the context of a large consortium-based research study to assess modeling factors related to prediction accuracy across 13 different phenotypic endpoints . Efforts such as CASP [25] , DREAM [26] , and CAFA [27] have created communities around key scientific challenges in structural biology , systems biology , and protein function prediction , respectively . In all cases it has been observed that the best crowd-sourced models usually outperform state-of-the-art off-the-shelf methods . Despite their success in achieving models with improved performance , existing resources do not provide a general solution for hosting open-access crowd-sourced collaborative competitions due to two primary factors . First , most systems provide participants with a training dataset and require them to submit a vector of predictions for evaluation in the held-out dataset [20] , [22] , [24] , [26] , often requiring ( only ) the winning team to submit a description of their method and sometimes source code to verify reproducibility . While this achieves the goal of objectively assessing models , we believe it fails to achieve an equally important goal of developing a transparent community resource where participants work openly to collaboratively share and evolve models . We overcome this problem by developing a system where participants submit models as re-runnable source code by implementing a simple programmatic API consisting of a train and predict method . Second , some existing systems are designed primarily to leverage crowd-sourcing to develop models for a commercial partner [22] , [23] who pays to run the competition and provides a prize to the developer of the best-performing model . Although we support this approach as a creative and powerful method for advancing commercial applications , such a system imposes limitations on the ability of participants to share models openly as well as intellectual property restrictions on the use of models . We overcome this problem by making all models available to the community through an open source license . In this study , we formed a research group consisting of scientists from 5 institutions across the United States and conducted a collaborative competition to assess the accuracy of prognostic models of breast cancer survival . This research group , called the Federation , was set up as a mechanism for advancing collaborative research projects designed to demonstrate the benefit of team-oriented science . The rest of our group consisted of the organizers of the DREAM project , the Oslo team from the Norwegian Breast Cancer study , and leaders of the Molecular Taxonomy of Breast Cancer International Consortium ( METABRIC ) , who provided a novel dataset consisting of nearly 2 , 000 breast cancer samples with median 10-year follow-up , detailed clinical information , and genome-wide gene expression and copy number profiling data . In order to create an independent dataset for assessing model consistency , the Oslo team generated novel copy number data on an additional 102 samples ( the MicMa cohort ) , which was combined with gene expression and clinical data for the same samples that was previously put in the public domain by the same research group [4] , [28] . The initial study using the METABRIC data focused on unsupervised molecular sub-class discovery [29] . Although some of the reported sub-classes do correlate with survival , the goal of this initial work was not to build prognostic models . Indeed , the models developed in the current study provide more accurate survival predictions than those trained using molecular sub-classes reported in the original work . Therefore , the current study represents the first large-scale attempt to assess prognostic models based on a dataset of this scale and quality of clinical information . The contributions of this work are two-fold . First , we conducted a detailed post-hoc analysis of all submitted models to determine model characteristics related to prognostic accuracy . Second , we report the development of a novel computational system for hosting community-based collaborative competitions , providing a generalizable framework for participants to build and evaluate transparent , re-runnable , and extensible models . Further , we suggest elements of study design , dataset characteristics , and evaluation criteria used to assess whether the results of a competition-style research study improve on standard approaches . We stress that the transparency enabled by making source code available and providing objective pre-defined scoring criteria allow researchers in future studies to verify reproducibility , improve on our findings , and assess their generalizability in future applications . Thus the results and computational system developed in this work serve as a pilot study for an open community-based competition on prognostic models of breast cancer survival . More generally , we believe this study will serve as the basis for additional competition-based research projects in the future , with the goal of promoting increased transparency and objectivity in genomics research ( and other applications ) and providing an open framework to collaboratively evolve complex models leading to patient benefit , beyond the sum of the individual efforts , by leveraging the wisdom of crowds .
We used the METABRIC dataset as the basis of evaluating prognostic models in this study . This dataset contains a total of nearly 2 , 000 breast cancer samples . 980 of these samples ( excluding those with missing survival information ) were available for the duration of the collaborative competition phase of this study . An additional 988 samples became available after we had concluded our evaluation in the initial dataset and , fortunately , served as a large additional dataset for assessing the consistency of our findings . For each sample , the dataset contains median 10 year follow-up , 16 clinical covariates ( Table 1 ) , and genome-wide gene expression and copy number profiling data , normalized as described in [29] , resulting in 48 , 803 gene expression features and 31 , 685 copy number features summarized at the gene level ( see Methods ) . Initial analysis was performed to confirm that the data employed in the competition were consistent with previously published datasets and to identify potential confounding factors such as internal subclasses . Data-driven , unsupervised hierarchical clustering of gene expression levels revealed the heterogeneity of the data and suggested that multiple subclasses do exist ( not shown ) [29] . However , for the current analysis we decided to focus on the well established separation into basal , luminal , and HER2 positive subclasses , as previously defined [2] , [30] . These subclasses are known to closely match clinical data in the following way: most triple-negative samples belong to the basal subclass; most ER positive samples belong to the luminal subclass; and most ER negative HER2 positive samples belong to the HER2 subclass . To ensure that this holds in the current dataset , the 50 genes that best separate the molecular subclasses in the Perou dataset [31] ( PAM50 ) were used for hierarchical clustering of the METABRIC data and compared with a similar clustering of the Perou dataset ( Figure 1A ) . The results of the supervised clustering reveal similar subclasses with similar gene expression signatures as those presented by Perou et al , and were also consistent with the clinical definitions as presented above . Finally , the 3 subclasses show a distinct separation in their Kaplan-Meier overall survival plots for the three subtypes defined by the clinical data , where the HER2 subclass has the worst prognosis , followed by the basal subclass , and the luminal subclass has the best prognosis , as expected ( Figure 1B ) . This analysis shows that sub-classification based on ER ( IHC ) , PR ( gene expression ) , and HER2 ( copy number ) should capture the major confounding factors that may be introduced by the heterogeneity of the disease . Multiple individual clinical features exhibit high correlation with survival for non-censored patients , and have well documented prognostic power ( Table 1 , Figure 1C ) , while others have little prognostic power ( Figure 1D ) . To demonstrate that the competition data is consistent in this respect , a Cox proportional hazard model was fit to the overall survival ( OS ) of all patients using each one of the clinical covariates individually . As expected , the most predictive single clinical features are the tumor size , age at diagnosis , PR status , and presence of lymph node metastases ( Table 1 ) . To assess the redundancy of the clinical variables , an additional multivariable Cox proportional hazard model was fit to the overall survival ( OS ) of all patients using all clinical features . The remaining statistically significant covariates were patient age at diagnosis ( the most predictive feature ) , followed by tumor size , presence of lymph node metastases , and whether the patient received hormone therapy . Participants from our 5 research groups were provided data from 500 patient samples used to train prognostic models . These models were submitted as re-runnable source code and participants were provided real-time feedback in the form of a “leaderboard” based on the concordance index of predicted survival versus the observed survival in the 480 held-out samples . Participants independently submitted 110 models to predict survival from the supplied clinical and molecular data ( Table S1 ) , showing a wide variability in their performance , which was expected since there were no constraints on the submissions . Post-hoc analysis of submitted models revealed 5 broad classes of modeling strategies based on if the model was trained using: only clinical features ( C ) ; only molecular features ( M ) ; molecular and clinical features ( MC ) ; molecular features selected using prior knowledge ( MP ) ; molecular features selected using prior knowledge combined with clinical features ( MPC ) ( Table 2 ) . The complete distribution of the performance of all the models , evaluated using concordance index , and classified into these categories is shown in Figure 2 . Analysis of the relative performance among model categories suggested interesting patterns related to criteria influencing model performance . The traditional method for predicting outcome is Cox regression on the clinical features [32] . This model , which used only clinical features , served as our baseline , and obtained a concordance index of 0 . 6347 on the validation set . Models trained on the clinical covariates using state-of-the-art machine learning methods ( elastic net , lasso , random forest , boosting ) achieved notable performance improvements over the baseline Cox regression model ( Figure 2 , category ‘C’ ) . Two submitted models were built by naively inputting all molecular features into machine learning algorithms ( i . e . using all gene expression and CNA features and no clinical features ) . These models ( our category ‘M’ ) both performed significantly worse than the baseline clinical model ( median concordance index of 0 . 5906 ) . Given that our training set contains over 80 , 000 molecular features and only 500 training samples , this result highlights the challenges related to overfitting due to the imbalance between the number of features and number of samples , also known as the curse of dimensionality [33] , [34] . Models trained using molecular feature data combined with clinical data ( category ‘MC’ ) outperformed the baseline clinical model in 10 out of 28 ( 36% ) submissions , suggesting there is some difficulty in the naïve incorporation of molecular feature data compared to using only clinical information . In fact , the best MC model attributed lower weights to molecular compared to clinical features by rank-transforming all the features ( molecular and clinical ) and training an elastic net model , imposing a penalty only on the molecular features and not on the clinical ones , such that the clinical features are always included in the trained model . This model achieved a concordance index of 0 . 6593 , slightly better than the best-performing clinical only model . One of the most successful approaches to addressing the curse of dimensionality in genomics problems has been to utilize domain-specific prior knowledge to pre-select features more likely to be associated with the phenotype of interest [35] . Indeed , the majority of submitted models ( 66 of 110 , 60% ) utilized a strategy of pre-selecting features based on external prior knowledge . Interestingly , analysis of model submission dates indicates that participants first attempted naïve models incorporating all molecular features , and after achieving small performance improvements over clinical only models , evolved to incorporate prior information as the dominant modeling strategy in the later phase of the competition ( Figure 2B ) . This observation is consistent with previous reports highlighting the importance of real-time feedback in motivating participants to build continuously improving models [36] . All models trained on only the molecular features ( i . e . excluding the clinical features ) and incorporating prior knowledge ( MP category ) performed worse than the baseline model , with the highest concordance index being 0 . 5947 , further highlighting the difficultly in using molecular information alone to improve prognostic accuracy compared to clinical data . Twenty-four models outperformed the baseline by combining clinical features with molecular features selected by prior knowledge ( MPC category ) . The overall best-performing model attained a concordance index of 0 . 6707 by training a machine learning method ( boosted regression ) on a combination of: 1 ) clinical features; 2 ) expression levels of genes selected based on both data driven criteria and prior knowledge of their involvement in breast cancer ( the MASP feature selection strategy , as described in Methods ) ; 3 ) an aggregated “genomic instability” index calculated from the copy number data ( see Methods ) . The wide range of concordance index scores for models in the MPC category raises the question of whether the improved performance of the best MPC models are explained by the biological relevance of the selected features or simply by random fluctuations in model scores when testing many feature sets . Due to the uncontrolled experimental design inherent in accepting unconstrained model submissions , additional evaluations are needed to assess the impact of different modeling choices in a controlled experimental design . We describe the results of this experiment next . We analyzed the modeling strategies utilized in the original “uncontrolled” model submission phase and designed a “controlled” experiment to assess the associations of different modeling choices with model performance . We determined that most models developed in the uncontrolled experiment could be described as the combination of a machine learning method with a feature selection strategy . We therefore tested models trained using combinations of a discrete set of machine learning methods crossed with feature selection strategies using the following experimental design: This experiment design resulted in a total of 60 models based on combinations of modeling strategies from the uncontrolled experiment ( Table S4 ) , plus 20 models using ensemble strategies . This controlled experimental design allowed us to assess the effect of different modeling choices while holding other factors constant . Following an approach suggested in the MAQC-II study [24] , we designed negative and positive control experiments to infer bounds on model performance in prediction problems for which models should perform poorly and well , respectively . As a negative control , we randomly permuted the sample labels of the survival data , for both the training and test datasets , and computed the concordance index of each model trained and tested on the permuted data . To evaluate how the models would perform on a relatively easy prediction task , we conducted a positive control experiment in which all models were used to predict the ER status of the patients based on selected molecular features ( excluding the ER expression measurement ) . We found that all negative control models scored within a relatively tight range of concordance indices centered around 0 . 5 ( minimum: 0 . 468 , maximum: 0 . 551 ) , significantly lower than the lowest concordance index ( 0 . 575 ) of any model trained on the real data in this experiment . Conversely , all ER-prediction models scored highly ( minimum: 0 . 79 , maximum: 0 . 969 ) , suggesting that the scores achieved by our survival models ( maximum: 0 . 6707 ) are not due to a general limitation of the selected modeling strategies but rather the difficulty of modeling breast cancer survival . Overall , we found that the predictive performance of the controlled experiment models ( Figure 3A ) was significantly dependent on the individual feature sets ( P = 1 . 02e-09 , F-test ) , and less dependent on the choice of the statistical learning algorithm ( P = 0 . 23 , F-test ) . All model categories using clinical covariates outperformed all model categories trained excluding clinical covariates , based on the average score across the 4 learning algorithms . The best-performing model category selected features based on marginal correlation with survival , further highlighting the difficulty in purely data-driven approaches , and the need to incorporate prior knowledge to overcome the curse of dimensionality . The best-performing model used a random survival forest algorithm trained by combining the clinical covariates with a single additional aggregate feature , called the genomic instability index ( GII ) , calculated as the proportion of amplified or deleted sites based on the copy number data . This result highlights the importance of evaluating models using a controlled experimental design , as the best-performing method in the uncontrolled experiment combined clinical variables with GII in addition to selected gene expression features ( clinical variables plus only GII was not evaluated ) , and the controlled experiment pointed to isolating GII as the modeling insight associated with high prediction accuracy . The random survival forest trained using clinical covariates and GII was significantly better than a random survival forest trained using clinical covariates alone ( P = 2e-12 by paired Wilcoxon signed rank test based on 100 bootstrap samples with replacement from the test dataset ) . We also tested if inclusion of the GII feature improved model performance beyond a score that could be obtained by chance based on random selection of features . We trained 100 random survival forest models and 100 boosting models , each utilizing clinical information in addition to random selections of 50 molecular features ( corresponding to the number of features used based on the MASP strategy , which achieved the highest score of all feature selection methods ) . The best-performing model from our competition ( trained using clinical covariates and GII ) achieved a higher score than each of these 100 models for both learning algorithms ( P< = . 01 ) . The use of the aggregate GII feature was based on previous reports demonstrating the association between GII and poor prognosis breast cancer subtypes like Luminal B , HER2+ and Basal-like tumors [37] . We found that HER2+ tumors had the strongest association with the GII score ( P = 1 . 65e-12 , t-test ) which partly explains why it performs so well considering none of the patients were treated with compounds that target the HER2 pathway ( e . g . Herceptin ) . Samples with high GII scores were also associated with high-grade tumors ( P = 7 . 13e-13 , t-test ) , further strengthening its credential as a good survival predictor . However , despite these strong associations , the genomic instability index provided an added value to the strength of predictions even as clinical covariates histologic grade and HER2 status are used in the models . Boosting was the best-performing method on average . Elastic net and lasso exhibited stable performance across many feature sets . Random survival forests performed very well when trained on a small number of features based on clinical information and the genomic instability index . However , their performance decreased substantially with the inclusion of large molecular feature sets . Ensemble methods trained by averaging predicted ranks across multiple methods systematically performed better than the average concordance index scores of the models contained in the ensemble , consistent with previously reported results [38] . Strikingly , an ensemble method aggregating all 60 models achieved a concordance index score of . 654 , significantly greater than the average of all model scores ( . 623 ) ( Figure 3B ) . The ensemble performed better than the average model score for each of 100 resampled collections of 60 models each , using bootstrapping to sample with replacement from all 60 models ( P< = . 01 ) . The ensemble model scored better than 52 of the 60 ( 87% ) models that constituted the ensemble . We note that 2 of the algorithms ( boosting and random forests ) utilize ensemble learning strategies on their own . For both of the other 2 algorithms ( lasso and elastic net ) the method trained on an ensemble of the 15 feature sets scored higher than each of the 15 models trained on the individual feature sets ( Figure 3B ) . Consistent with previous reports , the systematic outperformance of ensemble models compared to their constituent parts suggests that ensemble approaches effectively create a consensus that enhances the biologically meaningful signals captured by multiple modeling approaches . As previously suggested in the context of the DREAM project [38]–[41] , our finding further reinforces the notion that crowd-sourced collaborative competitions are a powerful framework for developing robust predictive models by training an ensemble model aggregated across diverse strategies employed by participants . In the first round of the competition , we did not restrict the number of models a participant could submit . This raises the possibility of model overfitting to the test set used to provide real-time feedback . We therefore used 2 additional datasets to evaluate the consistency of our findings . The first dataset , which we called METABRIC2 , consisted of the 988 samples ( excluding those with missing survival data ) from the METABRIC cohort that were not used in either the training dataset or the test dataset used for real-time evaluation . The second dataset , called MicMa , consisted of 102 samples with gene expression , clinical covariates , and survival data available [4] , [28] and copy number data presented in the current study ( see Methods ) . We used the models from our controlled experiment , which were trained on the original 500 METABRIC samples , and evaluated the concordance index of the survival predictions of each model compared to observed survival in both METABRIC2 and MicMa . The concordance index scores across models from the original evaluation were highly consistent in both METABRIC2 and MicMa . The 60 models evaluated in the controlled experiment ( 15 feature sets used in 4 learning algorithms ) had Pearson correlations of . 87 ( P<1e-10 ) compared to METABRIC2 ( Figure 4A ) and . 76 ( P<1e-10 ) compared to MicMa ( Figure 4C ) , although we note that p-values may be over-estimated due to smaller effective sample sizes due to non-independence of modeling strategies . Model performance was also strongly correlated for each different algorithm across the feature sets for both METABRIC2 ( Figure 4B ) and MicMa ( Figure 4D ) . Consistent with results from the original experiment , the top scoring model , based on average concordance index of the METABRIC2 and MicMa scores , was a random survival forest trained using clinical features in combination with the GII . The second best model corresponded to the best model from the uncontrolled experiment ( 3rd best model in the controlled experiment ) , and used clinical data in combination with GII and the MASP feature selection strategy , and was trained using a boosting algorithm . A random forest trained using only clinical data achieve the 3rd highest score . The top 39 models all incorporated clinical data . As an additional comparison , we generated survival predictions based on published procedures used in the clinically approved MammaPrint [6] and Oncotype DX [7] assays . We note that these assays are designed specifically for early stage , invasive , lymph node negative breast cancers ( in addition ER+ in the case of Oncotype DX ) and use different scores calculated from gene expression data measured on distinct platforms . It is thus difficult to reproduce exactly the predictions provided by these assays or to perform a fair comparison to the present methods on a dataset that includes samples from the whole spectrum of breast tumors . The actual Oncotype DX score is calculated from RT-PCR measurements of the mRNA levels of 21 genes . Using z-score normalized gene expression values from METABRIC2 and MicMa datasets , together with their published weights , we recalculated Oncotype DX scores in an attempt to reproduce the actual scores as closely as possible . We then scored the resulting predictions against the two datasets and obtained concordance indices of 0 . 6064 for METABRIC2 and 0 . 5828 for MicMa , corresponding to the 81st ranked model based on average concordance index out of all 97 models tested , including ensemble models and Oncotype DX and MammaPrint feature sets incorporated in all learning algorithms ( see Table S5 ) . Similarly , the actual MammaPrint score is calculated based on microarray gene expression measurements , with each patient's score determined by the correlation of the expression of 70 specific genes to the average expression of these genes in patients with good prognosis ( defined as those who have no distant metastases for more than five years , ER+ tumors , age less than 55 years old , tumor size less than 5 cm , and are lymph node negative ) . Because of limitations in the data , we were not able to compute this score in exactly the same manner as the original assay ( we did not have the metastases free survival time , and some of the other clinical features were not present in the validation datasets ) . We estimated the average gene expression profile for the 70 MammaPrint genes based on all patients who lived longer than five years ( with standardized gene expression data ) , then computed each patient's score as their correlation to this average good prognosis profile . We scored the predictions against the two validation datasets and observed concordance indices of 0 . 602 in METABRIC2 and 0 . 598 in MicMa , corresponding to the 78th ranked out of 97 models based on average concordance index . We were able to significantly improve the scores associated with both MammaPrint and Oncotype DX by incorporating the gene expression features utilized by each assay as feature selection criteria in our prediction pipelines . We trained each of the 4 machine learning algorithms with clinical features in addition to gene lists from MammaPrint and Oncotype DX . The best-performing models would have achieved the 8th and 26th best scores , respectively , based on average concordance index in METABRIC2 and MicMa . We note that using the ensemble strategy of combining the 4 algorithms , the model trained using Mammaprint genes and clinical data performed better than clinical data alone , and achieved the 5th highest average model score , including the top score in METABRIC2 , slightly ( . 005 concordance index difference ) better than the random forest model using clinical data combined with GII , though only the 17st ranked score in MicMa . This result suggests that incorporating the gene expression features identified by these clinically implemented assays into the prediction pipeline described here may improve prediction accuracy compared to current analysis protocols . An ensemble method , aggregating results across all learning algorithms and feature sets , performed better than 71 of the 76 models ( 93% ) that constituted the ensemble , consistent with our finding that the ensemble strategy achieves performance among the top individual approaches . For the 19 feature selection strategies used in the METABRIC2 and MicMa evaluations , an ensemble model combining the results of the 4 learning algorithms performed better than the average of the 4 learning algorithms in 36 out of 38 cases ( 95% ) . Also consistent with our previous result , for both algorithms that did not use ensemble strategies themselves ( elastic net and lasso ) , an ensemble model aggregating results across the 19 feature sets performed better than each of the individual 19 feature sets for both METABRIC2 and MicMa . Taken together , the independent evaluations in 2 additional datasets are consistent with the conclusions drawn from the original real-time feedback phase of the completion , regarding improvements gained from ensemble strategies and the relative performance of models .
“Precision Medicine” , as defined by the Institute of Medicine Report last year , proposes a world where medical decisions will be guided by molecular markers that ensure therapies are tailored to the patients who receive them [42] . Moving towards this futuristic vision of cancer medicine requires systematic approaches that will help ensure that predictive models of cancer phenotypes are both clinically meaningful and robust to technical and biological sources of variation . Despite isolated successful developments of molecular diagnostic and personalized medicine applications , such approaches have not translated to routine adoption in standard-of-care protocols . Even in applications where successful molecular tests have been developed , such as breast cancer prognosis [5] , [6] , a plethora of research studies have claimed to develop models with improved predictive performance . Much of this failure has been attributed to “difficulties in reproducibility , expense , standardization and proof of significance beyond current protocols” [43] . The propensity of researchers to over-report the performance of their own approaches has been deemed the “self-assessment trap” [19] . We propose community-based collaborative competitions [43]–[49] as a general framework to develop and evaluate predictive models of cancer phenotypes from high-throughput molecular profiling data . This approach overcomes limitations associated with the design of typical research studies , which may conflate self-assessment with methodology development or , even more problematic , with data generation . Thus competition-style research may promote transparency and objective assessment of methodologies , promoting the emergence of community standards of methodologies most likely to yield translational clinical benefit . The primary challenge of any competition framework is to ensure that mechanisms are in place to prevent overfitting and fairly assess model performance , since performance is only meaningful if models are ranked based on their ability to capture some underlying signal in the data . For example , such an approach requires datasets affording sufficient sample sizes and statistical power to make meaningful comparisons of many models across multiple training and testing data subsets . We propose several strategies for assessing if the results obtained from a collaborative competition are likely to generalize to future applications and improve on state-of-the art methodologies that would be employed by an expert analyst . First , baseline methods should be provided as examples of approaches an experienced analyst may apply to the problem . In our study , we employed a number of such methods for comparison , including methodologies used in clinical diagnostic tests and multiple state-of-the-art machine learning methods trained using only clinical covariates . Second , performance of models should be evaluated in multiple rounds of independent validation . In this study , we employed a multi-phase strategy suggested by previous researchers [50] in which a portion of the dataset is held back to provide real-time feedback to participants on model performance and another portion of the dataset is held back and used to score the performance of all models , such that participants cannot overfit their models to the test set . If possible , we recommend an additional round of validation using a dataset different from the one used in previous rounds , in order to test against the possibility that good performance is due to modeling confounding variables in the original dataset . This experimental design provides 3 independent rounds of model performance assessment , and consistent results across these multiple evaluations provides strong evidence that performance of the best approaches discovered in this experimental design are likely to generalize in additional datasets . Finally , statistical permutation tests can provide useful safeguards against the possibility that improved model performance is attributable to random fluctuations based on evaluation of many models . Such tests should be designed carefully based on the appropriate null hypothesis . A useful , though often insufficient , test is to utilize a negative control null model , for example by permuting the sample labels of the response variable . We suggest that additional tests may be employed as post-hoc procedures designed specifically to provide falsifiable hypotheses that may provide alternative explanations of model performance . For example , in this study we assessed the performance of many models trained using the same learning algorithm ( random survival forest ) and the same clinical features as used in the top scoring model , but using random selections of molecular features instead of the GII feature . This test was designed to falsify the hypothesis that model performance is within the range of likely values based on random selection of features , as has been a criticism of previously reported models [18] . We suggest that the guidelines listed above provide a useful framework in reporting the results of a collaborate competition , and may even be considered necessary criteria to establish the likelihood that findings will generalize to future applications . As with most research studies , a single competition cannot comprehensively assess the full extent to which findings may generalize to all potentially related future applications . Accordingly , we suggest that a collaborative competition should indeed report the best forming model , provided it meets the criteria listed above , but need not focus on declaring a single methodology as conclusively better than all others . By analogy to athletic competitions such as an Olympic track race , a gold medal is given to the runner with the fastest time , even if by a fraction of a second . Judgments of superior athletes emerge through integrating multiple such data points across many races against different opponents , distances , weather conditions , etc . , and active debate among the community . A research study framed as a collaborative competition may facilitate the transparency , reproducibility , and objective evaluation criteria that provide the framework on which future studies may build and iterate towards increasingly refined assessments through a continuous community-based effort . Within several months we developed and evaluated several hundred modeling approaches . Our research group consisted of experienced analysts trained as both data scientists and clinicians , resulting in models representing state-of-the art approaches employed in both machine learning and clinical cancer research ( Table 3 ) . By conducting detailed post-hoc analysis of approaches developed by this group , we were able to design a controlled experiment to isolate the performance improvements attributable to different strategies , and to potentially combine aspects of different approaches into a new method with improved performance . The design of our controlled experiment builds off pioneering work by the MAQC-II consortium , which compiled 6 microarray datasets from the public domain and assessed modeling factors related to the ability to predict 13 different phenotypic endpoints . MAQC-II classified each model based on several factors ( type of algorithm , normalization procedure , etc ) , allowing analysis of the effect of each modeling factor on performance . Our controlled experiment follows this general strategy , and extends it in several ways . First , MAQC-II , and most competition-base studies [20] , [22] , [26] , accept submissions in the form of prediction vectors . We developed a computational system that accepts models as re-runnable source code implementing a simple train and predict API . Source code for all submitted models are stored in the Synapse compute system [51] and are freely available to the community . Thus researchers may reproduce reported results , verify fair play and lack of cheating , learn from the best-performing models , reuse submitted models in related applications ( e . g . building prognostic models in other datasets ) , build ensemble models by combining results of submitted models , and combine and extend innovative ideas to develop novel approaches . Moreover , storing models as re-runnable source code is important in assessing the generalizability and robustness of models , as we are able to re-train models using different splits or subsets of the data to evaluate robustness , and we ( or any researcher ) can evaluate generalizability by assessing the accuracy of a model's predictions in an independent dataset , such as existing related studies [5] or emerging clinical trial data [52] . We believe this software system will serve as a general resource that is extended and re-used in many future competition-based studies . Second , MAQC-II conducted analysis across multiple phenotypic endpoints , which allowed models to be re-evaluated in the context of many prediction problems . However , this design required models to be standardized across all prediction problems and did not allow domain-specific insights to be assessed for each prediction problem . By contrast , our study focused on the single biomedical problem of breast cancer prognosis , and allowed clinical research specialists to incorporate expert knowledge into modeling approaches . In fact , we observed that feature selection strategies based on prior domain-specific knowledge had a greater effect on model performance than the choice of learning algorithm , and learning algorithms that did not incorporate prior knowledge were unable to overcome challenges with incorporating high-dimensional feature data . In contrast to previous reports that have emphasized abstracting away domain-specific aspects of a competition in order to attract a broader set of analysis [50] , in real-word problems , we emphasize the benefit of allowing researchers to apply domain-specific expertise and objectively test the performance of such approaches against those of analysts employing a different toolbox of approaches . Finally , whereas MAQC-II employed training and testing splits of datasets for model evaluation , our study provides an additional level of evaluation in a separate , independent dataset generated on a different cohort and using different gene expression and copy number profiling technology . Consistent with findings reported by MAQC-II , our study demonstrates strong consistency of model performance across independent evaluations and provides an important additional test of model generalizability that more closely simulates real-world clinical applications , in which data is generated separately from the data used to construct models . More generally , whereas MAQC-II evaluated multiple prediction problems in numerous datasets with gene expression data and samples numbers from 70 to 340 , our study went deeper into a evaluating a single prediction problem , utilizing copy number and clinical information in addition to gene expression , and with a dataset of 2 , 000 samples in addition to an independently-generated dataset with 102 samples . The model achieving top performance in both the initial evaluation phase and the evaluation in additional datasets combined a state-of-the-art machine learning approach ( random survival forest ) with a clinically motivated feature selection strategy that used all clinical features together with an aggregate genomic instability index . Interestingly , this specific model was not tested in the uncontrolled phase , and was the result of the attempt to isolate and combine aspects of different modeling approaches in a controlled experiment . The genomic instability index measure may serve as a proxy for the degree to which DNA damage repair pathways ( including , for instance , housekeeping genes like p53 and RB ) have become dysregulated [37] . Beyond the specifics of the top performing models , we believe the more significant contribution of this work is as a building block , providing a set of baseline findings , computational infrastructure , and proposed research methodologies used to assess breast cancer prognosis models , and extending in the future to additional phenotype prediction problems . Towards this end , we have recently extended this work into an open collaborative competition through which any researcher can freely register and evaluate the performance of submitted models against all others submitted throughout the competition . Though this expanded breast cancer competition , and future phenotype prediction competitions to be hosted as extensions of the current work , we invite researchers to improve , refute , and extend our findings and research methodologies to accelerate the long arc of cumulative progress made by the community through a more transparent and objectively assessed process .
Our competition was designed to assess the accuracy of predicting patient survival ( using the overall survival metric , median 10 year follow-up ) based on feature data measured in the METABRIC cohort of 980 patients , including gene expression and copy number profiles and 16 clinical covariates ( Table 1 ) . Participants were given a training dataset consisting of data from 500 samples , and data from the remaining 480 were hidden from participants and used as a validation dataset to evaluate submitted models . We developed the computational infrastructure to support the competition within the open-source Sage Synapse software platform . Detailed documentation is available on the public competition website: https://sagebionetworks . jira . com/wiki/display/BCC/Home . The system is designed to generalize to support additional community-based competitions and consists of the following components ( Figure 5 ) : All models are available with downloadable source code using the Synapse IDs displayed in Table S1 and Table S4 . An automated script continuously monitored for new submissions , which were sent to worker nodes in a computational cluster for scoring . Each worker node ran an evaluation script , which called the submitted model's customPredict method with arguments corresponding to the gene expression , copy number , and clinical covariate values in the held-out validation dataset . This function returns a vector of predicted survival times in the validation dataset , which were used to calculate the concordance index as a measure of accuracy compared to the measured survival times for the same samples . Concordance index scores were shown in a real-time leaderboard , similar to the leaderboards displaying the models scores shown in Table S1 and Table S4 . Concordance index ( c-index ) is the standard metric for evaluation of survival models [53] . The concordance index ranges from 0 in the case of perfect anti-correlation between the rank of predictions and the rank of actual survival time through 0 . 5 in the case of predictions uncorrelated with survival time to 1 in the case of exact agreement with rank of actual survival time . We implemented a method to compute the exact value of the concordance index by exhaustively sampling all pairwise combinations of samples rather than the usual method of stochastically sampling pairwise samples . This method overcomes the stochastic sampling used in standard packages for concordance index calculation and provides a deterministic , exact statistic used to compare models . Data on the original 980 samples were obtained for this study in early January , 2012 . Study design and computational infrastructure were developed from then until March 14th , at which point participants were given access to the 500 training samples and given 1 month to develop models in the “uncontrolled experiment” phase . During this time , participants were given real-time feedback on model performance evaluated against the held-out test set of 480 samples . After this 1-month model development phase , all models were frozen and inspected by the group to conduct post-hoc model evaluation and identify modeling strategies used to design the controlled evaluation . All models in the controlled evaluation were re-trained on the 500 training samples and re-evaluated on the 480 test samples . After all evaluation was completed based on the original 980 samples , the METABRIC2 and MicMa datasets became available , and were used to perform additional evaluations of all models , which was conducted between January 2013–March 2013 . For the new evaluation , all data was renormalized to the gene level , as described below , in order to allow comparison of models across datasets performed on different platforms . Models were retrained using the re-normalized data for the same 500 samples in the original training set . All model source code is available in the subfolders of Synapse ID syn160764 , and specific Synapse IDs for each model are listed in Table S1 and Table S4 . Data stored in Synapse may be accessed using the Synapse R client ( https://sagebionetworks . jira . com/wiki/display/SYNR/Home ) or by clicking the download icon on the web page corresponding to each model , allowing the user to download a Zip archive containing the source files contained in the submission . The METABRIC dataset used in the competition contains gene expression data from the Illumina HT 12v3 platform and copy number data derived from experiments performed on the Affymetrix SNP 6 . 0 platform . In the initial round of analysis , the first 980 samples data was normalized as described in [29] , corresponding to the data available in the European Genome-Phenome Archive ( http://www . ebi . ac . uk/ega ) , accession number EGAS00000000083 . Copy number data was summarized to the gene level by calculating the mean value of the segmented regions overlapping a gene . Data for use in our study are available in the Synapse software system ( synapse . sagebase . org ) within the folder with accession number syn160764 ( https://synapse . prod . sagebase . org/#Synapse:syn160764 ) , subject to terms of use agreements described below . Data may be loaded directly in R using the Synapse R client or downloaded from the Synapse web site . Patients treated for localized breast cancer from 1995 to 1998 at Oslo University Hospital were included in the MicMa cohort , and 123 of these had available fresh frozen tumor material [4] , [28] . Gene expression data for 115 cases obtained from an Agilent whole human genome 4×44 K one color oligo array was available ( GSE19783 ) [54] . Novel SNP-CGH data from 102 of the MicMa samples were obtained using the Illumina Human 660k Quad BeadChips according to standard protocol . Normalized LogR values summarized to gene level were made available and are accessible in Synapse ( syn1588686 ) . All data used for the METABRIC2 and MicMa analyses are available as subfolders of Synapse ID syn1588445 . For comparison of METABRIC2 and MicMa , we standardized all clinical variables , copy number , and gene expression data across both datasets . Clinical variables were filtered out that were not available in both datasets . Data on clinical variables used in this comparison are available in Synapse . All gene expression datasets were normalized according the supervised normalization of microarrays ( snm ) framework and Bioconductor package [55] , [56] . Following this framework we devised models for each dataset that express the raw data as functions of biological and adjustment variables . The models were built and implemented through an iterative process designed to learn the identity of important variables . Once these variables were identified we used the snm R package to remove the effects of the adjustment variables while controlling for the effects of the biological variables of interest . SNP6 . 0 copy number data was also normalized using the snm framework , and summarization of probes to genes was done as follows . First , probes were mapped to genes using information obtained from the pd . genomewidesnp . 6 Bioconductor package [57] . For genes measured by two probes we define the gene-level values as an unweighted average of the probes' data . For genes measured by a single probe we define the gene-level values as the data for the corresponding probe . For those measured by more than 2 probes we devised an approach that weights probes based upon their similarity to the first eigengene . This is accomplished by taking a singular value decomposition of the probe-level data for each gene . The percent variance explained by the first eigengene is then calculated for each probe . The summarized values for each gene are then defined as the weighted mean with the weights corresponding to the percent variance explained . For Illumina 660k data we processed the raw files using the crlmm bioconductor R package [58] . The output of this method produces copy number estimates for more than 600k probes . Next , we summarized probes to Entrez gene ids using a mapping file obtained from the Illumina web site . For genes measured by more than two probes we selected the probe with the largest variance . Feature selection strategies used in the controlled experiment ( identified through post-hoc analysis of the uncontrolled experiment ) are described briefly in Table 3 . Specific genes used in each category are available within Synapse ID syn1643406 and can be downloaded as R binaries via the Synapse web client or directly loaded in R using the Synapse R client . Most feature selection strategies are sufficiently described in Table 3 , and we provide additional details on 2 methods below . The MASP ( Marginal Association with Subsampling and Prior Knowledge ) algorithm employs the following procedure: all genes were first scored for association with survival ( using Cox regression ) in chunks of 50 randomly selected gene expression samples . This process was repeated 100 times which resulted in an overall survival association score where is the p-value associated with the Cox regression on the expression of gene i in sample set j . All genes were sorted in descending order by their survival association score and the top 50 oncogenes and transcription factors were kept . A list of human transcription factors was obtained from [59] and a list of oncogenes was compiled by searching for relevant keywords against the Entrez gene database . GII is a measure of the proportion of amplified or deleted genomic loci , calculated from the copy number data . Copy number values are presented as segmented log-ratios with respect to normal controls . Amplifications and deletions are thus counted when or and devided by the total number of loci . The data used in this study were collected and analyzed under approval of an IRB [29] . The MicMa study was approved by the Norwegian Regional Committee for medical research ethics , Health region II ( reference number S-97103 ) . All patients have given written consent for the use of material to research purposes .
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We developed an extensible software framework for sharing molecular prognostic models of breast cancer survival in a transparent collaborative environment and subjecting each model to automated evaluation using objective metrics . The computational framework presented in this study , our detailed post-hoc analysis of hundreds of modeling approaches , and the use of a novel cutting-edge data resource together represents one of the largest-scale systematic studies to date assessing the factors influencing accuracy of molecular-based prognostic models in breast cancer . Our results demonstrate the ability to infer prognostic models with accuracy on par or greater than previously reported studies , with significant performance improvements by using state-of-the-art machine learning approaches trained on clinical covariates . Our results also demonstrate the difficultly in incorporating molecular data to achieve substantial performance improvements over clinical covariates alone . However , improvement was achieved by combining clinical feature data with intelligent selection of important molecular features based on domain-specific prior knowledge . We observe that ensemble models aggregating the information across many diverse models achieve among the highest scores of all models and systematically out-perform individual models within the ensemble , suggesting a general strategy for leveraging the wisdom of crowds to develop robust predictive models .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
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"cancer",
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"diagnosis",
"medicine",
"genome",
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"cancer",
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"breast",
"tumors",
"statistics",
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"cancers",
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"basic",
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"research",
"algorithms",
"oncology",
"mathematics",
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"personalized",
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] |
2013
|
Improving Breast Cancer Survival Analysis through Competition-Based Multidimensional Modeling
|
The production of anticompetitor toxins is widespread among bacteria . Because production of such toxins is costly , it is typically regulated . In particular , many toxins are produced only when the local cell density is high . It is unclear which selection pressures shaped the evolution of density-dependent regulation of toxin production . Here , we study the evolution of toxin production , resistance and the response to a cell-density cue in a model of an evolving bacterial population with spatial structure . We present results for two growth regimes: ( i ) an undisturbed , fixed habitat in which only small fluctuations of cell density occur , and ( ii ) a serial-transfer regime with large fluctuations in cell density . We find that density-dependent toxin production can evolve under both regimes . However , the selection pressures driving the evolution of regulation differ . In the fixed habitat , regulation evolves because it allows cells to produce toxin only when opportunities for reproduction are highly limited ( because of a high local cell density ) , and the effective fitness costs of toxin production are hence low . Under serial transfers , regulation evolves because it allows cells to switch from a fast-growing non-toxic phenotype when colonising a new habitat , to a slower-growing competitive toxic phenotype when the cell density increases . Colonies of such regulating cells rapidly expand into unoccupied space because their edges consist of fast-growing , non-toxin-producing cells , but are also combative because cells at the interfaces with competing colonies do produce toxin . Because under the two growth regimes different types of regulation evolve , our results underscore the importance of growth conditions in the evolution of social behaviour in bacteria .
Many bacteria produce antimicrobial toxins that impede the growth of competing bacteria or even kill them [1–3] . A wide variety of such toxins has been discovered , ranging from narrow-range bacteriocins to broad-range antimicrobials that may even affect eukaryotic cells [4 , 5] . Because producing and secreting toxins is metabolically costly , toxin producing strains have a reduced growth rate compared to non-producers [6–8] . Toxin production is therefore an example of spiteful behaviour: it is costly to the actor and harmful to the recipient [9 , 10] . Over the years , the questions how and under what conditions such spiteful production of anticompetitor toxins can evolve have inspired many experimental and theoretical studies [6 , 7 , 9 , 11–14] . These studies showed that the spatial scale over which interactions between bacteria take place is a key determinant of the evolutionary stability of toxin production . Modelling work predicted that toxin production is evolutionarily unstable in homogeneous , well-mixed environments with global interactions ( e . g . , a shaken flask ) , while stable coexistence between a toxin-producing strain ( or killer , K ) and sensitive strain ( S ) can arise in spatially structured environments where interactions are local ( e . g . , agar plates ) [7 , 11] . Under well-mixed conditions , the K strain is fully outcompeted by resistant ( R ) cells ( which for instance arise from K cells through mutational loss of toxin production but not resistance ) , because R cells avoid the metabolic costs for toxin production but equally benefit from the killing of S cells by the K strain . In spatially structured environments , however , killing and competition are local processes and hence K cells preferentially benefit from the killing effect of their toxin compared to non-producing cells . The population dynamics then follow local cycles of non-transitive “rock-paper-scissors” interactions: The K strain invades patches of S cells; these K cells are subsequently outcompeted by the R strain; and these R cells are in turn outcompeted by the faster-growing S strain [7] . These local KRS-dynamics cause the emergence of wave-like spatial patterns , in which all three strains ( K , R , and S ) coexist [11 , 15 , 16] . These theoretical predictions were confirmed in vitro in populations of colicin-producing , -sensitive and -resistant Escherichia coli cells growing in flasks or on plates [6 , 7] , and in vivo in enteric bacterial populations in a mouse model [12] . Coexistence of a toxin-producing , -resistant , and -sensitive strain was also found in the more complex environment of a growing biofilm in vitro [13] , and in silico modelling showed that the structure of the biofilm strongly affects the evolution of toxin production [14] . In all studies described above , genes for toxin production and resistance were constitutively expressed . Like many metabolically costly traits , however , toxin production is often tightly regulated [3 , 4 , 17] . In particular , the expression of many anticompetitor toxins is regulated by cell-density cues: small diffusible molecules that are excreted by bacterial cells , such that their extra-cellular concentration reflects the local density of bacteria ( see refs [4 , 18 , 19] for reviews ) . Responding to a density cue allows bacteria to express costly genes only when the local cell density is high . The high prevalence of toxin regulation by density cues raises the question of how such regulation evolved . A common explanation for the regulation of social behaviours by cell-density cues is that the benefits of the regulated social behaviour outweigh the costs only if a sufficient number of cells ( the quorum ) display the behaviour at the same time; the regulation is then also called quorum sensing ( QS ) [20 , 21] . This is for instance the case for the cooperative production of some public goods , like siderophores in Pseudomonas aeruginosa [22 , 23] . For such costly public-good production , both theoretical and experimental work has shown that production of , and response to , a quorum sensing signal can be beneficial , as it allows cells to produce the public good only if the cell density is high and the benefit of coordinated public-good production is hence substantial [23–28] . Whereas the evolution of density-dependent regulation is relatively well-understood in the context of cooperative public goods , its evolution in relation to spiteful toxin production is less well-studied . In a single modelling study , Czárán and Hoekstra ( 2007 ) considered whether the evolution of density-dependent toxin production could be explained by similar reasoning as described above for public goods [29] . A key feature of their model is that the toxin was assumed to be effective only if the local density of toxin producing cells exceeded a threshold density , which required toxin producers to cooperate . Furthermore , the model allowed gain and loss mutations of QS signal production , the hypothesis being that a genotype-specific cue that is produced by killer cells only might evolve to inform the killer cells about the local killer cell density . The study found , however , that QS regulation of toxin production was evolutionarily unstable to resistant “cheater” cells that produce the QS signal ( and hence induce killer cells to produce toxin ) but not the costly toxin [29] . Hence , considering anticompetitor toxin as a type of public good that is cooperatively produced has so far been unsuccessful in explaining density regulation of toxin production , and it remains unclear what selection pressures drive the evolution of toxin regulation by density cues . Here , we therefore explore different explanations for the evolution of density-dependent toxin production . We use a computational model of evolving , spatially structured bacterial populations that deliberately differs from previous studies . In particular , we do not impose that a minimal quorum of toxin-producing cells is required in order to affect sensitive cells , but instead assume that the effect of toxin increases linearly with its concentration . Also , we focus on cases where toxin production is regulated by density cues that are produced by all cells ( including cells that are sensitive to the toxin ) . For instance , production of the antimicrobial pyocyanin by the common pathogen Pseudomonas aeruginosa increases in the presence of peptidoglycan fragments , a general indicator of the local density of gram-positive bacteria [30] ( see Discussion for more examples ) . In such cases , the cue indicates the total cell density rather than the density of killer cells . We obtain results for two growth regimes: ( i ) a long-term local competition regime , in which the population evolves in a fixed , densely populated habitat , and ( ii ) a serial-transfer regime in which small , random subsets of the population repeatedly colonise new habitats . We show that density-dependent regulation of toxin production can evolve under both regimes . By characterising the selection pressures shaping the evolution of regulation , we explain how density-dependent toxin production can evolve under various growth regimes .
The bacteria in our model live on a square lattice ( Fig 1 ) . The model bacteria have several evolvable characteristics , which constitute their “genotype” ( Fig 1A ) . Firstly , they carry a toxin production “gene” and a resistance “gene” , each with three possible alleles: inactive ( “Off” ) , constitutively expressed ( “On” ) , or expressed in response to the density cue ( “Regulated” , or “Reg” ) . We refer to a cell’s toxin production and resistance genotype using a bracket notation: e . g . , bacteria with genotype “ ( Reg , On ) ” regulate their toxin production but constitutively express resistance . Secondly , bacteria that express their toxin gene may differ in their toxin production rate πT . Lastly , each bacterium has a response threshold value θ , which is the cue concentration above which it expresses its regulated genes , if it has any . The concentrations of the cell-density cue and the toxin are modelled with partial differential equations describing their local production , degradation , and diffusion . The cue molecule is produced by all cells , while the toxin is only produced by cells expressing the toxin production gene . We consider the dynamics of the cue and toxin to be much faster than the population dynamics of the bacteria , so that the concentration profiles of the cue and toxin at any given time are determined by the current spatial distribution of bacteria [14 , 26 , 31 , 32] . We choose arbitrary units of concentration such that the concentration of the density cue varies between 0 and 1 and the toxin concentration varies between 0 and max ( πT ) , the largest toxin production rate in the bacterial population ( see Methods ) . Time in the model progresses in discrete steps . At the beginning of each time step , each bacterium senses the local cue concentration , which together with the cell’s genotype determines the cell’s “phenotype” ( Fig 1A ) . The phenotype is given by three variables: toxin production ϕT , resistance ϕR , and cue response ϕC , which take the value 0 ( Not expressed ) or 1 ( Expressed ) . If the cell has a regulated gene , the corresponding phenotype value is set to 1 if the cue concentration exceeds the cell’s response threshold value θ , and to 0 otherwise . Variable ϕC indicates whether the cell expresses a regulatory response system; it is 1 if the cell has at least one Reg gene , and 0 otherwise . Note that a regulating cell’s phenotype adapts to the local cue concentration at each simulation time step . An exception to this instantaneous adaptation is made in cells that regulate both their toxin production and resistance ( genotype ( Reg , Reg ) ) : inspired by the com-regulon of Streptococci which displays a delay in the expression of the bacteriocins CbpD and LytA relative to the immunity factor ComM [33–35] , a delay of τdelay time steps is implemented between the expression of resistance and the expression of toxin . This delay prevents cells from killing neighbouring cells that have exactly the same genotype but coincidentally experience a slightly lower cue concentration and therefore do not ( yet ) express resistance . Reproduction and cell death depend on a cell’s phenotype . The death rate of sensitive bacteria increases linearly with the local toxin concentration ( similar to refs [7 , 11 , 14] ) . Importantly , this means that no minimal density ( quorum ) of toxin producers is required for the toxin to have an effect on sensitive cells ( in constrast to [29] ) . Rather , each toxin producing cell proportionally adds to the killing rate of sensitive cells in the local neighbourhood . The bacteria locally compete for a growth-limiting resource . To incorporate such local competition , at most one bacterium is allowed to occupy each lattice site . Bacteria surrounding an empty site compete for reproduction based on their respective reproduction rates ( which depend on their phenotypes , as described below ) . When a cell reproduces , the daughter cell inherits the parent’s genotype , except that with small probability mutations are introduced . Toxin production and resistance are metabolically expensive [6–8] . Being able to respond to the density cue requires the production of receptors and a signal transduction pathway , and therefore likely also bears a metabolic cost . We incorporate these metabolic costs by reducing the reproduction rate of cells expressing these phenotypes . The costs for resistance and the ability to respond to the cue are constant , while the cost for toxin production increases linearly with the cell’s toxin production rate πT . Note that cells that regulate a gene always pay a cost for being able to respond to the cue , but in return may avoid the costs of toxin production and resistance when the density cue concentration is below their response threshold θ .
We first considered a bacterial population growing in a fixed habitat without external perturbations , by running the model on an undisturbed simulation lattice . Because we aimed to investigate the evolutionary potential of the production of anticompetitor toxin and its density-dependent regulation in general , rather than to model a specific strain of bacteria , we explored possible evolutionary outcomes of the model by performing a parameter sweep over the six defining parameters of our model: the spatial range of the density cue Lcue , the spatial range of the toxin Ltox , R 0 - 1 of the bacteria ( where R0 is the maximal expected number of daughter cells produced per bacterial life time ) , the scaled toxin production cost b ^ T , the resistance cost CR , and the cue response cost CC ( see S1 Text for the derivation of these parameters ) . We performed 2000 simulations with random parameter settings uniformly sampled from broad parameter ranges ( see Methods , Table 1 ) . For each simulation , we then calculated the mean abundance of each genotype and each phenotype after an evolutionary steady state was reached . Based on this evolved population composition , the simulations were classified into four categories ( S1 Fig ) . In 1737 of the 2000 runs , the sensitive genotype ( Off , Off ) fixed in the population , indicating that most parameter conditions were unfavourable to the evolution of toxin production . In 228 simulations , at least one toxin producing genotype , sensitive genotype , and resistant genotype were found , hence yielding a KRS-system . Most of these evolved KRS-systems consisted of non-regulating killers ( genotype ( On , On ) ) , non-regulating resistant cells ( genotype ( Off , On ) ) , and sensitive cells ( genotype ( Off , Off ) ) ( Fig 2A ) , reproducing the KRS-dynamics observed in earlier studies [7 , 11 , 15 , 16] . In 22 of the 228 simulations yielding KRS-dynamics , however , at least one regulating genotype was selected . In a clear majority of these ( 17 runs ) , a single regulating genotype was found: cells that regulate their toxin production , but constitutively express resistance ( genotype ( Reg , On ) ) . These regulating cells coexisted with sensitive cells ( genotype ( Off , Off ) ) and resistant cells ( genotype ( Off , On ) ) ( Fig 2B ) . Lastly , 35 simulations did not result in fixation of sensitives or a KRS-system and were classified as “other” . In none of these simulations regulation evolved , and they were therefore not further considered . So far , we have considered model bacteria living in a fixed , undisturbed habitat . Natural growth conditions , however , tend to vary substantially over space and time , and such variations in growth conditions may cause large fluctuations in cell density . To examine how externally induced density fluctuations affect the evolution of density-dependent toxin regulation , we simulated serial transfers: a procedure , well-known from experimental evolution studies , in which a small sample of the population is regularly transferred to fresh medium [38–42] . The population dynamics were simulated as before , except that periodically the simulation was paused , a random sample of cells was taken from the population , and these founder cells were randomly placed on a new simulation lattice ( “fresh medium” ) . These serial transfers were continued for many cycles to allow the system to approach evolutionary steady state .
Using a simulation model , we have shown that the production of anticompetitor toxins can become regulated by a cell-density cue in evolving populations under two different growth regimes: in a fixed habitat , and in serial-transfer cycles . Under both regimes , regulation of toxin production evolves because it allows cells to adjust their investment in toxin production to changes in the local competition and growth opportunities . However , the selection pressures driving the evolution of toxin production at high density , and the resulting types of regulation that evolve , differ between the growth regimes . In the fixed habitat , regulating killer cells evolved that produce toxin only at very high local cell densities ( Fig 4 ) . We showed that these cells use the density cue to produce toxin only if reproduction opportunities are very scarce and the effective fitness costs of toxin production are therefore low ( S4 Fig ) . This type of regulation relies on the fact that , in the model , cells that cannot reproduce due to a lack of empty neighbouring lattice sites can nevertheless produce toxin , at very low or even zero fitness cost . This phenomenon could occur in reality if at low cell density reproduction and toxin production are limited by the same resource ( s ) ( e . g . , the availability of carbon or nitrogen substrates ) , while at high cell density reproduction is limited by a different resource that does not limit toxin production ( e . g . , crowding or a lack of substrate not required for toxin production ) . Interestingly , such conditions have previously been found to stabilise cooperative secretions of swarming-promoting biosurfactants in Pseudomonas aeruginosa [43] . Production and secretion of these carbon-rich biosurfactants is regulated by nutrient availability , such that they are only produced when growth is limited by another nutrient than carbon ( in this case , the nitrogen source ) and the fitness costs of biosurfactant secretion are hence low , a mechanism called metabolic prudence by Xavier et al . , 2011 [43] . Our model hence predicts that such metabolic prudence could also promote the evolution of density-dependent toxin regulation in long-term local competition environments by reducing the effective fitness costs of toxin production . Under the serial transfer regime , we find that the evolution of regulation is dictated by two selection pressures: ( i ) selection for fast reproduction at the edge of expanding colonies , and ( ii ) selection for the expression of competitive phenotypes ( toxin producing and/or resistant ) at the interface between colonies ( Figs 5 and 6 ) . The dynamics of single cells founding expanding colonies leads to competition between these clonal colonies , and bacteria are selected for the colony structure that they produce ( see S3 Video ) . After a serial transfer , those colonies that express a sensitive phenotype at their edges expand more rapidly into the newly available empty ( or “resource-rich” ) space . Regulation allows cells to recognise the expanding edges of their colonies , because the local cell density at colony edges is low , and to thus express a sensitive phenotype at these edges . The selection for fast colony expansion explains why cells are selected to express a sensitive phenotype at the edge of expanding colonies , but does not explain why expression of resistance and/or toxin production at high cell density is favoured . As long as a colony clonally expands without interacting with other colonies , the production of toxins does not confer any benefit . However , since the colony expands at its edges and the spatial range of the toxin is limited , the observed production of toxin in the interior of the colony also does not hamper the fast expansion of the colony . As soon as the expanding colony meets another colony , the situation changes: toxin production then yields a potential benefit in the competition with cells of the other colony ( which might be sensitive to the toxin ) . Regulating cells cannot distinguish between the interior of a single colony or the interface between two colonies , because at both sites the local cell density is high . Responding to high cell density however allows the cells to express their competitive phenotype ( toxin production or resistance ) when in direct competition with cells of another colony ( thus performing “competition sensing” c . f . , [4] ) , while expression of the competitive phenotype in the interior of the colony does not slow down the colony’s expansion . The marked differences between results obtained in the fixed habitat and under serial transfers show that the evolutionary dynamics in the model strongly depend on the growth regime . This is not just true for the model presented here . For instance , in experimentally grown colonies of toxin-producing , resistant , and sensitive E . coli strains it was found that populations under range expansion do not always show the coexistence patterns found in a stationary environment [44] . In experimental evolution , it has also long been known that the experimental regime can pose strong selection pressures on evolving populations [45 , 46] . Because results obtained in one growth condition often do not generalise to other conditions , it is important to consider multiple regimes in theoretical and experimental evolutionary studies . The differences between the evolution of regulation in the two growth regimes also warrant the question which results provide the more likely explanation for the observed density-dependent regulation of toxin production in nature . In our model , regulation evolved more frequently in the serial transfer regime than in the fixed habitat ( S6A Fig ) . The regulation that evolved under serial transfers was also more robust to a lag between the change in cue concentration and the switch in phenotype ( S5 and S11 Figs ) , and to noise in the cue concentration ( S12 Fig ) . Lastly , the limitation of toxin production in the fixed habitat to instances where the effective fitness cost is very low ( similar to metabolic prudence , c . f . , [43] ) can only explain the evolution of density-dependent toxin regulation if specific conditions on the resources limiting reproduction and toxin production are met ( see Discussion above ) . The use of the density cue to recognise the edge of an expanding colony after a serial transfer , however , can favour the evolution of density-dependent toxin regulation as long as bacterial replication is limited by a resource that is present at higher concentration on the edge of a colony than in its interior . This seems to be a fairly general condition . Although “metabolic prudence” might contribute to the evolution of regulation for some toxins , we consider selection for the ability to switch between a fast-growing phenotype when colonising a new environment and a competitive phenotype when competing with other bacteria as the biologically more feasible and general candidate to explain evolution of density-dependent toxin regulation . A similar switch between a fast-growing phenotype when colonising an environment and slower-growing social phenotypes when cell density is high was recently found in a model of quorum-sensing ( QS ) regulated cooperative public good production in growing biofilms [47] . In this model by Schluter et al . ( 2016 ) , cells that regulated the production of costly public good through QS were found to outcompete constitutive producer cells , because the regulating cells exhibited a fast-growing , non-producer phenotype during the early stages of biofilm growth , and only switched to public good production when cell density increased . Regulation hence allowed colonies to expand rapidly when cell density was low , and to express a cooperative phenotype when cell density was high . Although this selection for fast colonisation is indeed reminiscent of our results , the selection pressures underlying the social behaviour ( public good secretion or toxin production ) differ substantially . In the case of public good secretion , Schluter et al . show that the QS signal acts as a measure of local relatedness , allowing cells to delay the secretion of public good until they are surrounded by clone mates and the benefit of public good production is high . In the case of toxin production , however , relatedness is a double-edged sword . While toxin production is promoted by high relatedness between toxin producers and those benefitting from the killing , this benefit only arises when non-related sensitive cells are present in the local neighbourhood [48 , 49] . In the model presented here , the benefits of the toxin production at high cell density are not explained by high local relatedness , but rather by the presence of ( unrelated ) sensitive competitors . A key feature of the density cue considered in this study is that it is produced by all bacteria . The choice of such a “total-density” cue was inspired by many natural examples of regulation by such cues . For instance , expression of the bacteriocin mutacin in the dental bacterium Streptococcus mutans is regulated by autoinducer-2 ( AI-2 ) [50 , 51] , a general quorum sensing molecule that is produced by many species of bacteria as a metabolic byproduct [52 , 53] . AI-2 is also involved in the regulation of bacteriocin production in the insect pathogen Photorhabdus luminescens [54] . Additionally , in the common pathogen Pseudomonas aeruginosa the production of the broad-spectrum antimicrobial pyocyanin is enhanced by the presence of peptidoglycan fragments , which indicates high local density of gram-positive bacteria [30] . In addition to the examples of regulation by total-density cues provided above , a wide variety of density-dependent toxin regulation mechanisms exists . Instead of using a density cue as an indicator for the presence of competitor cells , some bacteria more directly sense the presence of competitors , for instance through cell damage caused by these competitors , and respond with toxin production [55 , 56] . Other toxins promote their own production; examples include several colicins [56–58] and the lantibiotics , a large class of bacteriocins produced by Gram-positive bacteria including nisin and subtilin [18 , 59] . For these bacteriocins , a modelling study showed that during invasion events , cells that regulate their bacteriocin based on cell density outcompete cells that constitutively express bacteriocin , if the cost of bacteriocin production and the amount of bacteriocin produced are high [60] . Although this study did not consider long-term evolutionary dynamics and did for instance not include resistant , non-toxin-producing strains , its prediction agrees with the conditions that we find for the evolution of regulation ( see Fig 3 and S6B Fig ) . Notably , the expression of many other toxins is regulated by quorum sensing molecules that seem to be produced specifically for regulation of the toxin [4 , 17 , 61] . These QS molecules are often produced by toxin producing cells only , and hence act as “killer-specific” cues . A population in which such a killer-specific quorum sensing signal evolves might be prone to social cheating on the signal , e . g . , by cells that produce the signal but do not produce the toxin , or by cells that cease their signal production and go “under the radar” . Such social signalling cheaters cannot arise for total-density cues , because these are by definition produced by all cells . The evolutionary explanations presented in this work for regulation by total-density cues therefore cannot necessarily be generalised to regulation by killer-specific cues . As far as we are aware , only a single modelling study has been undertaken to examine the co-evolution of toxin production and potentially killer-specific quorum sensing , which found that QS control of toxin production was unstable to social cheating [29] ( see also the Introduction ) . In this model , however , cell density was fixed , and only the local population composition ( e . g . , the fraction of toxin producing bacteria ) varied over time . In the model presented here , we have seen that large fluctuations in cell density drastically change the evolution of social behaviours associated with cell density , and might favour the evolution of regulation . Studying the evolution of toxin regulation by killer-specific cues under serial transfers is therefore an interesting and promising direction for future research . Testing whether or not bacteria employ the types of regulation we identified would require the careful monitoring of the temporal dynamics of toxin production , resistance and cell division in bacterial colonies expanding after serial transfers or growing on a plate at high density , preferably at single-cell level ( as e . g . , done by Mavridou et al . [56] ) . Under serial transfers , the model predicts that bacterial colonies should consist of toxin-producing cells at the interior of the colony , and sensitive cells on the edge of the colony . Furthermore , these sensitive cells at the edge should switch to a toxin-producing phenotype when encountering another colony . These predictions can be tested by following toxin production at the single-cell level in growing bacterial colonies of bacteria known to regulate their toxin production with a general cell-density cue . Ahead of such experiments , our modelling work has provided more insight into the mechanisms underlying the evolution of complex regulation systems in microbial populations .
We developed a spatially explicit individual-based model of bacteria evolving their production of an anticompetitor toxin , resistance , and response to a cell-density cue . Bacteria in the model are characterised by a genotype of four characteristics: a toxin production gene , a resistance gene , a toxin production rate πT and a cue response threshold θ ( Fig 1A ) . The bacteria live on a square N × N lattice ( N = 512 for all simulations in this paper ) with periodic boundary conditions . Each lattice site can contain at most one bacterium . To understand why the regulating killer cells ( genotype ( Reg , On ) ) can outcompete constitutive killer cells ( genotype ( On , On ) ) under certain conditions , we compared invasion dynamics of these two killer types . To allow for a fair comparison , we first evolved constitutive killers under conditions that would usually favour regulation by removing the possibility of regulation from the model ( S3A Fig , evolved under the same parameter conditions as Fig 4 ) . Over three replicate simulations , constitutive killers under these conditions evolved a mean toxin production rate of πT = 0 . 13 . Over the ten replicate simulations of evolving ( Reg , On ) -killers , these cells evolved a mean toxin production rate of πT = 0 . 8 and a mean response threshold of θ = 0 . 875 ( S2 Fig ) . We therefore constructed two “average evolved killer strains” , a constitutive killer with genotype ( On , On ) and πT = 0 . 13 , and a regulating killer with genotype ( Reg , On ) , πT = 0 . 8 and θ = 0 . 875 , and compared the invasion dynamics of these two constructed killer strains . To characterise the invasion into a sensitive population , a 20-cell-wide strip of one of the two killer strains was placed on a lattice that was otherwise filled with a sensitive population at carrying capacity ( S3B Fig ) . Population dynamics were then simulated and the decline of the number of sensitives over time was followed ( S3B Fig ) . The invasion speed was calculated as v inv S = - β S on t K S N lattice sites time , ( 10 ) where βS on t is the linear regression coefficient of the number of sensitive cells on time , N is the number of rows of the simulation lattice and KS = ( 1 − δ/γ ) is the density of sensitive cells at carrying capacity . Similarly , the invasion speed of resistant cells ( genotype ( Off , On ) ) into a population of ( Reg , On ) -cells and ( On , On ) -cells was measured by placing a 20-cell-wide strip of resistant cells on a lattice otherwise filled with ( Reg , On ) -cells or ( On , On ) -cells at carrying capacity and calculating v inv R = β R on t K R N lattice sites time , ( 11 ) where βR on t is the linear regression coefficient of the number of resistant cells on time and KR = ( 1 − δ/ ( γ ( 1 − cR ) ) is the density of sensitive cells at carrying capacity . ( The invasion speeds we measure here serve as a tool to quantify the difference between the two killer strains and thus better understand the evolutionary outcome of the simulations . For a more formal analysis of the effect of toxin production and quorum sensing on invasion speeds , see [67] ) . Note that to calculate v inv S the decline of the number of sensitives is used , while in the calculation of v inv R the increase in the number of resistant cells is considered . This choice was made because the characteristics of the sensitive strain and the resistant strain are the same in both invasion experiments , while the two killer strains differ . For each invasion experiment , 10 replicate runs were performed . Under the serial transfer regime , simulations were again initialised with cells with random genotypes placed at a random 10% of lattice sites . Population dynamics were simulated as before , except that the simulations were periodically paused and a transfer was performed . At each transfer , a random sample of the population at the end of the growth cycle was taken as founder cells for the new population . These founder cells were then randomly placed on an otherwise empty simulation lattice , and the simulation of the population dynamics was resumed until the next transfer . Unless otherwise noted , transfers were performed every 500 simulation time steps , and each new cycle was seeded with 1000 founder cells . Simulations were continued for 800 ( parameter sweep ) or 1200 ( example runs ) transfer cycles . Evolutionary steady state was generally reached well before the end of the simulation .
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Bacteria live in microbial communities , in which they compete with many other microbes for nutrients and space . In this competitive environment , almost all known bacterial strains produce toxins that impair or kill other bacteria . This chemical warfare is thought to be one of the major factors shaping microbial diversity . Many toxins are produced only if the local density of bacteria is high . To achieve this , bacteria respond to cell-density cues: signalling molecules or other indicators of the presence of other cells . Here , we use a computational model to study the evolution of density-based regulation of toxin production in bacterial populations . We show that such regulation can arise under various growth conditions , and analyse the selection pressures driving its evolution . In particular , we find that if bacteria regularly need to colonise a new habitat , density-based regulation allows them to express a fast-growing , non-toxic phenotype when expanding into uncolonised territory , and a slower-growing , toxin-producing phenotype when competing with other strains . Colonies of regulating cells show a typical structure , with cells of the fast-growing , sensitive phenotype at their expanding edges , and toxin-producing cells in the colony interior and at interfaces between colonies .
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2019
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Toxin production spontaneously becomes regulated by local cell density in evolving bacterial populations
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Inherited ataxias are characterized by degeneration of the cerebellar structures , which results in progressive motor incoordination . Hereditary ataxias occur in many species , including humans and dogs . Several mutations have been found in humans , but the genetic background has remained elusive in dogs . The Finnish Hound suffers from an early-onset progressive cerebellar ataxia . We have performed clinical , pathological , and genetic studies to describe the disease phenotype and to identify its genetic cause . Neurological examinations on ten affected dogs revealed rapidly progressing generalized cerebellar ataxia , tremors , and failure to thrive . Clinical signs were present by the age of 3 months , and cerebellar shrinkage was detectable through MRI . Pathological and histological examinations indicated cerebellum-restricted neurodegeneration . Marked loss of Purkinje cells was detected in the cerebellar cortex with secondary changes in other cortical layers . A genome-wide association study in a cohort of 31 dogs mapped the ataxia gene to a 1 . 5 Mb locus on canine chromosome 8 ( praw = 1 . 1×10−7 , pgenome = 7 . 5×10−4 ) . Sequencing of a functional candidate gene , sel-1 suppressor of lin-12-like ( SEL1L ) , revealed a homozygous missense mutation , c . 1972T>C; p . Ser658Pro , in a highly conserved protein domain . The mutation segregated fully in the recessive pedigree , and a 10% carrier frequency was indicated in a population cohort . SEL1L is a component of the endoplasmic reticulum ( ER ) –associated protein degradation ( ERAD ) machinery and has not been previously associated to inherited ataxias . Dysfunctional protein degradation is known to cause ER stress , and we found a significant increase in expression of nine ER stress responsive genes in the cerebellar cortex of affected dogs , supporting the pathogenicity of the mutation . Our study describes the first early-onset neurodegenerative ataxia mutation in dogs , establishes an ERAD–mediated neurodegenerative disease model , and proposes SEL1L as a new candidate gene in progressive childhood ataxias . Furthermore , our results have enabled the development of a genetic test for breeders .
Ataxia is a neurological symptom of defective motor coordination that can affect gait , balance , speech and gaze [1] . Human hereditary ataxias are rare heterogeneous disorders characterized by progressive degeneration of the cerebellum and cerebellar connections , with a variable degree of involvement from extra-cerebellar structures [2] . The predominant inheritance patterns are autosomal dominant and autosomal recessive [1] . Unlike the autosomal dominant spinocerebellar ataxias ( SCAs ) , which usually affect the central nervous system ( CNS ) , the recessive disorders involve more often other organs [1] . Typical age of onset for dominant ataxias is between 30 to 50 years of age [3] , whereas the recessive forms tend to have an onset before the age of 20 years [4] . Causative mutations have been identified for at least 19 different dominant SCAs , most of which are caused by repeat expansions [5] , [6] . In recessive human ataxias , the number of known disease genes is somewhere around 20 , depending on the classification criteria [2] , [7]–[10] . Described pathological mechanisms are diverse but include some common themes , such as accumulation of protein aggregates , defects in the DNA-repair system , mitochondrial dysfunction and oxidative stress [1] , [2] , [9] , [11] . In addition to the known human ataxia genes , several spontaneous mutations that cause cerebellar degeneration have been recognized in mice [12]–[14] . Cerebellar degeneration has also been described in several dog breeds [15]–[30] . In veterinary medicine , the disease group is referred to as cerebellar cortical abiotrophies ( CCAs ) , where abiotrophy describes the idiopathic premature neuronal degeneration [31] . Clinical signs in canine CCAs include ataxia , dysmetria , tremors , broad-based stance and loss of balance , all of which contribute to the often significant ambulatory difficulties [32] , [33] . Majority of the described canine phenotypes are early-onset and manifest by the age of 3 to 4 months [16] , [17] , [19]–[23] , [25] , [27] , [28] . Later-onset and slowly progressing CCAs are less common but occur in some breeds [18] , [24] , [26] . In a classical CCA , pathological findings are focused on the cerebellar cortex where the primary degenerative change is the loss of cortical Purkinje cells ( PCs ) , followed by secondary changes in granular and molecular cell layers [32] , [33] . Primary degeneration of cortical granule cells is seen more rarely [27] , [29] . Involvement of CNS structures other than the cerebellum has been reported in some breeds , for instance in Kerry Blue Terriers [16] and Brittany Spaniels [24] . A more systemic phenotype is seen in the Bernese Mountain Dog , where cerebellar degeneration is accompanied by a hepatic degeneration [23] . In Rhodesian Ridgebacks , affected dogs present with a diluted coat color [22] . Collectively , the variability in disease onset , severity and histopathological details indicate a heterogeneous genetic etiology across different breeds . Although autosomal recessive inheritance has been proposed in several breeds [16] , [18] , [26] , [30] , the underlying genetic causes of canine primary ataxias have remained largely unidentified . Thus far , a molecular characterization has been reported only in a rare type of neonatal ataxia in Coton De Tulear dogs that have a mutation in the GRM1 glutamate receptor gene [34] . Additionally , a putative CCA locus has been recently mapped to canine chromosome 3 ( CFA3 ) in Australian Kelpies [35] . In the present study , we have examined clinical and genetic characteristics of hereditary ataxia that affects the Finnish Hound ( FH ) dog breed . A previous case report has indicated an early-onset progressive cerebellar neurodegeneration in a FH puppy [15] . We provide a more comprehensive clinical picture in a larger sample cohort and identify a recessive mutation in a novel ataxia gene .
Ten affected FH puppies from six different litters were referred to a veterinary neurology clinic for general clinical , orthopedical and neurological examinations ( Table 1 ) . One healthy littermate was examined as a control dog . At the time of examination , affected puppies were from 3 to 4 months old . The clinical signs were first noticed at a mean age of 9 weeks , ranging from 4 to 12 weeks ( Table 1 ) . General clinical and orthopedical examination did not reveal any significant changes . Neurological examinations were strongly indicative of cerebellar dysfunction by revealing generalized cerebellar ataxia with dysmetria ( Video S1 ) , postural reaction deficits ( Video S2 ) and intention tremor ( Video S3 ) . Cranial and spinal nerve reflexes were normal , and all affected dogs had normal cognition . Serum biochemistry profiles , complete blood cell count ( CBC ) and cerebrospinal fluid ( CSF ) cell count were within normal limits in all affected dogs . For an undefined reason , protein concentration was mildly elevated in one affected puppy . All ten affected puppies were euthanized because of a rapid disease progression and poor prognosis . Cerebellar pathology was supported by magnetic resonance imaging ( MRI ) and post-mortem examinations . Nine out of ten affected puppies showed reduced cerebellar size on T1- and T2-weighted ( T1W and T2W ) midsagittal brain MRI scans ( Figure 1 ) . No changes were detected in the cerebrum or brainstem . General pathological examination did not reveal any significant gross changes outside the CNS . The weight of the cerebellum relative to the total brain mass was measured in five dogs and ranged from 8 . 1 to 9 . 7% . This indicated a loss of cerebellar mass as the normal proportion of the cerebellum is ≥10% [32] . A few disease nonspecific histological findings were made; a mild interstitial pneumonia was seen in four dogs and mild follicular hyperplasia in the spleen of three dogs . Histological changes of the nervous system were restricted to the cerebellum in all examined puppies . The cerebellar cortex showed marked premature degeneration and loss of PCs with consequent neuronal depletion in the granular cell layer ( Figure 2B and 2D ) . The cerebellar vermis and the paramedian lobule were consistently the most severely affected areas . The cranial regions of the cerebellar cortex were more affected than the caudal regions . The ventrolateral parts , including paraflocculi and flocculus , were spared and partially normal ( Figure 2A and 2C ) . In the cortical areas , where severe PC loss was present , glial cells ( Bergmans glia ) were proliferating between the molecular and the granular cell layers ( Figure 2D ) . The remaining PCs were shrunken and eosinophilic with marginated nuclear chromatin or showed total loss of cytoplasmic basophilic Nissl substance ( chromatolysis ) ( Figure 2E and 2F ) . The granular cell layer was markedly depleted of neurons and showed mild astrocytosis in areas of profound PCs loss ( Figure 2G ) . Occasional degenerated and vacuolated axons were detected in the granular layer . Mild to moderate ongoing degeneration and myelinophagia was seen within the cerebellar white matter of the severely affected areas ( Figure 2H ) . Neither transsynaptic degeneration in the cerebellar nuclei nor retrograde degeneration of the olivary nucleus was found . Immunohistochemical ( IHC ) staining for canine distemper virus and parvovirus showed no positivity . The overall severity of the histopathological findings , including active PC degeneration , total granule cell and PC loss , consecutive white matter lesions and the extent of the lesions , are summarized in Table 1 . A pedigree was established around the known affected FH puppies to determine the most likely mode of inheritance ( Figure 3 ) . According to the pedigree data , all affected dogs were born from healthy parents , both sexes were affected and the proportion of affected puppies across litters was 29% , near the expected 25% . These observations were consistent with an autosomal recessive mode of inheritance . We first used a candidate gene approach to try to identify the causative gene . Altogether 24 known human and murine ataxia genes were selected ( Table S1 ) , and the segregation of microsatellite markers , within or adjacent to the candidate genes , was studied in three nuclear families . Each family included both parent dogs and at least one affected and one healthy offspring . No co-segregation was observed between any of the markers and the disease . We subsequently proceeded to map the FH ataxia locus by using a genome-wide approach . A cohort of 31 dogs , comprising 13 cases , 11 obligate carrier parents and seven non-affected siblings , were genotyped using Illumina's 22K canine SNP chip . A standard case-control association test was carried out on the 13 cases and seven full-sibling controls by using PLINK software [36] . This revealed a genome-wide significant association on CFA8 with two SNPs , BICF2P948919 and BICF2P754995 , that had the best nominal and corrected p-values of praw = 1 . 1×10−7 and pgenome = 7 . 5×10−4 ( Figure 4A ) . The association results were confirmed by utilizing a joint family-based linkage and association analysis program PSEUDOMARKER [37] . The joint analysis , which was carried out on the associated CFA8 using the entire genotyped sample cohort , identified the same locus and a single most significant SNP , BICF2P948919 ( LOD score = 3 . 3 , association p = 2 . 2×10−7 and joint analysis p = 4 . 0×10−10 ) ( Figure 4B ) . Assessment of genotypes at the associated CFA8 locus revealed a shared 1 . 5 Mb homozygous haplotype block in affected dogs , spanning from 56 . 0 to 57 . 5 Mb ( Figure 4C ) . All genotyped parent dogs were carriers of this disease haplotype . Within the 1 . 5 Mb block , only the most significant SNP , BICF2P948919 , showed complete segregation with the disease , indicating that the causative variant probably lies in its vicinity . The 1 . 5 Mb haplotype contained seven genes . Two of these , LOC100687147 ( serine/threonine-protein kinase Nek6-like ) and LOC612107 ( uncharacterized hypothetical gene ) were likely pseudogenes . The other five were known protein-coding genes , centrosomal protein 128kDa ( CEP128 ) , thyroid stimulating hormone receptor ( TSHR ) , general transcription factor IIA ( GTF2A1 ) , stoning 2 ( STON2 ) and sel-1 suppressor of lin-12-like ( SEL1L ) ( Figure 4D ) . We ranked SEL1L as the best candidate gene for mutation screening due to its neuronal expression , and function in a protein degradation pathway in the endoplasmic reticulum ( ER ) [38]–[41] . Impaired protein degradation is a common feature in several neurodegenerative diseases [42] , [43] . In addition , the fully segregating SNP , BICF2P948919 , was located on the second intron of the SEL1L gene . Besides SEL1L , the STON2 gene was considered a plausible causative gene because of its neuronal function in synaptic vesicle recycling at presynaptic nerve terminals [44]–[47] . The coding regions of the five known protein coding genes were sequenced in two affected puppies and in two obligate carrier parents in order to identify possible disease-causing mutations . No sequence variants that segregated with the phenotype were found in TSHR or GTF2A1 . Sequencing of STON2 and CEP128 revealed a few segregating variants but none of them were in the coding regions ( Table S2 ) . In SEL1L , we identified altogether four coding and 19 non-coding variants , all of which segregated with the disease ( Table S2 ) . Three of the exonic variants were synonymous but one was a non-synonymous cytosine to thymine change on exon 19 ( c . 1972T>C ) that results in a serine to proline alteration at position 658 of the encoded protein ( p . Ser658Pro ) ( Figure 4E ) . The c . 1972T>C variant was genotyped in the full sample cohort and showed complete segregation and a 100% penetrance with the disease . All 13 affected puppies were homozygous for the T>C change , all 13 parents heterozygous and 20 littermates either heterozygous ( 12 out of 20 ) or wild-type ( 8 out of 20 ) . Segregation of the variant was further validated by genotyping altogether 241 randomly selected unaffected FHs . None of these population controls were homozygous for the C allele but a 10% carrier frequency ( 24/241 ) was indicated . Segregation analysis gave a highly significant association between the C allele and disease ( p = 1 . 8×10−42 ) . Moreover , the c . 1972T>C allele was completely absent in 349 dogs from 51 other breeds ( Table S3 ) , including a Russian hound breed , which is related to FHs . We later received a sample from a newly affected FH puppy from Finland that was found homozygous for the CC genotype . Another suspected FH ataxia puppy from Sweden was tested free of the mutation . However , further inquiries of this puppy's phenotype revealed that it had not presented with clinical signs typical for FH ataxia but had in reality suffered from an episodic disorder . The puppy could not be examined further as it had been euthanized at 6 weeks of age . The probability of the SEL1L p . Ser658Pro change having a pathogenic effect was evaluated by utilizing bioinformatics tools . SEL1L is a transmembrane glycoprotein that resides in the endoplasmic reticulum ( ER ) [48] , [49] . The carboxy-terminus of the protein harbors the transmembrane domain and amino-terminal protein body is exposed to the ER lumen . The lumenal protein body is composed of a single fibronectin type II domain and three clusters of tetratricopeptide repeat ( TPR ) -like motifs , the Sel1-repeats ( Figure 5A ) [39] , [40] , [49] , [50] . The p . Ser658Pro amino acid change is positioned in one out of the several Sel1-repeat motifs of the protein ( Figure 5A ) . We found Ser658 to be fully conserved in all aligned vertebrates , insects and in the hemichordate acorn worm . Only C . elegans's orthologue Sel1 and yeast's orthologue Hrd3p differed at the position ( Figure 5B ) . Moreover , three sequence homology-based prediction tools , PANTHER , PolyPhen and SIFT , all predicted the Ser658Pro change to have a probably damaging effect on protein function ( PANTHER SubPSEC score = −5 . 4 , PolyPhen-2 ( HumVar-trained ) score = 0 . 98 and the SIFT score = 0 . 02 ) . According to various expression databases , mammalian SEL1L is ubiquitously expressed . However , we wanted to specifically confirm SEL1L cerebellar expression and also to see , if the non-synonymous c . 1972T>C variant has an effect on SEL1L mRNA stability . Amplification and sequencing of the entire SEL1L transcript confirmed cerebellar expression , the presence of the mutation at mRNA level and the predicted exon/intron boundaries , and finally , excluded the possible splicing effects of the several identified intronic variants ( Table S2 ) . Real-time quantification of the SEL1L transcript in cerebellar cortical tissue samples of five affected dogs and a control puppy revealed a 2 . 8-fold increase in the affected dogs ( Figure 6A ) . We also studied the cerebellar expression of the two other genes with segregating non-coding variants , STON2 and CEP12 , but did not find differences in their transcript levels between the control and affected dogs ( Figure 6A ) . SEL1L is a component of an ER-associated protein complex that functions in protein degradation [38]–[41] . Impaired protein degradation is known to affect ER homeostasis and result in ER stress [51] , [52] We therefore hypothesized that the c . 1972T>C change in SEL1L could lead to dysfunction of the protein complex and cause ER stress . Disruption of ER homeostasis activates the unfolded protein response ( UPR ) , which upregulates genes that are required for cell survival during ER stress [53]–[57] . We measured the transcript levels of nine known ER stress responsive genes , ATF6 , CHOP , XBP1 , HSPA5 , HERPUD1 , DNAJB11 ( ERdj3 ) , DNAJC ( p58IPK ) , EDEM1 and HRD1 in cerebellar cortical tissue of five affected FHs and a control dog . For XBP1 , we measured both the unspliced ( XBP1u ) and the spliced ( XBP1s ) transcripts [58]–[60] . All tested UPR genes showed increased expression levels in the affected dogs ( Figure 6B ) . The highest , 14-fold increase was observed for CHOP , whereas 1 . 9 to 4 . 2-fold increases were observed for the other eight UPR genes . These results are indicative of ER stress in the affected cerebellar cortex and support the pathogenicity of the identified SEL1L mutation .
We describe here the clinical and histopathological phenotype of a progressive early-onset cerebellar ataxia in FHs and identify a missense mutation in the SEL1L gene that segregates with the recessive disease . The clinical course in FH ataxia is compatible to the classical canine cerebellar abiotrophy , which has an early-onset , rapid progression and poor prognosis [16] , [17] , [19]–[22] , [25] , [27] , [28] . Affected FHs present with a progressive ataxia that causes significant ambulatory difficulties . The symptoms worsen rapidly and the affected puppies are euthanized soon after diagnosis . Cerebellar degeneration is visible in MRI at the age of 3 months . Histopathological features are consistent with a premature degeneration of cerebellar cortical PCs , where the PCs are the primary target of a degenerative pathological process , followed by secondary changes in other cortical cell layers . We suggest that the now identified SEL1L mutation ( c . 1972T>C , p . Ser658Pro ) is the most likely underlying cause in FH ataxia . Several lines of evidence support SEL1L as the causative gene . First , SEL1L is the best positional and functional candidate in the 1 . 5 Mb disease associated haplotype block . The haplotype contained five known protein coding genes , which were screened for mutations . The only coding variant that causes a protein level change was found in the SEL1L gene . SEL1L shows ubiquitous expression in adult tissues and is widely expressed during embryonic development , with intense expression in developing neural tissue and pancreas [49] , [61] , [62] . We confirmed SEL1L expression in the cerebellar cortex , the affected organ in FH ataxia . Second , the SEL1L protein belongs to a disease-relevant pathway . SEL1L functions in a large protein complex in the ER , in a cellular process referred to as ER-associated degradation ( ERAD ) [38]–[41] . The ERAD machinery targets terminally misfolded and unassembled polypeptides which are recognized , dislocated to the cytoplasm and marked for proteasome-dependent degradation [55] , [63] , [64] . SEL1L is a component in an ERAD complex that is organized around an E3 ubiquitine ligase , HRD1 [38] , [40] , [41] , [65]–[67] . Although the precise function of SEL1L is not known , it is proposed to have an adaptor role and to be involved in recruiting ERAD substrates to the HRD1 ligase complex , either directly or via other proteins [39] , [40] , [65]–[67] . ER stress and impaired protein degradation have been implicated in several neurodegenerative diseases [52] , [68] , [69] . As an indication of acute ER stress in the cerebellar cortex of affected FHs , we show increased expression of several transcription factors and chaperone proteins that belong to the ER stress response pathway , UPR . Increased transcription levels were also found for SEL1L and HRD1 , which are both known to be upregulated in ER stress [70] . However , the highest increase was detected in the expression of a proapoptotic transcription factor CHOP [71]–[73] . CHOP is normally expressed at very low levels but is highly induced in ER stress [74] , [75] . Overall , our expression data supports the pathogenic role of the SEL1L mutation in FH ataxia . There is one previous report that connects SEL1L to a neurodegenerative phenotype . A tentative association was found between an intronic SEL1L SNP and Alzheimer's disease [76] . However , given the early-onset and rapid progression of the disease in FHs , our study indicates that SEL1L has crucial role in the developing brain and suggests that SEL1L is an unlikely candidate gene for late-onset neurodegenerative disorders . This is further supported by a prenatal lethality of Sel1l-deficient mice [77] . Third , the p . Ser658Pro amino acid change hits a highly conserved residue in an evolutionary conserved repeat motif in the SEL1L protein . The affected domain is the next to last carboxy-terminal Sel1-repeat . Unlike the amino-terminal fibronectin type II domain , which is absent from invertebrate SEL1L orthologues , the Sel1-repeats are highly conserved across species [49] . The α/α-helical Sel1-repeats are found in several proteins that function as adaptors in protein complex formation [78] . Deletion of the two most carboxy-terminal Sel1-repeat clusters in mammalian SEL1L have been reported to abolish interactions with HRD1 and other proteins [40] , and recent data suggests that the stabilization of the mammalian SEL1L depends on HRD1 [79] . Sequence homology-based prediction programs all indicated a likely damaging effect of the p . Ser658Pro change . Moreover , there is a considerable difference in structure between the two amino acids , serine and proline . The latter possesses a conformationally restricted cyclic molecular structure , which could interfere with proper protein folding . Given the role of the Sel1-repeats in protein interactions , it is possible that the p . Ser658Pro change disrupts SEL1L protein-protein interactions , for instance with HRD1 , compromising the function of the ERAD complex . Forth , a mouse model connects Sel1l deficiency to impaired ER-mediated protein quality control . A recent paper reported embryonic lethality and signs of systemic ER stress in Sel1l-deficient mice [77] . Heterozygous mice were normal and fertile but those that were homozygous for a gene trap mutation between Sel1l exons 14 and 15 , died at mid-gestation , and suffered from growth retardation and morphological brain abnormalities [77] . Cell lines derived from the mutant mice revealed changes in ER morphology , hypersensitivity to ER stress inducers , defects in degradation of unfolded proteins and an impaired protein secretory pathway [77] . In accordance with our results , several UPR genes were upregulated in the mutant embryos [77] . The embryonic lethality of the Sel1l-deficient mice suggests that the canine mutation is milder and does not abolish all SEL1L function . The affected FH puppies did not show any gross morphological abnormalities and seemed to develop normally over the first few weeks of life . Furthermore , some SEL1L functions might be complemented by protein isoforms [49] , [62] , [80]–[82] . Amino-terminal SEL1L isoforms that contain a variable number of Sel1-repeats but lack the carboxy-terminal region , have been indicated to function in parallel or complementary to the full length SEL1L in ER stress [81] , [82] . Although SEL1L is ubiquitously expressed , another explanation for the cerebellum-restricted pathology may come from the vulnerability of the cerebellar PCs to ER stress . Various disease phenotypes indicate that cerebellar PCs are susceptible to undergo premature degeneration [13] , [14] , [33] , [83] , [84] and accumulating evidence indicate ER stress specifically as causative in PC pathology [85]–[92] . These observations could account for the primary PC degeneration in affected FHs . In addition to our results on SEL1L cerebellar expression , a previous study found HRD1 in murine PCs [93] . It is plausible that a compromised function of the HRD1-ligase pathway would not be tolerated by ER stress sensitive PC and would lead to excessive or prolonged ER stress , triggering apoptosis and neurodegeneration . Whether any pathological changes would be seen in other tissues if the affected dogs were kept alive longer , is not known . In conclusion , we identify a novel progressive ataxia gene and link a defective ERAD pathway to an early-onset cerebellar neurodegeneration . Our study implicates a critical function for the HRD1-SEL1L-mediated ERAD pathway in the postnatal PC survival and provides a novel model to investigate the role of the ER stress in neurodegeneration . SEL1L represents a novel candidate gene for human ataxias and we have initiated mutation screenings in childhood phenotypes . Meanwhile , a genetic test has been offered for FH breeders to identify carriers and to eradicate the disease from the breed .
All the dogs used in this study were privately-owned pets , and the genetic and clinical examinations were approved by the Animal Ethics Committee at the State Provincial Office of Southern Finland ( permits: ESLH-2006-08207/Ym-23 and ESHL-2009-07827 ) . EDTA-blood samples were collected from 13 affected FH puppies that belonged to seven different litters , and from 33 unaffected first-degree relatives . The unaffected relative sample cohort comprised 13 parent dogs and 20 unaffected full-siblings . A pedigree was constructed around the affected dogs by using GenoPro genealogy software ( http://www . genopro . com/ ) . FH population controls ( n = 241 ) and an additional control sample cohort of 349 dogs from 51 other dog breeds were selected among samples stored at the Canine DNA Bank located at Biomedicum Helsinki , Finland . The FH population cohort included random samples from unaffected FHs that had been collected for instance at dog shows . Full-siblings were excluded from the population cohort to obtain a reliable estimate a carrier frequency . The control cohort from other breeds comprised one dog from two breeds , two dogs from 38 breeds and 10 to 32 dogs from 11 breeds ( Table S3 ) . Puregene DNA Purification Kit ( Gentra Systems ) was used to extract genomic DNA from affected dogs and their relatives . A semi-automated Chemagen extraction robot ( Chemagen Biopolymer-Technologie AG ) was used for the control samples . Concentration of DNA samples was determined using a ND-1000 UV/Vis Spectrophotometer ( NanoDrop Technologies ) . General clinical , orthopedical and neurological examinations were performed on ten ataxic FH puppies that were referred to a veterinary neurology clinic during December 2006 and September 2008 . The examined puppies came from six different litters . One healthy sibling was examined as a control dog . Neurological examinations were filmed and are available for retrospective evaluation . CBC and serum biochemistry profiles ( sodium , potassium , calcium , phosphorus , magnesium , glucose , total protein , albumin , globulin , blood urea nitrogen , creatinine , total bilirubin , alanine aminotransferase , aspartate aminotransferase , alkaline phosphatase , and creatine kinase ) were examined on admission . Brain MRI scanning and CSF sample collection were performed under general anesthesia . Intramuscular anaesthesia premedication was carried out with the combination of Medetomidine ( Domitor , Orion Pharma ) 0 . 02 mg/kg and Butorphanol ( Torbugesic , Scan-Vet ) 0 . 1 mg/kg . Anaesthesia was induced through intravenous bolus of Propofol ( Propofolum , Abbott Laboratories ) 6–8 mg/kg and maintained through inhalation of Isoflurane ( IsoFlo vet , Orion Pharma ) and oxygen . A 0 . 2 Tesla MRI scanner ( VetMR , Esaote ) was used to record T1W and T2W images in transversal and sagittal planes on animals placed in sternal recumbency . TW1 images were recorded using 750 . 0 ms repetition time ( TR ) , 26 . 0 ms echo time ( TE ) and a field of view ( FoV ) of 150×150 , 160×160 and 170×170 mm and T2W images with 3000 . 0 ms TR , 90 . 0 ms TE and 160×160 or 170×170 mm FoV . In all images , slice thickness was 4 . 0 , 4 . 5 or 5 . 0 mm and interslice gap 0 . 4–0 . 5 mm . T1W images were repeated immediately after intravenous injection of contrast agent , gadolinium-diethylenetriaminepenta-acetate ( -DTPA ) dimeglumine 0 . 2 mL/kg ( 0 . 1 mmol/kg ) . Two blinded examiners ( SC and JJ ) rated the dogs subjectively as affected or healthy based on cerebellar size , amount of CSF between the cerebellar folia , size of the fourth ventricle and distance between caudoventral edge of the cerebellum and foramen magnum . CSF samples were collected from the cerebellomedullary cistern after the MRI examination . Total cell count and protein concentration were evaluated from the CSF samples and considered normal if there were less than five nucleated cells per microliter and if Pandy reaction was negative . Affected dogs with compatible clinical signs and MRI findings were euthanized with owner's agreement . A complete autopsy was performed on the ten clinically examined dogs . For one additional puppy , only the brain was received for examination . The weight of the cerebellum relative to the total weight of the brain was determined in five dogs . Samples from the CNS , liver , lungs , spleen , kidney and heart were collected and fixed in neutral buffered 10% formalin . Sections from the fixed tissues were embedded in paraffin and processed for light microscopic examination , using the haematoxylin-eosin ( HE ) stain . The CNS was sectioned at nucleus caudatus with cerebral cortex , hippocampus with temporal cortex , mesencephalon at the height of the cranial colliculi , cerebellum transversally and longitudinally with pons and medulla oblongata . Sections from the CNS were stained with luxol fast blue/cresyl echt violet ( LFB-CEV ) to evaluate chromatolysis and myelin loss . An IHC stain for glial fibrillary acidic protein ( GFAP , MCA 1909 , Serotec ) was used to assess astrogliosis . IHC staining of spleen , lung and kidney sections were performed for canine distemper virus ( CDV , MCA 1893 , Serotec ) , and sections of spleen were stained for canine parvovirus ( CPV , MCA2064 , Serotec ) . Altogether 24 known human and murine ataxia genes were selected for a microsatellite marker-based candidate gene analysis ( Table S1 ) . Segregation of microsatellite markers was examined in three nuclear families , comprising six parents , five affected dogs and three healthy siblings . Allele sizes were determined by fragment analysis . Human and murine mRNA sequences for the candidate genes were obtained from the GeneBank database ( http://www . ncbi . nlm . nih . gov/ ) and the corresponding canine sequences were identified from the CanFam2 . 0 annotation using the BLAT search tool [94] . Microsatellite primers ( Table S4 ) were designed using Primer 3 ( http://frodo . wi . mit . edu/primer3/ ) . Forward primers were either directly labeled with a fluorescence dye ( HEX or FAM ) or alternatively , an M13-tail sequence was added to the 5′-end and used together with a third , FAM-labeled M13-primer [95] . PCR amplifications were performed using a PTC-225 Peltier Thermal Tetrad Cycler ( Bio-Rad ) and a standard PCR protocol . Reactions that included directly labeled forward-primers were performed in a reaction volume of 10 µl with 20 ng of genomic DNA , 1 X PCR buffer , 2 . 5 mM MgCl2 , 0 . 2 mM dNTPs , 0 . 5 µM of forward- and reverse primers and 0 . 375 units of AmpliTaq Gold Polymerase ( Applied Biosystems ) . Amplifications that were performed using the M13-primers were carried out in a 12 µl volume containing 15 ng of genomic DNA , 1 X PCR buffer , 2 . 1 mM MgCl2 , 0 . 33 mM dNTPs , 0 . 05 µM M13-tailed forward primer , 0 . 25 µM reverse primer , 0 . 2 µM M13 primer and 1 . 2 units of Biotools DNA polymerase . Intensity of PCR products was evaluated from 1% agarose gel stained with 0 . 5 µg/ml ethidium bromides ( Amresco ) . Fragment analysis runs were performed on a 3730xl DNA Analyzer ( Applied Biosystems ) . The Peak Scanner software ( Applied Biosystems ) was used to determine allele sizes . Thirteen affected dogs from seven nuclear families and 18 related control dogs were genotyped using Illumina's CanineSNP20 BeadChip of 22 , 362 validated SNPs . Healthy control dogs included 11 obligate disease carrier parents and seven non-affected siblings ( Figure 3 ) . Genotype data was filtered using a SNP call rate of >95% , an array call rate of >95% and minor allele frequency of >5% . Based on these criteria , 289 SNPs were removed for low genotyping efficiency and 6739 SNPs for low minor allele frequency . Mendel errors were detected in 35 SNPs , which were removed from analyses . No samples were removed for low genotyping and no SNPs for significant deviations from the Hardy-Weinberg equilibrium ( p≤0 . 0001 ) . After the filtering steps , 15 , 299 SNPs remained for analyses . A basic case-control association test was performed by using the software package PLINK [36] . Obligate carrier parents were excluded from the case-control association test and the remaining seven healthy siblings were used as controls . Genome-wide significance was ascertained through phenotype permutation testing ( n = 100 , 000 ) . Pseudomarker program was used to perform family-based testing on CFA8 , which showed genome-wide significant association in the PLINK analysis [37] . The family-based analyses were performed under a recessive inheritance model , and included parametric single-point linkage test , association analysis ( LD|Linkage ) and joint analysis ( LD+Linkage ) . Mutation screening of CEP128 , TSHR , GTF2A1 , STON2 and SEL1L exons , exon-intron junctions and 5′ and 3′ UTRs was performed using samples from two affected dogs and two obligate disease carriers . Primers ( Table S5 ) were designed by using Primer 3 ( http://frodo . wi . mit . edu/primer3/ ) . PCR reactions were carried out in a total reaction volume of 20 µl with 20 ng of genomic DNA , 1 X PCR buffer , 2 mM MgCl2 , 0 . 2 mM dNTPs , 0 . 5 µM of forward- and reverse primers and 0 . 5 units of Biotools DNA Polymerase . PCR amplification was performed using a PTC-225 Peltier Thermal Tetrad Cycler ( Bio-Rad ) and a standard PCR protocol . The reaction products were run on a 1% agarose gel stained with 0 . 5 µg/ml ethidium bromide ( Amresco ) . PCR products were purified with ExoSAP-IT ( GE Healthcare ) and sequenced using an Applied Biosystems' 3730xl DNA Analyzer . Sequence Scanner v1 . 0 and Variant Reporter v1 . 0 ( Applied Biosystems ) were used to assess sequence quality and identify polymorphisms . Control sample cohorts were screened by using Applied Biosystems' TaqMan chemistry and 7500 Fast Real-Time PCR instrumentation . The genotyping reactions were carried out in a 10 µl reaction volume with 1 X TaqMan genotyping assay ( Applied Biosystems ) , 1 X Taqman Genotyping Master Mix ( Applied Biosystems ) and 10 ng of genomic DNA . Primer sequences for the Taqman assay were 5′- CGTAGACTACGAGACTGCATTTATTCA -3′ for the forward , and 5′ - GATTAAACATAGCTTGTGCACTGTGT - 3′ for the reverse primer . Probe sequences were 5′ - TGCTGCTCAGATGCTA - 3′ and 5′ - TGCTGCTCAGGTGCTA - 3′ , labeled with VIC and FAM , respectively . Pfam protein families database ( http://pfam . sanger . ac . uk/ ) [96] and the SMART tool ( http://smart . embl-heidelberg . de/ ) [97] , [98] were used to confirm the protein domain structure of canine SEL1L . The ClustalW2 algorithm was used to compose a multiple sequence alignment to examine cross-species conservation ( http://www . ebi . ac . uk/Tools/clustalw2/ ) . Three sequence homology-based software tools , PANTHER ( http://www . pantherdb . org/tools/ ) [99] , [100] , PolyPhen-2 ( http://genetics . bwh . harvard . edu/pph2/ ) [101] and SIFT ( http://sift . jcvi . org/ ) [102]–[104] , were used to predict the potential functional impact of the identified non-synonymous variant . The PANTHER tool has a score range from 0 to about −10 , with a cutoff for functional significance at ≤−3 . The PolyPhen-2 score ranges from 0 to 1 , with the threshold for probably damaging at 0 . 85 . The SIFT score ranges from 0 to 1 , and substitutions are predicted to affect function if the score is ≤0 . 05 . Tissue samples were collected from the cerebellar cortex of five affected puppies immediately after euthanasia . Samples were stabilized in RNAlater reagent ( Ambion , Inc ) and stored in −80°C . Total mRNA extraction was performed using the RNeasy Mini Kit ( Qiagen ) with a DNase I digestion step included ( RNase-Free DNase Set , Qiagen ) . Concentration of total RNA was measured using a ND-1000 UV/Vis Spectrophotometer ( NanoDrop Technologies ) . Reverse-transcriptase ( RT ) -PCR was carried out on equal amounts of RNA in each sample by using the High Capacity RNA-to-cDNA Kit ( Applied Biosystems ) . A cerebellar control sample was obtained from an 11 days old Saluki puppy that was put down due to a peritoneo-pericardial hernia . SEL1L mRNA sequencing primers ( Table S5 ) and all qPCR primers ( Table S6 ) were designed using Primer 3 ( http://frodo . wi . mit . edu/primer3/ ) . If possible , forward and reverse primers were positioned in different exons to help control for genomic DNA contamination . SEL1L mRNA amplification reactions and sequence analysis were performed as described for mutation screening . Real-time quantitative PCR was performed by using the Applied Biosystems' 7500 Fast Real-Time PCR instrumentation and Roche's FastStart Universal SYBR Green Master . A total reaction volume of 20 µl was used , together with a 0 . 25 µM concentration of forward- and reverse primers . Two house-keeping genes , GAPDH and YWHAZ , were used as normalization controls , and triplicate samples were used for all reactions . The efficiency of the qPCR reactions was calculated from a five-point dilutions series . No significant differences were detected in the efficiencies between the house-keeping and target reactions , and the comparative ΔΔCt-method could be used to determine relative expression differences [105] . Statistical significance of the expression differences was calculated by using the Student's t-test on normalized mean cycle threshold ( Ct ) -values . PASW Statistics 18 software ( IBM ) was used to perform the statistical tests .
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Hereditary ataxias are a heterogeneous group of rare disorders characterized by progressive cerebellar neurodegeneration . Several causative mutations have been identified in various forms of human ataxias . In addition to humans , inherited ataxias have been described in several other species , including the domestic dog . In this study , we have studied the clinical and genetic properties of cerebellar ataxia in the Finnish Hound dog breed . The breed suffers from a progressive ataxia that has an early onset before the age of 3 months . Affected puppies have difficulties in coordinating their movements and balance , and have to be euthanized due to rapidly worsening symptoms . Our pedigree analysis suggested an autosomal recessive mode of inheritance , which was confirmed by identifying a homozygous mutation in the SEL1L gene through genome-wide association and linkage analyses . The SEL1L protein functions in a protein quality control pathway that targets misfolded proteins to degradation in the endoplasmic reticulum . Mutations in the SEL1L gene have not been previously found in ataxias . Our study indicates SEL1L as a novel candidate gene for human childhood ataxias , establishes a large animal model to investigate mechanisms of cerebellar neurodegeneration , and enables carrier screening for breeding purposes .
|
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"Abstract",
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"Results",
"Discussion",
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"Methods"
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"animal",
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"genome-wide",
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2012
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A SEL1L Mutation Links a Canine Progressive Early-Onset Cerebellar Ataxia to the Endoplasmic Reticulum–Associated Protein Degradation (ERAD) Machinery
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New drugs are needed to treat visceral leishmaniasis ( VL ) because the current therapies are toxic , expensive , and parasite resistance may weaken drug efficacy . We established a novel ex vivo splenic explant culture system from hamsters infected with luciferase-transfected Leishmania donovani to screen chemical compounds for anti-leishmanial activity . This model has advantages over in vitro systems in that it: 1 ) includes the whole cellular population involved in the host-parasite interaction; 2 ) is initiated at a stage of infection when the immunosuppressive mechanisms that lead to progressive VL are evident; 3 ) involves the intracellular form of Leishmania; 4 ) supports parasite replication that can be easily quantified by detection of parasite-expressed luciferase; 5 ) is adaptable to a high-throughput screening format; and 6 ) can be used to identify compounds that have both direct and indirect anti-parasitic activity . The assay showed excellent discrimination between positive ( amphotericin B ) and negative ( vehicle ) controls with a Z' Factor >0 . 8 . A duplicate screen of 4 chemical libraries containing 4 , 035 compounds identified 202 hits ( 5 . 0% ) with a Z score of <–1 . 96 ( p<0 . 05 ) . Eighty-four ( 2 . 1% ) of the hits were classified as lead compounds based on the in vitro therapeutic index ( ratio of the compound concentration causing 50% cytotoxicity in the HepG2 cell line to the concentration that caused 50% reduction in the parasite load ) . Sixty-nine ( 82% ) of the lead compounds were previously unknown to have anti-leishmanial activity . The most frequently identified lead compounds were classified as quinoline-containing compounds ( 14% ) , alkaloids ( 10% ) , aromatics ( 11% ) , terpenes ( 8% ) , phenothiazines ( 7% ) and furans ( 5% ) . The ex vivo splenic explant model provides a powerful approach to identify new compounds active against L . donovani within the pathophysiologic environment of the infected spleen . Further in vivo evaluation and chemical optimization of these lead compounds may generate new candidates for preclinical studies of treatment for VL .
New drugs are desperately needed to treat visceral leishmaniasis ( VL ) , and this in turn requires new approaches to discover novel lead compounds that might populate a pipeline of new therapeutics for patients with VL . Current therapies for the leishmaniases are toxic , difficult to deliver , expensive , and their efficacy is hindered by parasite resistance ( reviewed in [1] ) . The pentavalent antimony compounds , sodium stibogluconate and meglumine antimoniate , have been the mainstay of anti-leishmanial chemotherapy for more than 40 years . The recommended regimen involves prolonged and often repeated courses of drug administered by the intravenous or intramuscular routes . Cure rates of 80–100% were common in the 1990s , but have dropped off considerably because of parasite resistance [2] . Adverse effects of antimony therapy are multiple and often dose-limiting . Amphotericin B desoxycholate and the amphotericin lipid formulations are also used in the treatment of VL , and in many regions have replaced antimony as first-line therapy . The use of these drugs , however , is limited by their difficulty of administration , well-known risk of toxicity , and high cost . Parenteral treatment of VL with the aminoglycoside paromomycin ( aminosidine ) is used in India but not licensed in the U . S . Miltefosine , a membrane targeting alkylphospholipid , was recently licensed in India as the first oral treatment for VL , but after only a few years of use , drug resistance has emerged . The discovery of new drugs for VL would have huge impact on individual patients and on populations in the endemic area as a whole . Pre-clinical in vitro studies to identify candidate drugs for treatment of leishmaniasis have employed several different approaches , each of which has significant limitations . The testing of drugs using axenically cultured parasites , usually promastigotes , has been most commonly used , but this approach is limited by 1 ) the discordance of anti-leishmanial activity of compounds tested in axenically cultured promastigotes ( vector stage ) and amastigotes ( mammalian stage ) [3] , [4] , and 2 ) the testing of antiparasitic activity in the absence of host immune cells , which are known to profoundly influence parasite replication or killing [5] , [6] , [7] , [8] . The use of cultured macrophages that have been infected in vitro with Leishmania has the advantage of identifying drugs that are active against the intracellular amastigote , but in vitro infections are technically cumbersome , may have a variable number of promastigotes that remain attached but not internalized by macrophages , and the isolated macrophage-amastigote infection excludes other immunomodulatory host cells such as disease promoting or regulatory T cells ( reviewed in [9] ) . Pre-clinical in vivo studies have largely used murine models of Leishmania infection , however , L . donovani infection in mice does not fully represent the features of active VL , and thus arrest or cure of severe , progressive disease and death cannot be an endpoint in therapeutic trials in mice . Strikingly , Syrian hamsters recapitulate the progressive clinicopathological features of human VL [10] , [11] , [12] , most notably the profound immunosuppression associated with active disease that has fundamental importance to effective therapy . During progressive disease in the hamster model of VL , a Type 1 T cell response is mounted , but paradoxically it is ineffective [11] , [12] . This contrasts sharply with the mouse model , but is very similar to what has been demonstrated in humans with VL [13] , [14] , who also mount an ineffective Type 1 response . Most notably , progressive disease in hamsters is accompanied by low NOS2 expression [11] , and infected hamster macrophages , akin to human macrophages [15] , [16] produce very low levels of NO [17] . In striking contrast , activated mouse macrophages produce high levels of NO . Thus , the macrophage defense against intracellular pathogens in hamsters is uniquely similar to what is observed in human macrophages . Because of the profound influence of the host immune response on the treatment of Leishmania infection we sought to develop a test system that included the immunopathological milieu found at the site of the host-parasite interaction and active disease . This would enable the activity of new compounds to be determined within the context of the pathogenic mechanisms that contribute to progressive disease . To accomplish this , an ex vivo explant culture of spleen cells from L . donovani infected hamsters was established from hamster spleens at a point in the course of infection ( day 21 post-infection ) when disease and parasite replication are dramatically increased . This transition to explosive parasite replication and progressive disease is accompanied by a loss of T cell responsiveness ( as occurs in human disease ) and the development of an alternatively activated macrophage phenotype . The use of a parasite strain that expressed luciferase enabled determination of parasite killing by a large number of compounds in a medium- to high-throughput format . This approach enabled the identification anti-leishmanial drug candidates that are active in the face of the disease-promoting immune response , as must occur in the treatment of human VL .
Female inbred Chester Beatty hamsters ( 6–8 weeks-old ) derived from our own breeding colony were used . Leishmania donovani ( MHOM/SD/001S-2D ) promastigotes were cultured in complete M199 ( 0 . 12 mM adenine , 0 . 0005% Hemin , 20% FBS ) as described previously [18] . The L . donovani strain was transfected with an episomal vector containing the luciferase ( luc ) reporter gene [19] and was maintained routinely by isolation from infected hamsters , selection in complete M199 with 10 µg/ml of G418 , and intracardial subinoculation of new hamsters approximately every 3 months . These studies were reviewed and approved by the Institutional Animal Care and Use Committee of the University of Texas Health Science Center . Groups of 8 animals were infected by the intracardial route with 106 purified metacyclic promastigotes [11] and the body and spleen weights recorded at 7 , 14 and 21 days post-infection . At each time point , the splenocytes were harvested as described below and the number of cells and parasite burden determined by microscopy and luminometry , respectively . The luminometry counts were transformed to number of parasites using a linear standard curve of luciferase activity versus number of microscopically enumerated amastigotes . The amastigotes used for the standard curve were isolated from a cell suspension from hamster spleen at 20–30 days p . i . as follows: A splenocyte suspension was obtained by passing the spleen through a wire mesh and then the splenocytes were disrupted by passing sequentially through 27G½ and 30G½ needles , and polycarbonate membrane filters having pore sizes of 8 µm , 5 µm and 3 µm ( Isopore , Millipore ) . Released amastigotes ( free of host cells ) were washed twice with PBS ( 3000 g ×10 min ) and enumerated by direct microscopy . The alternative activation of macrophages was assessed by determining the arginase activity by the rate of urea formation from L-arginine in the presence of 1-phenyl-1 , 2-propanedione-2-oxime ( ISPF ) [20] and measuring the soluble collagen content ( Sircol Assay , Biocolor Ltd ) . The cellular immune response was evaluated by the proliferative response of spleen cells to Concanavalin A as described [21] . The general cell composition of the spleen explant was determined by microscopy and flow cytometry at 7 , 14 and 21 days post-infection and compared with uninfected spleen cells . The spleen cells were suspended in DMEM +5% FBS at 106 cells/100 µl , washed with PBS plus 0 . 1% BSA and 0 . 025% sodium azide , blocked for 20 min with PBS with 2% BSA and 5% of normal serum of the species in which the secondary antibodies were raised , and labeled with the monoclonal antibodies that are known to cross react with the corresponding hamster molecules . CD4 T cells were quantified by staining with rat anti-mouse CD4-PE ( clone GK1 . 5; BD , 0 . 5 µg/tube ) [22] followed by fixation , permeabilization ( Leucoperm; Serotec ) , and labeling with rat anti-human CD3-FITC ( CD3-12 , Serotec , 0 . 5 µg per tube ) which recognizes a highly conserved intracellular epitope of the CD3 molecule expressed by T lymphocytes in several species ( manufacturer's data ) . B lymphocytes were quantified by labeling cells that expressed both the MHCII alloantigen ( mouse anti-mouse I-E[k]-PE [clone 14-4-4S] , BD , 0 . 5 µg/tube ) [23] and IgG ( Goat F ( ab' ) 2 anti-hamster IgG ( clone Dlight 488; Serotec , 1 µg per tube ) . In all cases the percent of positive cells was determined by flow cytometry ( FacsAria , BD ) according to the threshold of the corresponding isotype controls . The number of parasites and the proportion of infected macrophages were determined after 0 , 24 , 48 and 72 hours of ex vivo culture at 37°C , 5% C02 using luminometry and light microscopy . The ability of the luminometric assay to discriminate between treated and untreated ( control ) splenic explant cultures was assessed in serial two-fold dilutions of cells cultured for 48 h in 100 µl of 0 . 2 µM amphotericin B ( Sigma ) or vehicle control . To establish the plate assay for drug screening , the spleens from 2 infected hamsters were aseptically removed and placed in a Petri dish containing 5 mL of Collagenase D ( Roche ) at 2 mg/mL of buffer ( 10 mM Hepes pH 7 . 4 , 150 mM NaCl , 5 mM KCl , 1 mM MgCl2 , 1 . 8 mM CaCl2 ) . The spleen was infiltrated by injection with approximately 2 mL of Collagenase solution , the tissue was cut into small pieces using sharp scissors , and incubated for 20 minutes at 37°C . The cell suspension and remaining tissue fragments were gently passed through a 100 µm cell strainer ( B–D ) to obtain a single cell suspension , which was washed twice by centrifugation ( 500 g for 7 min at 4°C ) and re-suspended in culture medium composed of DMEM ( Gibco ) , 5% FBS , 1 mM Sodium pyruvate ( Gibco ) , 1X MEM amino acids solution ( Sigma ) , 0 . 02% v/v/EDTA , 10 mM HEPES buffer ( Cellgro ) and 100 IU/mL Penicillin/100 mg/mL Streptomycin solution ( Cellgro ) . The splenocytes were counted and adjusted at concentrations from 100 , 000 to 500 , 000 cells/50 µL and used the same day for drug screening or determination of EC50 as described below . The following chemical libraries in 96-well plate format were screened: NINDS Custom Collection II ( MicroSource Discovery Systems , Inc . ) , which consists of 1 , 040 classical therapeutic agents , established experimental inhibitors , receptor agonist drugs and other bioactive compounds [24] , [25] , [26]; the Pure Natural Products library ( MicroSource Discovery Systems , Inc . ) a collection of 800 pure natural products and derivatives; and The Diversity Set and Natural Products set ( Developmental Therapeutics Program , NCI/NIH ) a set of 2 , 195 compounds selected from the almost 140 , 000 compounds based on diversity of structure [27] , [28] , [29] . Libraries containing the compounds at 10 mM concentration in DMSO were diluted into master plates at 200 µM concentration in DMEM . The 200 µM master plates were further diluted by transferring an aliquot of 5 µl into a 96-well sterile white-bottom plate ( Costar ) containing 45 µl of culture medium at 4°C . Splenocytes ( 100 , 000 in 50 µl of culture medium ) that had been obtained from infected hamsters as described above were added to the assay plate for a final drug concentration of 10 µM . The positive control ( splenocytes treated with 0 . 2 µM Amphotericin B ) and negative controls ( 0 . 1% DMSO vehicle ) were distributed in the two outer columns ( 8 wells each ) of each plate in alternating fashion to minimize any edge effect [30] . After 48 hours of culture at 37°C in a humidified atmosphere and 5% CO2 , the plates were centrifuged at 500 g for 7 min , the supernatant discarded using a multi- channel vacuum aspirator ( Costar ) , and 20 µl of 1X cell culture lysis reagent ( Promega ) was added to the cells . To complete the lysis procedure the plates were frozen at −70°C and then thawed , and the luciferase activity determined in a plate luminometer following addition of 100 µl of the luciferin substrate at room temperature ( Promega ) . The Z score was calculated for each compound as the mean counts of the compound tested minus the mean counts of all compounds in the plate divided by the SD all compounds in the plate [30] . The mean Z score for each drug was calculated based on duplicate screenings performed in two different experiments . A Z score of ≤–1 . 96 , which corresponds to a p value of ≤0 . 05 ( 95% confidence limit ) , was used as the threshold to identify the hits [30] . The conditions of the screening were optimized to obtain the best signal to noise ratio by calculating the Z factor obtained after exposure to different drug concentrations ( 10 , 5 , 2 . 5 µM ) . The quality of each assay was determined by calculating the Z prime ( Z' ) factor [31] , which measures the discrimination between positive and negative controls in the screen . The Z' factor was calculated as 1 – [ ( 3SD positive controls +3SD negative controls ) /absolute value of ( mean of the positive controls – mean of the negative controls ) ] . We used a cell-based assay as an alternative to animal testing to determine the toxicity of the identified hit compounds [32] . The cytotoxicity was evaluated in HepG2 cells ( human hepatocellular carcinoma , ATCC#HB 8065 ) maintained in MEM ( Gibco ) supplemented with 10% FBS 1 mM sodium pyruvate ( Gibco ) , 1X MEM aminoacids solution ( Sigma ) , 100 IU/mL penicillin , and 100 mg/mL streptomycin ( Cellgro ) . Cell monolayers were detached using 1X trypsin/EDTA ( Gibco ) , washed and adjusted to 500 , 000 cells/mL in supplemented MEM , and 50 µl of the cell suspension were added to white-bottom 96-well plates ( 25 , 000 per well ) containing 50 µl of serial 2-fold dilutions of the test compounds ( 0 . 1–100 µM ) or the DMSO control . After 24 hours of culture at 37°C the number of viable cells was determined by quantification of the ATP present in the cell using the CellTiter-Glo luminescent Cell Viability Assay ( Promega ) . The luminescence values were used to construct a curve using a linear regression model ( GraphPad Prism 5 . 0 ) and calculate the cytotoxic concentration that killed 50% of the cells ( CC50 ) . The mean and standard error of 3–5 different experiments were considered as the final CC50 for the purpose of calculating the in vitro therapeutic index ( see below ) . The anti-leishmanial efficacy of the compounds was determined using the splenic explant model in a 96-well plate format . In brief , splenocytes from infected hamsters were obtained as described above and a suspension of 100 , 000–500 , 000 in 50 µL of culture medium were added to white-bottom 96-well plates containing 50 µl of serial 2-fold dilutions of test compounds ( 0 . 03–20 µM ) in culture medium . Because variation in the parasite burden between different explant cultures was expected , the concentration of cells used in each experiment was adjusted to give approximately 100 counts by luminometry ( equivalent to ∼240 , 000 parasites ) . The number of surviving parasites was determined by luminometry as described above . Luminometry values were used to construct a curve using a linear regression model ( GraphPad Prism 5 . 0 ) and calculate the effective concentration of the compound that killed 50% of the parasites ( EC50 ) . The mean and standard error of the EC50 from 2–3 different experiments were considered as the final EC50 for the purpose of ranking the compounds and calculating the in vitro therapeutic index ( see below ) . Macrophages from uninfected hamsters were obtained by peritoneal lavage with DMEM ( Gibco ) and Heparin ( 2 units/ml; Elkins-Sinn , Cherry Hill NJ ) . The cells were washed twice by centrifugation and re-suspended in culture medium composed of DMEM , 5% FBS , 1 mM sodium pyruvate ( Gibco ) , 1X MEM amino acids solution ( Sigma ) , 0 . 02% v/v/EDTA and 10 mM HEPES buffer ( Cellgro ) . The peritoneal macrophages were adjusted to 5×105 cells/ml and allowed to adhere overnight at 37°C and 5% CO2 in flat bottom 96-well plates . Adherent macrophages were infected at 1∶5 ratio ( cells:parasites ) with stationary phase LUC-transfected L . donovani promastigotes and cultured at 37°C , 5% CO2 , for 2 h . The extracellular parasites were then removed by washing with warm Dulbeco's PBS ( Gibco ) . Infected macrophages were exposed to 2-fold serial dilutions of test compounds as described above for the ex vivo culture system . The infected macrophages were incubated for 48 hr in 5% CO2 atmosphere at 37°C . The number of surviving parasites was determined by luminometry and the effective concentration ( EC50 ) calculated by linear regression ( GraphPad Prism 5 . 0 ) as described above for the ex vivo system . The mean and standard error of the EC50 from 3 different experiments were considered as the final EC50 . The test compounds Amphotericin B , Miltefosine , Pentamidine , Fluconazole , Antimycin A , Disulfiram , Monensin A and Nortriptyline were purchased from Sigma , and Meglumine antimoniate ( Glucantime® ) was obtained from Aventis Pharma ( Brazil , Lot: L503451 ) . Tilorone was obtained from Hangzhou Trylead Chemical Tech ( China ) . All compounds were dissolved in sterile dimethyl sulfoxide ( analytical grade , cell culture tested; Sigma ) and stored frozen at −20°C until used . Hit compounds that showed a significant Z score in the ex vivo screening but demonstrated high toxicity for the HepG2 cell line ( CC50 ≤10 µM ) were excluded from further consideration . After the exclusion of these toxic compounds , the in vitro therapeutic index ( IVTI ) , which is the ratio of the CC50 obtained in the HepG2 cell line and the anti-leishmanial activity ( EC50 ) , was calculated for each of the hit compounds . The resulting IVTI was used to rank the hits and select lead compounds that had an IVTI >5 .
We first characterized the clinical , immunological , and parasitological features of the infected hamster spleen early in the course of VL ( which is ultimately fatal ) to identify a time point that indicated the course of infection was transitioning toward severe disease . At 21 days p . i . there was a dramatic increase in spleen size ( Fig . 1A ) , parasite burden ( Fig 1B ) , and cellular infiltration of the spleen ( Fig 1C ) . The splenomegaly was related to hypercellularity with a significant expansion of the macrophage population ( 6-fold increase over uninfected spleens , p = 0 . 016 ) and B lymphocytes ( Table 1 ) . The expansion of other cell populations that were not enumerated , such as fibroblasts , is likely to also contribute to the splenomegaly . The transition to progressive disease was also accompanied by loss of T cell responsiveness ( Fig 1D ) and preceded by a transient decrease in the percent of splenic CD3+ cells and CD4+ T cells at 14 days p . i . ( Table 1 ) . At 21 days p . i . there was also evidence of alternative macrophage activation with a significant increase in the amount of soluble collagen synthesis ( Fig 1E ) and arginase activity ( Fig . 1F ) . Therefore , we selected day 21 p . i . as the time point to establish the ex vivo splenic explant culture so that the screening of small molecules for anti-Leishmania activity would be conducted within the milieu of the immunopathological mechanisms that lead to progressive VL . For the ex vivo assay to effectively identify compounds that had inhibitory or leishmanicidal activity it was critical that the cultured splenocytes support the replication of the parasite in the absence of active drug over the course of the ex vivo culture . Additionally , since the luciferase-expressing episomal vector could be gradually lost after hamster inoculation , we had to establish the feasibility of quantifying parasite numbers in the ex vivo system . We found that the luciferase activity of amastigotes strongly correlated with the number of parasites counted by microscopy at the different times post-infection ( R2 = 0 . 99 , p<0 . 0001; Figure 2A ) . Luminometry clearly detected amastigote viability in the ex vivo system and most importantly demonstrated that the parasite numbers increased over 48 hrs of culture in the absence of test compound ( p<0 . 05 , at 48 h; Fig 2B ) , indicating ongoing replication of the parasite . This was confirmed by microscopic demonstration of an increase in the percentage of infected macrophages ( Fig . 2C ) and an increase in number of amastigotes per macrophage over the course of the culture ( Fig . 2D and compare Fig . 2E to 2F ) . Because the increase in luminometric counts leveled off at 48 hrs of ex vivo culture the drug screening and EC50 determinations were completed at this time point . However , empiric testing of a subset of compounds in 72 and 96 hr splenic explant cultures showed that culturing the explants with and without the test compound for up to 96 hrs had no effect on the calculated EC50 . The 48 hr ex vivo culture also showed good discrimination between drug-treated and untreated control wells . The percent reduction in parasite burden in amphotericin B treated ex vivo splenic explants remained relatively constant over a range of 3 , 000–200 , 000 splenocytes per well ( Table 2 ) indicating that there was considerable flexibility in the number of cells used for the screening assay . For the screening of chemical libraries 100 , 000 splenocytes per well were used . Thus , the number of splenocytes obtained from a single 21-day infected animal was sufficient to screen 13 plates containing a total of 1 , 040 compounds . The data in Table 2 also indicate that in situations where the number of animals were limited , or a very large number of compounds were going to be screened , the number of cells per well could be reduced substantially without compromising the discrimination between active and inactive compounds . We also found that variation of the ex vivo splenocyte number from 100 , 000 to 500 , 000 cells per well had no effect on the calculated EC50 of a test compound ( data not shown ) . To confirm the quality of the assay in discriminating active from inactive compounds we calculated that the Z prime ( Z' ) factor [31] in 3 different screening experiments using 36 different plates . The resulting value of 0 . 72±0 . 02 indicated that the assay could be considered as optimal ( optimal assay , Z factor ≥0 . 5 but ≤1 . 0 ) [31] . To select the optimal drug concentration for the assays we compared the number of hits obtained after screening 80 different compounds at 10 , 5 and 2 . 5 µM . Each of the concentrations gave similar numbers of hits ( data not shown ) , so we chose the 10 µM concentration to be inclusive of as many hits as possible and limit the skewing of the hit selection toward toxic compounds . The initial screening using the ex vivo model showed that 239 of 4 , 035 compounds ( 5 . 9% ) had a Z score of ≤–1 . 96 ( equivalent to a p≤0 . 05 ) and could be qualified as screening ‘actives’ ( Table 3 ) . We did not observe microscopic evidence of cell death in the splenic explant cultures after 48 hrs of culture with the test compounds . Flow cytometry of propidium iodide stained splenocytes cultured with three representative test compounds ( amphotericin B , tilorone , disulfuram ) confirmed no loss of splenocyte viability over the 48 hr culture period ( data not shown ) . To quantify cytotoxicity of a test compound we utilized an established cell line-based assay . We first excluded compounds that fell below an arbitrary cytotoxicity threshold in the HepG2 cell line ( CC50< 10 µM ) , and after exclusion of 37 ( 1% ) such ( toxic ) compounds , 202 ( 5% ) hits were left for further validation studies ( Table 3 ) . For each of these hits , anti-leishmanial activity ( EC50 ) was determined in the ex vivo splenic explant system . Comparison of these EC50 values with cytotoxicity ( CC50 ) values in the HepG2 cell line allowed determination of an in vitro therapeutic index ( IVTI: CC50/EC50 ) . Based on a threshold IVTI score ≥5 [33] , 84 compounds ( 2 . 1% of the total number of molecules screened ) were identified as lead compounds ( Tables 3 , 4 , S1 ) . Of the 84 lead compounds , 15 ( 17% ) had been shown previously to have anti-leishmanial activity ( Table S2 ) and 69 ( 83% ) had not been reported previously as having activity against Leishmania ( Tables 4 , S1 ) . A substantial number of the latter , however , had been shown previously to have anti-infective activity against other classes of pathogens , while others were known as immune regulators , antidepressant , antipsychotics , or had no known function ( Table 5 ) . In general , heterocyclic compounds were most highly represented among the lead compounds , comprising 55% of the total ( Table 4 ) . Eleven percent of the lead compounds were single-ring heterocyclic structures , 21% had 2-ring structures , 14% were classified as quinolines , and 7% were 3-ring phenothiazines . Ten percent of the leads were alkaloids and 20% were hydrocarbon structures composed of aromatics and terpenes ( Tables 4 , S1 ) . The chemical libraries screened included large numbers of known bioactives and drugs so it was not surprising that 27 of 84 ( 32% ) leads had been previously used clinically . Eleven of the lead compounds are recommended for topical use only ( Table 5 ) . Three known anti-leishmanial drugs , fluconazole , pentamidine and miltefosine included in the libraries surprisingly did not show a significant Z score and were not identified as hit compounds in the screening . To understand the reason behind this finding , we determined the EC50 of these and other anti-leishmania drugs and lead compounds in both the ex vivo splenic explant and in vitro macrophage infection models ( Table 6 ) . Repeated testing of Miltefosine from the NCI chemical library found it to be inactive , but testing of freshly solubilized compound from a different commercial source was found to be highly active ( EC50 = 1 µM ) . Thus it would appear that the miltefosine in the NCI library had degraded to an inactive form . Similarly , the EC50 calculated for the amphotericin B present in the library ( in DMSO vehicle ) was 10 . 7±0 . 9 µM , whereas freshly solubilized amphotericin B deoxycholate ( Sigma ) had an EC50 of 0 . 24±0 . 02 µM . In the case of pentamidine , the Z score of −1 . 87 was just outside the threshold for statistical significance ( a Z score of −1 . 96 is equivalent to p = 0 . 05 ) and determination of the EC50 for pentamidine revealed that it was active in the ex vivo splenocytes system ( EC50 = 3 µM ) . Fluconazole had no activity detected by either the screen or determination of the EC50 . Collectively these data indicate that the ex vivo system is a robust approach to identification of new compounds , but that like any screen , it is only as good as the quality of the compounds ( libraries ) screened . To further validate the ex vivo splenic explant model for drug screening we determined the EC50 of a subset of 10 compounds ( including 5 known anti-leishmania drugs ) using both the ex vivo and in vitro infected macrophage systems . Infected splenocytes and in vitro infected macrophages were cultured under the same conditions for 48 hrs in the presence or absence of serial dilutions of test compound . We found good correlation between the two systems ( R2 = 0 . 78; p = 0 . 027 ) .
New drugs are desperately needed for the treatment of VL , and innovative approaches are needed to identify new lead compounds and classes of compounds that can enter the pipeline of lead optimization and therapeutic testing . We describe here a novel approach to drug discovery for VL . We sought to develop a model system through which the activity of test compounds could be determined within the physiological and immunological environment of the site of infection . Since the host immune response is known to have profound influence on the treatment and outcome of Leishmania infection , and VL is characterized by suppression of cellular immune function , we felt it was critical that new compounds be screened for activity within the immunopathological milieu found at the site of the host-parasite interaction during active disease . The ex vivo splenic explant culture used for the drug screening reported here was established from L . donovani infected hamsters that demonstrated the immunopathological features of active VL in that 1 ) there was enlargement of the spleen , 2 ) the splenic parasite burden was rapidly increasing , 3 ) there was loss of antigen-specific T cell reactivity [34] , 4 ) the splenic T cell population was contracting while the B cell and macrophage populations were expanding [35] , and 5 ) the splenic macrophages had acquired an alternatively activated phenotype . Furthermore , the cultured spleen cell explants contained the full repertoire of spleen cells and supported ongoing parasite replication during the 48 hrs of exposure to test compound . The novel ex vivo splenic explant model showed excellent discrimination between active and inactive compounds in a medium-throughput screening format . Known anti-Leishmania drugs were readily identified upon the screening of the chemical libraries , confirming the ability of the ex vivo model to identify active compounds . Furthermore , the practical advantages of the ex vivo approach presented here are numerous . The model uses a Leishmania strain that is episomally transfected with the firefly luciferase reporter gene , which has been successfully used by others to quantify in vitro and in vivo infections [19] , [36] . The light emission of luciferase-tranfected parasites allows one to quantify the amastigote numbers in the samples by extrapolation to a standard amastigote curve , making this approach readily adaptable to a quantitative high throughput assay . The relative high cost of the luciferin substrate is offset by the radical decrease in labor , high sensitivity , and reproducibility of the method . Since in the ex vivo model the cells are harvested from infected animals and the screening is carried out in 96-well plates , no parasitological expertise to quantify amastigotes is necessary , and the time consuming and potentially biased microscopy-based evaluations that preclude automation are avoided . The fact that the luciferase-transfected L . donovani are selected by their resistance to the aminoglycoside G418 ( Geneticin ) suggests that related compounds would not be identified in the screen , however we found that another aminoglycoside , neomycin , was identified as a compound with activity against the transgenic L . donovani used in the ex vivo system . Other approaches that have used GFP-transfected Leishmania showed that the sensitivity of measuring fluorescence is not sufficient to enable microplate screening , and consequently a more demanding analysis using a flow cytometer is required [37] . The requirement of animals to establish the ex vivo splenic explant model is minimal because the cells obtained from a single infected hamster are sufficient to screen more than 1 , 000 compounds . Although axenically cultured amastigotes have been used to screen drug candidates on a small scale [38] , [39] not all parasite strains can be cultured as axenic amastigotes . It has been shown that in vitro assays that utilize intracellular amastigotes in macrophage cell lines correlate better with the response to treatment in vivo compared with assays in which promastigotes are used [40] . Screenings that involve in vitro infected macrophages have the technical limitation of difficulty in removal of extracellular promastigotes , and the theoretical concern that the non-macrophage immune regulatory cells are absent . Our results showed that the anti-Leishmania activity of lead compounds in the ex vivo spleen cell explants differed substantially from that found in cultured promastigotes , and to a lesser extent from freshly isolated tissue amastigotes . Thus , some of the lead compounds would not have been identified if promastigotes or cell-free amastigotes had been used . The lack of correlation between these techniques suggests that this ex vivo screening system can be exploited for the discovery of drugs targeting metabolic pathways of amastigotes in the host cells and/or interacting with the immune system . Accordingly , review of the published literature about the mechanisms of action of the 84 lead compounds revealed that the likely mode of action or parasite targets were the cell membrane ( 21% ) , cell metabolism ( 17% ) , the host immune response ( 13% ) , apoptosis ( 7% ) , and DNA interaction ( 6% ) . The screening of compounds for anti-Leishmania activity in the ex vivo model , coupled with screening for toxicity using the HepG2 hepatocyte cell line , enabled the selection of 84 lead compounds , 69 of which had not been identified previously to have anti-Leishmania activity . While the use of cell lines to predict in vivo toxicity has some limitations [41] , the HepG2 cell line is considered a good predictor of human toxicity [42] , [43] . Although measurement of ATP in viable cells this method is one of the most reliable methods to estimate cell toxicity [44] multiparametric toxicity testing ( based on features such as apoptosis markers and membrane integrity ) and other in vitro models may be desirable to help in the assessment of hepatic cytotoxicity [45] and further lead optimization [44] . Ultimately , the final selection of lead compounds will require in vivo studies to identify dose limiting toxicities and to evaluate whether the compound's pharmacokinetic and ADME ( absorption/distribution/metabolism/excretion ) properties make it suitable for use in VL . Already it is clear that some of the lead compounds ( e . g . topical and antiseptic agents ) are unlikely to be useful in treating systemic infection . Compounds containing the heterocyclic quinoline ring system were frequently identified in the screen as active inhibitors , representing 14% of the lead compounds . The quinoline leads could be further divided into several distinct classes . A number of the leads bear obvious structural relation to antimalarial quinolines , including several 4-aminoquinolines with basic side chains , as well as 8-aminoquinolines and a 2-arylquinoline derivative containing the core structure of the antimalarial drug mefloquine . Also represented were 8-hydroxyquinoline antifungals like clioquinol as well as more novel dimeric 2-aminoquinolines and quinoline diones . Several of the quinoline-containing compounds showed activity at micromolar concentrations and low toxicity to the HepG2 cell line , suggesting that they are good leads for optimization . Previous reports have described other quinoline-containing compounds with anti-Leishmania activity . For example , a 2-substitued quinoline alkaloid reduced by 79 . 6% the parasite load in the liver of BALB/c mice infected with L . donovani [46] . Sitamaquine , an 8-aminoquinoline , completed a phase II study for treatment of VL [47] , and more recently , DNDi has incorporated synthetic 2-quinoline derivatives as part of the strategy to develop new anti-Leishmania drugs ( http://www . dndi . org/newsletters/n18/edito . php ) . The quinoline ring system is common in known drugs and other bioactive molecules and such compounds can exhibit distinct bioactivities depending on their specific structures . Previously , quinoline derivatives have been reported to affect electron transport and generate lethal oxidative radicals against Leishmania , and also to inhibit cysteine proteases [48] , a gene family important for Leishmania virulence [49] . Other quinoline derivatives have also been shown to alter the vesicle trafficking and endocytosis in Plamodium falciparum [50] . Our results , together with these prior observations , suggest the likely possibility that multiple distinct bioactivities are represented among the quinoline leads identified here and that multiple pharmacologically orthogonal candidates might be selected for further lead optimization studies . Alkaloids also represented an important fraction of the lead compounds ( 10% ) identified in the ex vivo screening . This parallels the observation that alkaloids derived from natural products have been found to be active against Leishmania species ( reviewed by [51] ) . Disruption or alteration of membrane function was identified as a mode of action for a number of compounds ( ionophores , quaternary ammonium salts and tricyclic anti-depressants [52] ) that were identified as leads in our screen ( Tables 4 , S1 ) . In addition to a possible direct effect on the parasite membrane these inhibitors have the potential to affect the membrane integrity of the parasitophorous vacuole in which Leishmania resides , and perturb the capability to regulate the intraphagosomal trafficking of essential substrates for parasite survival [53] . Specific inhibition of biosynthesis of phosphatidylcholine has been proposed for some quaternary ammonium salts active against L . major promastigotes and L . braziliensis [54] . One of the leads identified here , cetrimonium bromide , is a cationic surfactant that closely resembles the structure of hexadecylphosphocholine ( miltefosine ) , a drug in current use for VL . It has also been shown to inhibit choline kinase that regulates the biosynthesis of the most abundant phospholipid ( phosphatidylcholine ) in Plasmodium falciparum [55] . Because these lead compounds are recommended only for topical and not systemic administration they will not be drug candidates for VL , but identification of their molecular targets could facilitate new screening campaigns that could identify lead compounds more suitable for systemic use . Phenothiazine compounds related to the tricyclic antidepressants constituted 7% of the leads , but their favorable IVTI was primarily a result of their low toxicity , rather than their good anti-Leishmania activity ( EC50 11±2 . 7 µM ) . This class of drugs was identified as having in vitro anti-Leishmania activity 30 years ago , but the absence in the literature of any in vivo therapeutic data would suggest that may not have good clinical efficacy . Other phenothiazine compounds are potent inhibitors of parasite trypanothione reductase , a key enzyme involved in many redox defenses of Leishmania . However , no correlation between anti-leishmanial efficacy and the potency of several trypanothione reductase inhibitors was found [56] . A broad range of biological effects have been recognized for the hydrocarbon terpenes , which represented 8% of the lead compounds . After alkaloids , natural product triterpenes are the inhibitors found most frequently as having activity against Leishmania spp . [51] . The terpenoids of the plant family Asteraceae parthenolide have been shown to inhibit amastigotes and promastigotes of L . amazonensis [57] . An analog of Harmine , a beta-carboline amine alkaloid identified in our screening , reduced the spleen parasite load by 40–80% in hamsters through necrosis and non-specific parasite membrane damage [58] . However , the selection of inhibitors targeting parasite specific metabolic pathways without altering the host's cell would be more desirable . In summary , the ex vivo splenic explant model , which is comprised of the full repertoire of host cells including chronically infected macrophages and fibroblasts , enabled the identification of small molecules that have anti-Leishmania activity within the immunopathological milieu that closely resembles the in vivo features of progressive VL . The inclusion of the complex biological interactions between the parasite and host within the test system may also favor the identification of lead compounds that act at multiple targets [59] . The standardized approach presented here identified a number of compounds that had good potency , including some that are currently in clinical use for other indications . Further study in animal infection models seems a prudent next step for those agents already used systemically . Among the other lead compounds identified in this work are several interesting chemotypes ( e . g . quinolines ) that are good candidates for a lead optimization . The identification of highly active anti-leishmanial compounds in this ex vivo model of VL could contribute greatly to new drug discovery for this serious and neglected disease .
|
Visceral leishmaniasis is a life threatening parasitic disease present in several countries of the world . New drugs are needed to treat this disease because treatments are becoming increasingly ineffective . We established a novel system to screen for new anti-leishmanial compounds that utilizes spleen cells from hamsters infected with the parasite Leishmania donovani . The parasite strain we used was genetically engineered to emit light by the incorporation of the firefly luciferase gen . This laboratory test system has the advantage of reproducing the cellular environment where the drug has to combat the infection . The efficacy of the compounds is easily determined by measuring the light emitted by the surviving parasites in a luminometer after exposing the infected cells to the test compounds . The screening of more than 4 , 000 molecules showed that 84 ( 2 . 1% ) of them showed anti-leishmanial activity and had an acceptable toxicity evaluation . Eighty two percent of these molecules , which had varied chemical structures , were previously unknown to have anti-leishmanial activity . Further studies in animals of these new chemical entities may identify drug candidates for the treatment of visceral leishmaniasis .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"infectious",
"diseases/protozoal",
"infections",
"infectious",
"diseases/antimicrobials",
"and",
"drug",
"resistance"
] |
2011
|
Identification of Small Molecule Lead Compounds for Visceral Leishmaniasis Using a Novel Ex Vivo Splenic Explant Model System
|
The ability to build in-depth cell signaling networks from vast experimental data is a key objective of computational biology . The spleen tyrosine kinase ( Syk ) protein , a well-characterized key player in immune cell signaling , was surprisingly first shown by our group to exhibit an onco-suppressive function in mammary epithelial cells and corroborated by many other studies , but the molecular mechanisms of this function remain largely unsolved . Based on existing proteomic data , we report here the generation of an interaction-based network of signaling pathways controlled by Syk in breast cancer cells . Pathway enrichment of the Syk targets previously identified by quantitative phospho-proteomics indicated that Syk is engaged in cell adhesion , motility , growth and death . Using the components and interactions of these pathways , we bootstrapped the reconstruction of a comprehensive network covering Syk signaling in breast cancer cells . To generate in silico hypotheses on Syk signaling propagation , we developed a method allowing to rank paths between Syk and its targets . We first annotated the network according to experimental datasets . We then combined shortest path computation with random walk processes to estimate the importance of individual interactions and selected biologically relevant pathways in the network . Molecular and cell biology experiments allowed to distinguish candidate mechanisms that underlie the impact of Syk on the regulation of cortactin and ezrin , both involved in actin-mediated cell adhesion and motility . The Syk network was further completed with the results of our biological validation experiments . The resulting Syk signaling sub-networks can be explored via an online visualization platform .
Tyrosine phosphorylation of proteins acts as an efficient switch allowing to control key signaling pathways involved in cell proliferation , apoptosis , migration , and invasion , and is thus involved in oncogenesis . Understanding the functioning of such complex pathways is crucial for both fundamental research and clinical applications and relies on the ability to build in-depth network models from extensive global experimental data [1–7] . The non-receptor spleen tyrosine kinase Syk has for a long time been considered as a hematopoietic cell-specific signaling molecule . In these cells , Syk is involved in coupling activated immunoreceptors to downstream signaling events affecting cell proliferation , differentiation and survival [8] . We and others have discovered that Syk is also present in non-hematopoietic cells [9–12] . More precisely , its expression was found in mammary epithelial cells and low-tumorigenic breast cancer cell lines , whereas invasive and metastatic breast cancer cells lacked Syk expression [11] . In patient samples , Syk expression exhibits a gradual loss during breast cancer progression and the low Syk levels are correlated with an increased risk of metastasis [13 , 14] . In hematopoietic cells , Syk functions as an essential component of the signaling machinery of multiple immune receptors and adapter proteins that are , however , not expressed in non-hematopoietic cells . Unveiling the Syk signaling pathways and tumor suppressor mechanisms is a public health issue as pharmacological Syk inhibitors are being used in clinical trials for treating auto-immune diseases [15 , 16] . We and other groups performed quantitative phospho-proteomic studies , based on differential Syk expression or activity , in order to identify novel Syk signaling effectors in breast cancer cells [17–19] . These approaches , however , allowed only to establish a comprehensive list of direct and indirect Syk targets . In this study , we use the data produced in these investigations to reconstruct a Syk-based signaling network and to identify the intermediary pathways via which the signal propagates from Syk to its effectors in this network . Phospho-proteomic studies provide data sets of phosphorylated proteins with a significant “fold change” in differential experiments . Comparably to the gene data sets in transcriptomic studies , these protein targets can be used to build networks by using comprehensive interaction databases ( hypergeometric test [20]; GSEA [21]; DAVID [22]; enrichment maps [23] ) . On the one hand , the set of identified targets is incomplete and intermediate variables and interactions are needed to describe systems-level functioning . On the other hand , the set may be too substantial and contain spurious or inessential components . Consequently , in order to obtain comprehensive networks that expose new essential constituents , network reconstruction procedures should contain both enrichment and pruning steps . Network pruning can be performed by sub-network extraction [7 , 24–27] . Three main approaches based on generic graph analysis methods have been used to extract sub-networks associated to a subset of its nodes: the classical shortest path [27–29] , Steiner trees [7 , 25 , 26] and random walk processes [30] . Module identification using expression data to score sub-networks [31] is a closely-related method but its aim is to identify a number of significant small sub-networks rather than produce a simplified connected network . Here we focus on the identification of signaling pathways from a single source to a collection of targets identified in phospho-proteomic studies . In this context , the classical shortest paths approach can be inaccurate and does not take into account alternative paths , while the k-shortest paths extension generates a collection of ranked alternative paths , but relies on well-separated weights between arcs to be effective [27] . Steiner trees enable the identification of the smallest set of edges allowing to connect a set of nodes: it may lead to longer individual paths but will reduce the number of interactions when considering the dataset as a whole , with the drawback of further reducing the number of alternative paths [25] . Suboptimal solutions of the Steiner tree problem were computed for a lymphoma network [32] but the functional significance of disparate solutions was not appraised . Finally , random walk processes have been used to estimate the probability of reaching network nodes by observing information flow propagation [30 , 33] The random walk approach can prioritize some proteins [30 , 34] but does not usually aim to identify paths from the source ( s ) to the target ( s ) . Additionally , it can be inaccurate and fail to render specific features of the signaling pathways because it assumes that the flow of information through the network satisfies linear equations ( Laplace equation on a graph ) with transitions that are mainly guided by topology . Other turn-key tools have been developed , such as Ingenuity Pathways Analysis ( IPA ) [35] . IPA mainly involves gene-regulatory networks and request the signs of the regulations in order to score interactions and paths . The latter information is rarely available in relation with protein modification/interaction and phospho-proteomics data . Furthermore , IPA is a proprietary software with a private database . Despite their limitations , each of these methods provides valuable information and it is useful to combine several approaches with ad-hoc adjustments when analyzing networks in relation with specific data . In this paper we assembled the interaction network of signaling pathways controlled by Syk in breast cancer cells , exploiting existing phospho-proteomic studies . To reconstruct and analyze the signaling network , we propose a novel methodology that combines the shortest paths methods with random walk processes . We defined interaction weights based on functional annotations and experimental datasets . Mainly , we pinpointed phosphorylation-based interactions leading directly to targets whose changes of phosphorylation are significant and interactions whose sources were identified targets . These weights define transition probabilities of a Markovian random walk on the network , which are further refined by replacing them with probability currents of the stationary Markov process . Thus , the random walk is not used for pruning directly , like in other implementations of this technique[30 , 33] , but applied for weight re-evaluation . To produce a list of biologically relevant paths relating Syk to its direct and indirect targets , we then searched for near-shortest paths in the resulting network with refined weights . Our method combines the advantages of weighted shortest path methods that take into account the functional importance of the interactions with those of the random walk methods that propagate the information on the network and smoothen large weight differences that could incidentally occur . The increase in specificity obtained by combining several sub-network extraction methods has also been exploited for analyzing metabolic networks [36] . By network pruning , relatively dense networks are downscaled to a few biologically significant alternative paths from Syk to its targets . This helps to generate hypotheses about Syk signal propagation that , however , need to be experimentally validated . We substantiated our in silico hypotheses with molecular and cell biology experiments , and identified two candidate mechanisms that support the impact of Syk on the regulation of cortactin and ezrin , two proteins involved in actin-based cell adhesion and motility . As a result of our biological validations , we propose a new Syk-Src-cortactin signaling axis , and a direct ezrin regulation by Syk phosphorylation . The Syk network was further corroborated with biological validation experiments and exploited to generate the sub-networks of the paths from Syk to its targets involved in ( i ) cell adhesion and motility , ( ii ) cell growth and death , ( iii ) immunity and inflammation and ( iv ) cell differentiation . The proposed sub-network extraction method was applied to connect Syk , a source in the network , to its direct and indirect targets . The same method can be applied in numerous other studies to connect several sources amongst themselves and to their targets . This method reveals important targets and interactions , allows to generate hypotheses and test new interactions . The simplified network resulting from this method provides insights into biological processes controlled by Syk and provides a challenging access to more mechanistic modeling approaches .
To bootstrap the reconstruction of a comprehensive network of Syk downstream signaling in breast cancer cells , we analyzed tyrosine phospho-proteomic data acquired in two independent mass spectrometric studies using breast cancer cell lines with modified Syk catalytic activity or protein expression . On the one hand , we selected from our own study the proteins lost or gained in tyrosine-phosphorylated protein complexes of the Syk-positive MCF7 cells treated with a pharmacological Syk inhibitor ( further referenced as the MCF7 dataset ) [19] . On the other hand , we identified the proteins with modified tyrosine phosphorylation after exogenous Syk expression in the Syk-negative MDA-MB-231 cells ( further referenced as the MDA231 dataset ) [17] . Post-treatment procedures of the original phospho-proteomic data are detailed in the Materials and Methods section . From these two studies; we respectively selected 265 and 487 proteins as Syk targets ( S1 and S2 Tables ) . Only 64 proteins were found in common , reflecting the complementarity of the two original phospho-proteomic studies . Indeed , the phospho-tyrosine enrichment prior to mass spectrometry as well as the experimental cell models used in those studies are different ( see the Materials and methods section ) . We analyzed the two datasets separately to evaluate whether they point to distinct or similar cell signaling pathways . We searched for enriched pathways in the lists of Syk targets , using pathways from the KEGG database [37] . We selected pathways which contain at least one of the proteins from the target lists and used a Fisher exact test to assess their enrichment . As we are only interested in overrepresented pathways , we removed the underrepresented ones: i . e; the ones for which the ratio “number of proteins from the target list per total number of proteins” is lower than the same ratio in the background list . Within the two rather poorly overlapping datasets , we found among the most enriched KEGG pathways those related to cell-cell and cell-substrate adhesions , actin cytoskeleton regulation and apoptosis ( S3 and S4 Tables ) . This observation is consistent with the reported role of Syk on cell adhesion , motility , proliferation and death in breast cancer cells [19 , 38–43] . Furthermore , the similarity in pathway enrichment , despite the limited overlap between the two datasets , indicates their relationship . We merged the original datasets and present here the results obtained for the network reconstruction . We assembled and explored a prior-knowledge interaction network to extract candidate mechanisms underlying the datasets . While such networks are often assembled using complete pathway or interaction databases , here we focused on previously identified enriched pathways . Using pathways rather than individual interactions will enable the extension of sub-networks with relevant interactions in their neighborhood for further analysis . By restricting our full network to a subset of pathways , some coverage may be lost but it allows to reduce the amount of irrelevant interactions and to better assess the relevance of the identified pathways . Hereafter we show that the enriched pathway can be used to identify candidate mechanisms . We also extended our search by using the Pathway Commons database [44] . KEGG provides a set of well-established pathways , while Pathway Commons allows a higher coverage by integrating pathways from multiple sources ( notably Reactome , Panther , and PID ) . To select the most relevant pathways guaranteeing to cover most identified targets , we filtered the pathways based on their enrichment p-value and selected those presenting a p-value lower than 0 . 1 ( in this step we aim to be as complete as possible , selecting 83 pathways from KEGG and 419 from Pathway Commons ) . Furthermore , we included the pathways containing Syk targets not covered by significantly over-represented pathways from the same database . This allowed to integrate 41 additional pathways from KEGG and 9 from Pathway Commons ( S5 and S6 Tables ) . In this merged network , each protein and interaction keeps track of the list of pathways in which it is involved . We also used the GO annotation to identify the proteins involved in processes in which Syk is implicated ( cell adhesion and motility , cell growth and death , immunity and inflammation , cell differentiation ) . The resulting oriented and partly signed network comprises 6438 proteins and 62322 interactions , from 552 pathways ( 124 from KEGG , 428 from Pathway Commons ) , covering 350 of the 687 identified targets ( 75 are only found in KEGG , 125 in Pathway Commons and 150 in both ) . Among these 350 targets , 245 are reachable from Syk ( steps 1–2 in Fig 1 ) . This network contains 979 interactions between two targets identified in the datasets . A closer scrutiny of these interactions further highlights connections between the two datasets: 146 and 260 interactions are associating targets specific to the MCF7 and MDA231 datasets respectively , 253 involve at least one target shared by the two datasets and 320 connect targets specific to different datasets . The paths connecting Syk to the identified targets in the reconstructed network describe possible mechanisms for its signal propagation . As a massive amount of alternative paths exist , it is crucial to identify the appropriate candidates amongst the mass by extracting the sub-networks that exhibit selected connections between Syk and a specific target ( step 3 in Fig 1; S1 Fig ) . We focused on the signal propagation from Syk to its targets involved in cell adhesion and motility , and in particular on cortactin and ezrin , two proteins that are differentially phosphorylated in a Syk-dependent manner and that functionally link the plasma membrane to the actin cytoskeleton [45 , 46] . We first considered a parsimonious approach to find the paths implicating the fewest nodes , and searched for these shortest paths using the classical Dijkstra algorithm [47] . We observed that the shortest paths between Syk and cortactin contain two intermediates , with three alternative proteins directly upstream of cortactin ( Fig 2A ) . Src is the only phospho-tyrosine modifier ( tyrosine kinases and phosphatases ) amongst them , suggesting that the paths involving Src are more credible to explain the change in cortactin phosphorylation . The shortest paths network linking Syk to ezrin included more proteins and contained also two intermediate nodes ( Fig 2B ) . These paths were related with the classical regulation of ezrin by the membrane lipid PIP2 or its phosphorylation on serine/threonine residue ( s ) [48 , 49] . None of the nodes directly upstream of ezrin were phospho-tyrosine modifiers that could explain the effect of Syk on ezrin tyrosine phosphorylation . The sub-networks obtained for cortactin and ezrin illustrate the need for careful analysis of phospho-tyrosine modifiers to explain the phosphorylation-based modifications noticed in the MDA231 dataset . To take into account the importance of phospho-tyrosine modifiers , we extended the GO annotation of network nodes with the corresponding terms ( S7 Table ) and modulated the length of the path using distances attached to the interactions: a path involving more steps associated to small distances can be selected over a short path with longer steps . We assigned distances giving the priority to paths linking a phospho-tyrosine modifier upstream of proteins experimentally identified as differentially phosphorylated . We also favored the inclusion of other experimentally identified proteins by reducing the distances of their outgoing interactions . The introduction of these distances led to more realistic suggestions for the cortactin sub-network , in which Src is the only protein directly upstream of cortactin ( paths via white nodes in Fig 2A ) . As shown in the dedicated Results subsection , biological validation confirmed the role of Src in mediating Syk signal propagation to cortactin . However , it did not enable the identification of significantly better suggestions for ezrin , suggesting a “missing link” ( paths via white nodes in Fig 2B ) . To resolve this inconsistency , we decided to evaluate the possibility of a direct interaction between Syk and ezrin . These results are described below and prompted us to propose that Syk directly phosphorylates ezrin . Taking into account our biological validation , we added the new protein interaction from Syk to ezrin to the Syk network . We also extended the list of Syk direct substrates by integrating the results of a third dataset that identified the peptides phosphorylated on tyrosine by Syk after an in vitro kinase reaction [18] ( S2 Table; for details , see Materials and methods ) . Finally , we added the direct interactions between Syk , E-cadherin and alpha-catenin , both members of the same cell-cell adhesion complex that we previously identified as direct Syk substrates [19] ( step 4 in Fig 1 ) . The sub-networks obtained with the weighted shortest-paths analysis still contain several equivalent paths which we would like to classify further by integrating a parameter based on the network topology . A random walk process provides a “reachability” score for each protein . By taking into account both a mix of network topology and the weighted interactions , this score highlights key proteins that are involved in multiple interesting and plausible candidate paths . To integrate this parameter into our analysis method , we used these scores to refine the distances associated to their outgoing edges , allowing the shortest-path approach to also include such key proteins . The weight refinement algorithm is fully described in the Methods section . This methodology was applied to refine analysis of the Syk signal propagation to its targets involved in cell adhesion and motility . The size of this sub-network ( e . g . number of nodes and edges ) decreased after modulation of the length of the paths and even more after random walk refinement ( Fig 3 ) . This ranking property was appropriate to highlight the most likely paths regarding our biological and topological criteria . These observations were confirmed by the sub-networks linking Syk to its targets involved in Syk-related cell processes ( S2–S4 Figs ) Nevertheless , we considered this method as too stringent and searched for near-shortest paths instead of strict shortest paths in order to generate a set of alternatives , selecting all paths for which the total distance is up to 20% higher than that of the shortest path . Using this setting for the analysis of the signal propagation from Syk to its targets involved in cell adhesion and motility , we retrieved a sub-network as large as the one obtained without random walk refinement . Moreover , the sets of interactions linking Syk to its targets involved in Syk-related cell processes were slightly distinct as compared to the sub-networks obtained after weighted shortest paths analysis , and after refinement with random walks , both allowing a 20% overflow ( Fig 4 and S5–S7 Figs ) . This suggests that introducing the “reachability” parameter not only ranks the alternatives , but also selects novel elements that allow to generate hypotheses on Syk signal propagation . Taken together , the purpose of Figs 3 and 4 is to illustrate the flexibility of our method . These two figures tell us that ( i ) taking into account the molecular biology parameters by attaching distances to interactions for shortest-paths analysis and ( ii ) taking into account the network topology by the random walk refinement , leads not only to shortest paths to be biologically validated in the first instance ( Fig 3 , network size decrease ) , but also to novel alternative paths that were absent in the unweighted shortest paths analysis , and to rank more realistically the set of paths . Weighted shortest-path analysis from Syk to cortactin pointed to the Src tyrosine kinase as the phospho-tyrosine modifier that could account for the Syk impact on cortactin tyrosine phosphorylation ( paths via white nodes in Fig 2A ) . This hierarchy was contradictory with previous studies describing the direct interaction of Syk and cortactin in breast cancer cells [18 , 42 , 50] , and the impact of Src on Syk phosphorylation in colon cancer cells , opposite to our observations [51] . To test the ability of Src to drive the Syk signal propagation , we analyzed cortactin tyrosine phosphorylation , together with Syk and Src activity in cells treated with tyrosine kinase inhibitors . Cortactin phosphorylation and Src activity , evaluated by the phosphorylation of its tyrosine 418 residue , were decreased after cell pretreatment with Syk or Src pharmacological inhibitors ( Fig 5A and 5B ) . Syk activity , evaluated by its auto-phosphorylation on the tyrosine 525/526 residues , was not affected by Src inhibitors , demonstrating the Syk-Src-cortactin hierarchy . In the MCF7 dataset , cortactin is poorly affected by Syk inhibition . As the quantitative measurement was obtained by calculating the median SILAC ratio of several peptides from cortactin , we decided to analyze the phosphorylation of its individual peptides . The quantity of the phosphotyrosine pTyr334 cortactin peptide was ~2 fold decreased by Syk inhibition ( S8 Fig ) . Conversely , Tyr446 phosphorylation was not affected ( S9 Fig ) . Those observations were consistent with the data from MDA231 dataset ( Fig 5C ) . Taken together , our results indicate that the signal transmission from Syk to cortactin is mediated by the Src kinase . None of the nodes directly upstream of ezrin were phospho-tyrosine modifiers that could explain the Syk impact on ezrin tyrosine phosphorylation ( Fig 2B ) . To explore this more profoundly , we evaluated the possibility of a direct interaction between Syk and ezrin . Both proteins are localized in phospho-tyrosine enriched plasma membrane extensions ( ruffles ) of MDA-MB-231 breast cancer cells in which actin is dynamically reorganized ( Fig 6A ) . Colocalization of Syk and ezrin was evaluated quantitatively ( Fig 6B ) . Purified recombinant Syk was able to induce direct ezrin phosphorylation on tyrosine residue ( s ) in in vitro kinase assays ( Fig 6C ) . Moreover , in an immune-complex in vitro kinase assay using endogenous or exogenously expressed FLAG tagged or GFP fusion-proteins , ezrin was phosphorylated dependent on the Syk catalytic activity ( S10A and S10B Fig ) . It is worth noting that tyrosine phosphorylation of Syk is also enhanced in the presence of ezrin ( compare lane 3 with lane 1 ) in both autoradiography and Western blot analyses which may have a biological explanation . Ezrin contains a canonical ITAM motif that has been shown to interact with Syk [52] and even play a role in Syk recruitment and activation by binding to its tandem SH2 domains [53] . A positive feedback loop may thus exist as binding of Syk is activated by its SH2 domain binding to bi-phosphorylated ITAM motifs but should be explored more deeply . Detailed analysis of the ezrin post-translational modifications by isoelectric focusing revealed that its in vitro phosphorylation by Syk induced a unique phosphorylation of one third of the total ezrin protein ( S10C and S10D Fig ) . Mass spectrometric analysis demonstrated that ezrin is phosphorylated by Syk on a peptide containing the phosphorylated Tyr424 residue ( S11 Fig ) . The same ezrin phospho-peptide was the only one identified in the Syk-positive breast cancer cells in the MDA231 dataset . Taken together , these results prompted us to propose the new protein interaction between Syk and ezrin , leading to ezrin phosphorylation on the Tyr424 residue ( Fig 6D ) .
Using datasets extracted from two published complementary phospho-proteomic studies that identified Syk targets in breast cancer cells , we reconstructed a Syk-controled molecular network by integrating the components of signaling pathways enriched for Syk targets ( S1 File ) . Two different breast cancer cell models were used: Syk-positive MCF7 cells treated or not with a pharmacological Syk inhibitor versus exogenous Syk expression in the Syk-negative MDA-MB-231 cells . This was expected to bring some heterogeneity in the molecular paths linking Syk to its targets . Nevertheless , lists of Syk targets emerging from the different datasets showed similarities in pathway enrichment . Moreover , in the Syk network subpart containing the reachable targets , we found that one third of the interactions involving two targets connect targets identified in different datasets , which justified combining them into a unique set despite the partial overlapping . This proportion is conserved after shortest paths analysis , suggesting that a number of mechanisms by which Syk activates its targets are common to the cell models used . Although the biochemical methods to enrich protein extracts before mass spectrometry analysis were distinct ( enrichment of tyrosine phosphorylation dependent-protein complexes versus tyrosine-phosphorylated peptides ) , they produced complementary information . On the one hand , phospho-proteomic studies at the protein level identified not only proteins that are differentially phosphorylated but also their partners present in the protein complexes . Several conserved protein domains are involved in phospho-tyrosine-dependent protein-protein interaction . On the other hand , phospho-proteomic studies at the peptide level identified the differentially phosphorylated tyrosine residues , allowing to increase the precision of our experimental validations . Knowing the phosphosites provides insight in the functional consequences of the phosphorylation and the integration of this functional information allows to construct more coherent networks . The introduction of distances was a crucial step in our approach that allowed the selection of realistic paths . The extra random walk step enabled the refinement of these distances , so the selection of too many paths having the same length was avoided . In contrast to methods proposing a unique solution ( or too many alternatives ) , our method allows to downscale to a few biologically significant alternative paths . The simplified network obtained by sub-network extraction allowed us to generate computational hypothesis about Syk signal propagation that were subsequently validated . As a result of our biological validations , we proposed a new molecular explanation of the impact of Syk on cortactin , a protein involved in cell adhesion and motility by its ability to regulate the cortical actin cytoskeleton . Previous studies described cortactin as a direct Syk substrate [42 , 50] . However , in the phospho-proteomic data exploited in our study , the cortactin Tyr421 residue directly phosphorylated by Syk in vitro [18] does not match the Tyr residues phosphorylated in a Syk-dependent manner in cellulo [17] . In our model , we proposed a new Syk-Src-cortactin signaling axis ( paths via white nodes in Fig 2A ) . We demonstrated that cortactin tyrosine phosphorylation induced by Syk is dependent on Src catalytic activity . Conversely , inhibition of Src did not affect the Syk catalytic activity , but both are required to induce cortactin phosphorylation in breast cancer cells ( Fig 5A and 5B ) . This tyrosine kinase hierarchy does not match previous observations showing that Src inhibition induces a decreased tyrosine phosphorylation of Syk [51] . This discrepancy could be explained by the distinct cell models ( breast cancer versus colorectal cancer cells ) and by the fact that Leroy and colleagues [52] analyzed global Syk phosphorylation rather than specific phosphorylation on the Tyr525/526 residues , which are located in the activation loop of the Syk kinase domain and are more relevant to detect changes in Syk catalytic activity [54] . Multiple tyrosine phosphorylation sites have been described within cortactin ( for review , [46]http://www . phosphosite . org/ ) . We confirmed that the phosphorylation of cortactin Tyr446 residue is not directy affected by Syk ( Fig 5C ) . Conversely , the phosphorylation of the Tyr334 residue is Syk-dependent , but can , according to our model , be phosphorylated also by Src [55] . There is currently no information about the functional consequences of this residue’s phosphorylation . Finally , the Syk-Src-cortactin signaling axis we proposed is consistent with the positive impact of Syk on E-cadherin dependent cell-cell adhesion [19 , 42] . Src-dependent phosphorylation of cortactin is necessary to link the E-cadherin adherens junction complex to the actin cytoskeleton , that subsequently supports cell-cell contact formation [56 , 57] . Our initial computational hypotheses of molecular circuits linking Syk to ezrin did not explain its tyrosine phosphorylation , by the lack of a phospho-tyrosine modifier directly upstream of ezrin ( Fig 2B ) . We demonstrated that Syk phosphorylates ezrin in a direct manner on its Tyr424 residue and that both proteins colocalize in the plasma membrane ruffles in breast cancer cells ( Fig 6A and 6B ) . Previous studies described a Syk-dependent tyrosine phosphorylation of ezrin in B lymphocytes on its Tyr353 residue [58 , 59] , a site that can be also phosphorylated by the epidermal growth factor receptor ( EGFR ) [60] . Phosphorylation of this residue leads to activation of the ezrin downstream signaling pathways as JNK or PI3K/Akt [59 , 61] . Two other ezrin tyrosine residues , Tyr146 and Tyr478 , can be phosphorylated by Src ( Tyr146 , also being phosphorylated by EGFR ) , mediating cell scattering and stimulating motility [62 , 63] . We showed here that in breast cancer cells , Syk phosphorylates ezrin on the Tyr424 residue , rather than Tyr353 as it occurs in B lymphocytes , and could induce a signaling cascade different than the ones previously described for tyrosine phosphorylated ezrin . This novel molecular regulation of ezrin could help to explain the negative impact of Syk on epithelial cell motility . By this study , we do provide to the cancer cell signaling community access to the Syk network and sub-networks of the paths from Syk to its targets involved in ( i ) cell adhesion and motility , ( ii ) cell growth and death , ( iii ) cell differentiation and ( iv ) immunity and inflammation ( see Supporting Information ) . As Syk is involved in breast cancer suppression , it is not surprising that these cellular processes involved in cancer progression can be affected by Syk in breast cancer cells . Nonetheless , many of the Syk interactions described in pathway databases are extracted from molecular studies in cells of hematopoietic origin . More precisely , Syk signaling has been extensively studied in B lymphocytes where it is indispensable for immune cell differentiation . Which part of Syk signaling is shared between epithelial and hematopoietic cell types could be determined by Syk related phospho-proteomic experiments with a more comprehensive collection of cell models . At this stage , our model suggests a number of plausible mechanisms linking Syk with cancer-related cellular processes . These can be used to generate more hypotheses , validation and provide valuable inputs for further developments . A crucial issue in Syk-related research is how to activate compensatory mechanisms maintaining tumor suppression signaling even when Syk is downregulated . The network we propose here is the first step towards addressing this question . To advance towards more refined mechanistic models the annotation of the interactions , for instance the sign , should be completed by integration of more data and by formal inference methods [64 , 65] . The careful consideration of possible feed-backs should also be considered in these developments . For instance , in tyrosine kinase signaling , many feed-back interactions involve phosphatase players whose role and significance are largely ignored even for very well studied pathways such as MAPK . New biological experiments are needed to unravel new players and interactions . Basic networks like the one resulting from our approach can be used for planning such experiments . As a simple example , if phosphatases are present upstream in the network one would want to test the effect of their inhibition on downstream proteins . The bioinformatics method we used to reconstruct and prune the Syk signaling network can be used in other studies , whenever the focus is on finding candidate mechanisms explaining how signals propagate in large networks and how the network state-changes under perturbations . We consider that the combination of ad-hoc distances , random walk , and near-shortest paths provides good candidate mechanisms , by implementing the following requirements: ( i ) minimize the number of steps ( shortest paths ) ; ( ii ) fit with the data ( ad-hoc distances ) ; ( iii ) further favor intermediates which belong to multiple appropriate candidate paths ( random walk ) ; ( iv ) propose and rank multiple alternatives ( near-shortest paths ) . This strategy can be generally applied to other studies of signaling networks using datasets based on distinct post-translational modifications , separately or combined . The open source code for the network reconstruction and extraction of relevant sub-networks has been made available for the computational biology community ( S1 File and https://github . com/aurelien-naldi/NetworkReconstruct ) .
Uniprot ID mapping from uniprot . org/downloads ( 2015/07 ) HGNC dataset from genenames . org/cgi-bin/statistics ( 2015/07 ) GO ontology from geneontology . org/page/download-ontology ( go-basic . obo , 2015/10 ) GO annotation from geneontology . org/page/download-annotations ( goa_human . gaf , 2015/10 ) KEGG: www . kegg . jp , release 75 ( 2015/07 ) Pathway commons:pathwaycommons . org release 7 ( 2015/03 ) Proteins are identified by their Uniprot IDs , without the isoform postfix . We used Uniprot ID mapping files to associate KEGG and HGNC identifiers ( transferred to the corresponding gene symbols ) with these Uniprot IDs . When multiple Uniprot IDs are associated to the same KEGG or HGNC ID , they were grouped and a single ID is selected for the group , preferably a reviewed entry ( allowing to map unreviewed Uniprot entries to the associated reviewed entry when possible ) . The network is annotated based on the dataset , the pathways , and the GO annotation . Nodes and interactions keep track of the list of pathways in which they appear . Interactions types ( phosphorylation , modification , regulation ) and signs ( + or - ) are transferred from the pathways when available . When the same interaction is described in several pathways , all types and a combination of the signs are conserved ( an interaction described as positive in a pathway and negative in another is considered as unclear ) . We selected some groups of GO terms representing relevant processes and functions in this dataset , in particular cell adhesion and motility ( GO:0048870 , GO:0007155 , GO:0034330 , GO:0022610 , GO:0060352 , GO:0030030 ) , cell growth and death ( GO:0008283 , GO:0007049 , GO:0008219 , GO:0019835 , GO:0000920 , GO:0007569 , GO:0051301 , GO:0060242 ) , immunity and inflammation ( GO:0002376 , GO:0001906 ) , and cell differentiation ( GO:0030154 , GO:0036166 ) . We also annotated as phospho-tyrosine modifiers the components of the network with tyrosine kinases ( GO:0004713 ) and tyrosine phosphatases ( GO:0004725 ) GO terms and manually verified this list ( S7 Table ) . Many nodes in KEGG pathways represent groups of proteins , where the same protein can be part of several groups ( with variable overlap ) . These groups are conserved in the merged network by introducing “group nodes” with bidirectional links to their members . Proteins can thus have interactions associated directly to them or through one or several groups . Such groups are “exploded” before the path search step described below . Cytoscape 3 . 4 ( http://www . cytoscape . org/ ) was used to generate figures and Cytoscape web was used to provide interactive access to the Syk network . We searched for “near-shortest paths” between Syk and a list of targets , using an approach similar to the classical Dijkstra algorithm for shortest paths . We started by identifying the length of the shortest path for every node as in Dijkstra’s method ( starting from the source node , we iteratively picked the closest new neighbor of all reachable nodes: at each step we obtained the best result for a new node , ending with the node with the longest of the shortest paths ) . In the Dijkstra algorithm , the shortest paths were then obtained by starting from the target nodes and going backward to the source by selecting the incoming edge ( s ) which can satisfy this best distance: i . e . the best distance of the current node is equal to the sum of that of the source and the distance of the edge . Here we define an acceptable extra distance to include additional nodes and edges during this backtracking step . Note that the resulting sub-network can contain paths that are longer than acceptable , but all selected nodes and edges are involved in at least one acceptable path . For example if ( A , B , C ) is the shortest path from A to C , and ( A , I , B , C ) and ( A , B , J , C ) are also acceptable , then the path ( A , I , B , J , C ) exists in the resulting sub-network despite being too long . In the resulting sub-network , the selected nodes and edges are annotated with the “overflow” needed to include them: i . e . the extra distance of the best path using them compared to the actual shortest path . Members of the shortest paths have no overflow . To improve the identified paths , we selected edge weights based on the available annotations: “normal” edges have a distance of 5 ( d = 5 ) , we promoted edges coming out of identified proteins ( d = 3 ) , edges reaching an identified protein while coming out of a tyrosine kinase or phosphatase ( d = 2 ) or combining these two conditions ( d = 1 ) . On the other end , we demoted edges reaching a target identified as differentially phosphorylated , but which did not come from a tyrosine kinase or phosphatase ( d = 8 ) , even if they come out of another identified protein ( d = 6 ) . Finally , edge distances are refined to integrate the results of the random walk estimation: they are multiplied by the inverse of the normalized score of their source node . We adapted the netwalk implementation in R from the GUILD software [66]: http://sbi . imim . es/web/index . php/research/software/guildsoftware Edge weights are based on the distances originally defined for the shortest paths search ( reversed as a higher distance corresponds to a lower weight ) . More precisely , let dij be the distance from a node i to its direct target j , previously defined for the shortest path search . Given a node i the set of all its direct targets ( successors in the directed network ) is denoted Succ ( i ) . The random walk is defined by transition probabilities pij defined as: pij= ( 1−p0 ) dij−1∑j∈Succ ( i ) dij−1 , where p0 is the return probability to the origin node Syk ( chosen the same for all nodes ) . The probabilities pij together with p0 added as last entry of each row are the entries of the stochastic matrix P ( each row of this matrix sums to one ) . The equilibrium or limiting distribution of the random walk is a normalized row vector π satisfying the equation: πP=π . This distribution can be estimated by starting the random walk from any node and running it a sufficiently long time for equilibration . A finite , connected network with possibility of return to the Syk node from terminal nodes is ergodic guaranteeing the existence and uniqueness of the equilibrium distribution . Nodes are scored by the values of the equilibrium probabilities πi . In order to eliminate biases created by topology a second simulation is performed where all edges have the same weight . The resulting scores in this second simulation are the equilibrium probabilities πi0 . The two scores are then used to refine the distances as follows d˜ij=dijπi0πi . MCF7 , MDA-MB-231 and COS7 cell lines were obtained from the ATCC and maintained in Dulbecco’s modified Eagle’s medium ( DMEM , Gibco ) supplemented with 10% fetal calf serum ( FCS , Eurobio ) . All cell cultures were carried out at 37°C using a 5% CO2 atmosphere . For cell stimulation studies , cell lines were stimulated with Sodium pervanadate ( PV , premix of 1 mM H2O2 and 1 mM Na3VO4 ) and incubated for 15 min at 37°C . For the evaluation of the effect of the kinase inhibitors ( all from Selleckchem ) , cells were incubated in medium for 2 hours with 2 . 5 mM R406 , 5 mM PRT062607 , 1 mM PP2 , 0 . 5 mM AZD0530 . Stock solutions for all those kinase inhibitors were prepared in dimethyl sulfoxide ( Sigma , Hybri-Max grade ) , which is used as vehicle negative control Cells were washed with ice-cold phosphate buffered saline solution and scrapped in 10 mM Tris-HCl ( pH 7 . 4 ) , 150 mM NaCl , 0 . 5 mM EDTA ( Sigma ) , 1% Nonidet-P40 ( Sigma ) , 0 . 5% sodium deoxycholate ( Sigma ) , 1 mM Na3VO4 ( Sigma ) , 50 mM NaF ( Sigma ) and a protease inhibitor cocktail ( Sigma ) at 4°C . After transfer to an Eppendorf tube and extensive vortexing , lysates were cleared by centrifugation at 10 , 000 rpm for 10 min at 4°C and supernatants diluted in 4x SDS-PAGE sample buffer . Immunoprecipitations were performed as described previously [44] . Protein samples were diluted in 4x SDS-PAGE Laemmli sample buffer , denatured by boiling for 5 min at 95°C in SDS-PAGE sample buffer , separated electrophoretically and transferred to polyvinylidene difluoride membranes . Membranes were blocked using 5% BSA in tris-buffered saline solution with Tween-20 detergent ( TBS-T; 25 mM Tris-HCl pH 8 . 0 , 150 mM NaCl , 0 . 1% Tween-20 ) for 1 h and then incubated at 4°C with the appropriate primary antibodies diluted in blocking buffer . Those included a mix of two monoclonal antibodies to phospho-tyrosine ( 1:1 mix vol/vol mix of the 4G10 and PY20 hybridoma supernatants ) , monoclonal antibodies to the FLAG epitope ( clone M2 , Sigma ) , cortactin ( clone 4F11; Millipore ) , Syk ( clone 4D10 , Santa Cruz ) and alpha-tubulin ( clone DM1A , Sigma ) , rabbit polyclonal antibodies to pTyr418 Src ( Invitrogen ) , GFP ( Chemokine ) , a home-made rabbit polyclonal antibody to the COOH-terminal domain of human ezrin [67] , and a rabbit monoclonal antibody to pTyr525/526 Syk ( Cell Signaling Technology ) . After three washes with TBS-T , the membrane was incubated with horseradish peroxidase-conjugated appropriate secondary antibody ( 1:5000 , Jackson ImmunoResearch ) for 1 h at room temperature . Immunoblots were revealed using a standard chemoluminescent method ( ECL , Ozyme ) and a Multi-application gel imaging system ( PXi , Syngene ) . Membranes were optionally stripped with the Restore PLUS Western blot stripping buffer ( Thermo Scientific ) before a second immunoblotting . Immunoblot-derived signals were quantified using the ImageJ software ( NIH ) with three independent biological and technical replicates for each quantification . The signals were normalized on the lane corresponding to the total protein quantity loaded ( immunoglobulin heavy chain in case of immunoprecipitation; Tubulin-α in case of whole cell lysate ) and the unstimulated condition was arbitrarily set at 1 . Assays with proteins extracted from cell lysates , immunoprecipitations and Western blot analyses were performed as described previously [43] . Otherwise , recombinant GST-Syk ( BPS Bioscience , San Diego , CA ) and GST-ezrin ( previously described in [68] ) were used . In vitro kinase assays were performed as described [43] . For the two-dimensional electrophoresis analysis , proteins were precipitated for 2 h in two volumes of acetone at −20°C , and resuspended in 8 M urea , CHAPS 4% , and thiourea 2 M . We used 18 cm IPG-strips ( Amersham Biosciences ) with linear pH range of 3–10 for the first dimension . Proteins were loaded on the IPG-strips and run in a Multiphor II apparatus ( Amersham Biosciences ) . After focusing , a second migration was performed in 10% SDS-PAGE gel and proteins were stained with silver nitrate ( Amersham Biosciences ) . MDA-MB-231 cells were transiently transfected with pDsRed-Syk [43] using Fugene 6 ( Roche Applied Bioscience ) . Immunostaining procedures have been described before [43] . The following primary antibodies were used: monoclonal antibody 4G10 hybridoma supernatant ( diluted 1:50 in TBS ) and a home-made rabbit polyclonal antibody to the C-terminal domain of human ezrin [67] . The secondary antibodies used were goat-anti-mouse-Cy5 and donkey-anti-rabbit-FITC ( Jackson ImmunoResearch Laboratories ) . Confocal images of immunostained cells were obtained as described [69] . For quantitative analysis of colocalization , we used the ImageJ software plug-ins ( https://imagej . nih . gov/ij/ ) with the “Colocalization Finder” module ( https://imagej . nih . gov/ij/plugins/colocalization-finder . html ) to generate the merged picture of Syk and ezrin channels in which colocalized pixels are displayed in white , and with the “Coloc 2” module ( http://imagej . net/Coloc_2 ) to generate the scatter plot of pixel intensities in Syk and ezrin channels and to compute the Pearson correlation of pixel intensities over space . Statistical analyses were performed using the two-tailed Student’s t test for paired and unpaired data versus control values . Experimental values in this work are all given as mean and standard error of the mean ( SEM ) . Results with a P value ≤ 0 . 05 were considered as statistically significant .
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The complex nature of cancer hampers traditional biological approaches to unravel its molecular mechanisms and develop targeted drug therapies . Cancer affects a number of “hallmark” cellular processes controlled by multiple signaling pathways . Our goal is to identify the pathways that negatively affect tumor development and progression . We established that the Syk protein tyrosine kinase exhibits a tumor-suppressive function in breast cancer . Large scale global biochemical analyses allowed to identify Syk targets in cancer cells , but their mechanisms and interrelationships remain unknown . Our main goal was to pinpoint a limited number of biologically realistic molecular “paths” from Syk to its effectors . We therefore developed a new methodology combining graph theoretical methods allowing to reveal the shortest “paths” between “nodes” in a graph including an approach that investigates also longer “paths” . Applied to the Syk network , this method allowed us to propose and validate new signaling axes relating Syk to major effectors of the cell adhesion and mobility that are crucial cancer hallmarks .
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2017
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Reconstruction and signal propagation analysis of the Syk signaling network in breast cancer cells
|
Current state-of-the-art mathematical models to investigate complex biological processes , in particular liver-associated pathologies , have limited expansiveness , flexibility , representation of integrated regulation and rely on the availability of detailed kinetic data . We generated the SteatoNet , a multi-pathway , multi-tissue model and in silico platform to investigate hepatic metabolism and its associated deregulations . SteatoNet is based on object-oriented modelling , an approach most commonly applied in automotive and process industries , whereby individual objects correspond to functional entities . Objects were compiled to feature two novel hepatic modelling aspects: the interaction of hepatic metabolic pathways with extra-hepatic tissues and the inclusion of transcriptional and post-transcriptional regulation . SteatoNet identification at normalised steady state circumvents the need for constraining kinetic parameters . Validation and identification of flux disturbances that have been proven experimentally in liver patients and animal models highlights the ability of SteatoNet to effectively describe biological behaviour . SteatoNet identifies crucial pathway branches ( transport of glucose , lipids and ketone bodies ) where changes in flux distribution drive the healthy liver towards hepatic steatosis , the primary stage of non-alcoholic fatty liver disease . Cholesterol metabolism and its transcription regulators are highlighted as novel steatosis factors . SteatoNet thus serves as an intuitive in silico platform to identify systemic changes associated with complex hepatic metabolic disorders .
Mathematical modelling provides an intuitive tool to investigate complex diseases that display multiple causal mechanisms and a complex pathobiology . In particular , liver-associated pathologies that have a high prevalence in Europe and other Western populations face poor prognosis , and the lack of adequate molecular understanding and management [1] . Hence , novel interdisciplinary strategies to comprehend the pathogenesis are required to quicken the pace of identifying suitable treatment regimens and monitor disease progression . Mathematical models describing hepatic metabolic pathways have been constructed , including models of cholesterol synthesis [2] , [3] , glucose and lipid metabolism [4] , [5] etc . HepatoNet1 that was constructed as a part of the Virtual Liver Network is a comprehensive reconstruction of a human hepatocyte [6] , [7] which was further extended by a recent genome-scale metabolic model , iHepatocytes2322 describing the lipid metabolic pathways in detail [8] . While these extensive hepatocyte-specific models have immense potential to investigate liver functions , they may be less informative to study the aetiology of complex diseases , where deregulations occur in multiple tissues . An additional drawback of current metabolic models is the difficulty in simultaneously integrating metabolic pathways with both gene expression and signalling networks [9] . Thus , the robustness and genotype-phenotype correlation in these models is notably compromised . Moreover , a general hurdle for large computational models is parameter estimation since kinetic constants derived from in vitro experiments are at present poorly documented , display variability and are frequently incompatible with molecular behaviour in vivo [10] . Taking into consideration the currently prevalent drawbacks , we describe the SteatoNet , the first multi-pathway and multi-tissue model including key hepatic metabolic pathways , their interaction with extra-hepatic tissues and hierarchical feedback regulation at the gene expression and signal transduction levels . The kinetic parameters of the network are computed for a specified normalised steady state by utilizing user-defined values for the reversibility of reactions , the distribution of fluxes at pathway branches , and the metabolic influx into the network . Thus , the challenging task of accurately selecting kinetic parameters for large metabolic networks is bypassed . The estimated parameters are semi-quantitative and provide insights into the global system behaviour rather than accurate estimations of model variables . In order to illustrate the utility of SteatoNet in investigating liver-associated pathologies , we describe a model analysis to identify candidate mediators involved in the initiation of non-alcoholic fatty liver disease ( NAFLD ) . NAFLD , the hepatic manifestation of the metabolic syndrome , is the most common chronic liver disease in western populations with a prevalence of 25–30% [11] . The complexity and poor understanding of the NAFLD spectrum is emphasised by the small number of associated genetic variants identified by genome wide association studies [12] . Numerous metabolic pathways are involved in NAFLD pathogenesis ( cell development , inflammation , fibrosis , endoplasmic reticulum stress , lipid and glucose metabolism , etc . ) , in addition to environmental factors and aberrant xenobiotic metabolism [13] . Thus , its multifactorial nature suggests that NAFLD is better described as a “network” disease instead of the currently accepted “two” or “three hit” disease hypothesis [14] , [15] . SteatoNet analysis undertakes an engineering solution to provide evidence for the systemic multi-tissue characteristic of NAFLD initiation and highlights the utility of multi-tissue models that include regulatory aspects to investigate complex diseases .
To validate the SteatoNet , we simulated metabolic conditions that have been well studied , including the response to fasting , the absence of stearoyl-CoA desaturase ( SCD ) , a crucial lipogenic enzyme , overexpression of adiponectin , an insulin-sensitising anti-inflammatory cytokine released by the adipose and hepatic steatosis triggered by a high-fat diet , which can be subsided on treatment with a peroxisome proliferator-activated receptor alpha ( PPARα ) agonist . The fold-change in variable values in each simulation is with respect to their normalized value of 1 . 0 at the initial steady state . Hence , at steady state the observed changes are the net result of model perturbation . In the case of inconsistencies between experimental observations and model simulations , a series of simulations was implemented to identify the network components that display erroneous behaviour . Additional expert-based literature searches were performed to identify regulations that have been established experimentally but were absent in the network .
As suggested by Lanpher et al [49] , most complex diseases can be described as less severe cases of Mendelian inborn errors of metabolism ( IEM ) in which several pathways are subtly affected . The cumulative effect of genomic variations and environmental/dietary factors results in altered metabolic flux distributions and a broad spectrum of disease phenotypes . Thus , utilising a holistic approach to study disease-related networks can provide a clearer portrait of the systemic deregulations in complex diseases . It can also guide drug development strategies towards systems medicine and multi-targeting approaches . In this article we present SteatoNet , a closed multi-compartmental metabolic network , which serves as an in silico platform to systematically investigate hepatic metabolic phenomena and associated deregulations . Validation of SteatoNet and identification of deregulated flux disturbances that have been proven experimentally in NAFLD patients or in animal models highlights the ability of SteatoNet to describe biological behaviour by steady-state analysis in the absence of experimentally determined kinetic parameters . The problem of parameter estimation in complex models is a major limiting factor for accurate reconstructions of biological pathways . Models thus often involve multiple assumptions . Our approach bypasses this hurdle and underlines the importance of expansiveness and structural accuracy for representation of biological systems . This methodology is sufficient for qualitative modelling whereby the model is utilized to study particular biological phenomena and hypothesize/identify network-wide changes in response to particular disturbances . Although the method may not provide precise absolute values of the variables , it overcomes a major problem in biological modelling by bypassing the need to fix enzymatic parameters for an enormous number of enzymes in such large-scale multi-tissue models . The application of modelling techniques from machinery , based on object libraries and closed circuits , proves to be appropriate and efficient . The physiological state of an organism is a result of complex system-wide interactions and hence , analysing individual pathways in isolation leads to loss of information and inaccurate interpretations . Analysing the broad scheme of the ‘candidate’ disease network and then channelling efforts towards specific molecular interactions is a fundamental primary step in systems pathobiology . A dominant and unique feature of SteatoNet is integration of the metabolic network with regulation at the gene expression and signal transduction levels , which contributes to its robustness and effective concurrence with biological behaviour . In spite of its limited quantitative predictive value resulting from bypassing kinetic parameters and integrating multiple types of complex information ( signalling , gene expression , metabolic reactions ) , SteatoNet provides an effectively regulated system-wide virtual space for generating scientifically sound hypotheses . The omission of details in the model may have an important impact on the small-scale cell metabolism but is completely masked at a network-wide scale . The SteatoNet is thus the first integrated model of human metabolism that represents the multiple layers of metabolic regulation . Experimental evidence has indicated the systemic nature of NAFLD pathogenesis , involving the role of the adipose tissue [50] , skeletal muscles [51] intestine [52] and the heart [53] , however , directed efforts to study NAFLD as a systemic condition were lacking . iHepatocytes2322 , a comprehensive genome-wide network reconstruction of hepatocyte metabolism was utilised to analyse transcriptomic data from NASH patients and identify diagnostic biomarkers [8] . While this study identified the role of pathways such as serine metabolism in NASH , the model is inadequate to capture systemic metabolic changes in the organism , which is especially critical in the initial steatosis stage of NAFLD as observed from our study . Sensitivity analysis of the SteatoNet was aimed to identify small changes in pathway fluxes that significantly influence hepatic triglyceride concentration . Such focal points in the network have a significant effect on NAFLD onset , as small disturbances in the metabolic network can result in large effects on triglyceride concentration . To-date , a limited number of polymorphisms have been associated with NAFLD populations indicating the absence of dominant genetic aberrations that are causal of this disorder . Taking into consideration the complexity of NAFLD , it can be expected that the disease is initiated as a result of multiple subtle changes in the metabolic homeostasis ultimately resulting in hepatic steatosis . We provide evidence that NAFLD is not solely the hepatic manifestation of the metabolic syndrome but arises as a result of network-wide perturbations in flux distributions at the organism-level , ultimately resulting in hepatic steatosis . The critical dependence of hepatic triglyceride concentration on inter-tissue transport reactions highlights the multi-compartmental nature of NAFLD and thus , disruption in the homeostatic balance of lipid distribution is at least partially causal of the disease state . The accumulation of hepatic triglycerides may initially be triggered due to a deregulation in pathway branch-points that are “intolerant” to flux alterations . The enrichment of these focal points in the adipose compartment for maintaining a balance in triglyceride synthesis , storage and hydrolysis , may indicate the initial role of adipose malfunction in triggering hepatic steatosis . The “threshold effect” displayed by these branch-points may be specific to individuals and may potentially explain the variability in NAFLD pathogenesis . This trigger may elicit functional changes in regulatory factors , such as FXR , which in turn activate a cascade of alterations in the rest of the network . In addition to confirming hepatic triglyceride sensitivity to previously identified deregulations in NAFLD , the SteatoNet identified novel candidate mechanisms , such as cholesterol transport , ketone body metabolism and regulatory functions of FXR , LXR and SREBP-2 , that require experimental focus in the future . However , potential mechanisms that result in altered flux distributions remain an open question . Regulation of metabolic flux is a complex phenomenon that cannot be pinpointed to a single mechanism , as metabolic pathways are intricately connected and involve multiple enzyme isoforms with different substrates that are regulated by numerous factors at the transcriptional and translational level . Moreover , the inconsistency between the high prevalence of NAFLD and the small number of genetic variants identified in association with the disease indicates a critical role of environmental and dietary factors . The ability of SteatoNet to simulate the effect of the diet on metabolic homeostasis is a vital feature to investigate complex disorders and provides an advantage over currently available models . Dietary factors cause diverse changes in the organisms' metabolic status by triggering the activation or inhibition of regulators , such as hormones , cytokines and nuclear receptors [54] . SteatoNet analysis , which was conducted on a high-glucose and high-triglyceride intake background , highlighted the shift in the metabolic steady state on excessive dietary lipid or carbohydrate intake towards overproduction of the resulting metabolite substrates , which eventually causes enzyme saturation and a shift of metabolic flux into alternative pathways . Thus , individuals with genetic variations or inborn errors in metabolism affecting the activity or concentration of multiple enzymes or regulatory factors may be predisposed to hepatic steatosis as a result of inherent deregulations in flux distributions , with further adverse effects in the absence of suitable dietary measures . In summary , we highlight the novelty of SteatoNet as a highly regulated multi-tissue platform , with the flexibility to systematically investigate genetic and dietary effects influencing hepatic metabolic homeostasis , in the absence of constrained kinetic parameters . SteatoNet computationally suggests that a single ‘hit’ is not sufficient to trigger hepatic steatosis . The concerted and cumulative action of various focal points in a systemic network are responsible to elicit significant phenotypic changes . SteatoNet thus promotes the application of basic engineering modelling strategies to solve complex biological questions in a simple and efficient manner .
An object-oriented modelling and simulation programme based on the Modelica language , Dymola ( Version 7 . 4 , Dassault Systems , Lund , Sweden ) [55] , was utilised for the construction of SteatoNet in a systematic workflow summarised in Fig . 1 . Dymola is best known in engineering industries but less in systems biology applications . An advantage of object-oriented modelling is the reusability of general equations and model object classes with a user-friendly graphical interface . We chose Dymola for the modelling purpose due to its ability to define user-specific model objects and libraries , handle large , dynamic , multi-domain models and generate simulations rapidly . A systems biology library of objects was generated based on differential algebraic equations ( DAEs ) corresponding to biological pathway entities , as described here and previously [3] . The basic objects of the library include enzymes , metabolites , non-enzymatic regulatory proteins , mRNAs , genes , flux sources and positive and negative regulatory objects for gene expression and protein activity ( S1 Figure ) . SteatoNet was generated by compilation of these objects by linking them with connectors , thus forming a closed multi-pathway network ( Fig . 2 ) . The pathways in SteatoNet are based on their curation in Kyoto Encyclopaedia of Genes and Genomes ( KEGG , http://www . genome . jp/kegg/ ) and the Reactome ( www . reactome . org ) databases . Regulation at the transcriptional and post-translational levels has also been incorporated following expert-based manual inspection of the literature . The literature was manually scanned ( >500 articles ) to select studies that identify and confirm regulatory interactions in various metabolic pathways prior to the incorporation of regulation in the SteatoNet . Putative interactions that have not been confirmed were not included in the model . The inclusion of nuclear receptors in the SteatoNet was based on their confirmed role in the glucose , lipid and amino acid metabolic pathways portrayed in the SteatoNet . The included nuclear receptors were verified for their regulatory role and ligands by extensive manual search of the literature and databases such as NURSA ( Nuclear Receptor Signalling Atlas ) . Hence , only receptors with known expression , endogenous ligands and specific targets in either the liver/adipose/muscle tissues were included in the SteatoNet . Regulatory interactions that were unintentionally omitted were identified during the model validation procedure and incorporated in the model . Thus , the incorporation of the regulatory layer in the model was done in an iterative manner to ensure correspondence with biological behaviour . The pathways included are glycolysis , gluconeogenesis , citric acid cycle , pentose phosphate pathway , de novo lipogenesis , β-oxidation , lipolysis , amino acid metabolism , ketone body synthesis and the transport of metabolites between the liver , adipose tissue , pancreas , other extra-hepatic tissues and macrophages via the blood . The external nutrient sources ( influx of glucose , fats/triglycerides , cholesterol and essential amino acids ) have been incorporated into the SteatoNet . These influxes represent the dietary intake and intestinal absorption of metabolites into the portal vein or secretion into the intestinal lymph ( in the case of chylomicrons ) , which supply nutrients to the liver . The enzyme levels are governed by gene expression objects , which are regulated by transcription factors , such as PPARα , PPARγ , SREBP-1c , SREBP2 , LXR , FXR , glucocorticoid receptor and PPARγ coactivators 1 alpha ( PGC1A ) . The regulatory action of the hormones insulin and glucagon , the adipokines leptin and adiponectin and the cytokine TNFα has also been incorporated . The metabolites , enzymes and non-enzymatic regulatory proteins included in SteatoNet and the pathways they are associated with , are listed in S3 , S4 , S5 Tables S6 and S7 Tables provide detailed lists on the transcriptional and post-translational regulators of metabolism included in the model , their targets and the type of regulatory function elicited by them . An open-access version of the SteatoNet along with the systems biology library has been included in the supplementary materials ( S1 and S2 Datasets ) . These files can be accessed by the OpenModelica Connection Editor ( OMEdit ) software [56] , which can be downloaded for free online ( https://openmodelica . org/openmodelicaworld/tools ) . S1 Text provides instructions to access the model in OMEdit . The distribution of fluxes at pathway branch points is defined by additional model equations that set the initial ratio of flux distribution from the parent pathway into each branch . Thus , the distribution of the metabolic flux , f , in each of the pathway branches is an additional independent parameter that must be specified in the model . Vo et al [57] generated a comprehensive flux network based on isotopomer tracer analysis in HepG2 cells . The estimated reaction fluxes from this study were utilised to approximate f at various SteatoNet branching points . The flux distribution proportion was calculated by summing the total flux at the branch-point and determining the proportion entering each branch as a fraction of the total flux . The choice of tracers in [57] and utilization of cell lines prevented identification of flux distributions in the lipid metabolism pathway , the transport of metabolites and distributions among tissues . Thus , at branch points with uncertainty in the value of f , an arbitrary value was assigned that resulted in stable model simulations . For several low tolerance branch-points , the value of f was not completely arbitrary as these focal points can tolerate only a low range of flux distributions . The flux estimates from the Vo et al study were utilized to approximate f at branch-points within SteatoNet in order to gauge a physiological value for parameter assignment , while taking into consideration that in vitro isotopomer studies are subject to large variances due to differing cell culture conditions , sampling , pre-analytical processing , etc . [58] . It must be highlighted that due to the qualitative nature of the model and the normalization of model parameters , the assignment of values to the parameter f is critical in terms of model identification rather than quantitative reproduction of hepatic function . Hence , the assignment of arbitrary flux distribution values is feasible , provided that the model generates stable simulations and can simulate biological phenomena for validation purposes . In addition , immortal cell lines such as HepG2 display alterations in dynamics compared to primary cells and do not realistically depict organism-level flux distributions [59] . Consequently , the approximation of network flux distributions provides a reference stable steady state for comparison with model responses on perturbation , instead of precise quantitative estimations . The modelling method based on DAEs and analysis in steady-state utilised to build the model have been described previously [3] . The reaction dynamics ( Fig . 3 ) is described by four equations in an extension to the Michaelis-Menten model of enzyme kinetics: ( 1 ) ( 2 ) ( 3 ) ( 4 ) S , E , C and P denote the concentrations of the substrate , enzyme , enzyme-substrate ( ES ) complex and product , respectively . Constants kC , kP , kCR and kPR represent the rate constants of complex formation , product formation , ES complex dissociation into the enzyme and substrate and product reversion to the enzyme-product ( EP ) complex . φI , φO , φEI and φEO correspond to the substrate influx , the product efflux , the enzyme influx and the enzyme degradation flux . f denotes the proportion of the total substrate influx into alternative pathways . Concentrations of S , E , C and P at steady-state are represented by SSS , ESS , CSS and PSS . These variables can be converted into dimensionless quantities by normalising them with their steady-state counterparts . Thus , ( 5 ) The variables have been deliberately normalized to non-dimensional variables with the goal to observe relative changes of the variables . The normalization only affects the variable values while the systems dynamics remained unchanged . Extending the derivations described by Belič et al [3] , an additional steady-state ratio is described to define the relative concentration of bound and free enzyme: ( 6 ) The normalised value of the complex is determined in terms of the free enzyme concentration at steady state rather than the steady-state complex concentration . Thus , ( 7 ) Similar to derivations in [3] , the steady state normalised values SN , EN and PN are equal to 1 and according to the relation described above , CN = w . The rate constants are now described with incorporation of w: ( 8 ) ( 9 ) ( 10 ) ( 11 ) All model variables can be uniquely calculated with the knowledge of the reversibility of the reaction r , the distribution of the influx f into alternative pathways , the total influx φI and the ratio between the bound and free enzyme , w . To derive the relations of the variables at the new steady-state , , , , and f* at which the system settles in an event of disturbance , we substitute these variables into the steady-state form of equations 1 and 2: ( 12 ) ( 13 ) This allows solving for the new steady-state concentration of the enzyme , substrate and product: ( 14 ) ( 15 ) ( 16 ) Equations 14–16 illustrate that apart from the classical interdependence between substrate , product and enzyme concentration , as stated by the Michaelis-Menten relations , the relative concentrations in the new steady-state depend also on the distribution of the influx into alternative pathway branches , but they do not directly depend on the absolute value of the total metabolic flux . The reversibility of a reaction , r , is an inherent property of the enzyme and the equilibrium constant between the species involved in the reaction . r represents the initial ratio of the reverse and forward metabolic flux , required to estimate the model parameters at the initial steady state . The parameter r was fixed based on literature searches for reactions with known reversibility ratios . For reactions with known reversibility but lack of specific defined ratios , r was fixed as 0 . 5 and for irreversible reactions r was fixed as 0 . When the network is disturbed , a new steady state is reached where the reverse-forward flux ratio ( when r>0 i . e . for reversible reactions ) depends on the entire network dynamic properties and the type and magnitude of the disturbance . Most reactions in higher organisms have low reversibility , thus r is small , however , our simulation studies show that model performs well even for values of r above 0 . 5 . Although , the squared form of r diminishes its influence on the concentration of the substrate , enzyme or product , it accounts for the thermodynamic constraints related to Gibb's free energy in the reactions . The flux distribution , f* , in metabolic pathways in the event of a disturbance adapts depending on the newly attained values of substrate influx , demand of downstream pathways and alterations in enzyme concentration in the acquired steady state and also on the initial network flux distribution . Thus , the value of f* is calculated from the variable values at the new steady state attained after the disturbance . In addition to the biological entities involved directly in metabolic reactions , feedback regulation was also incorporated into SteatoNet . mRNA transcription was modelled as a sigmoid function with separate object classes specified for positive and negative expression regulation . Negative expression control is described by the following equation: ( 17 ) where QmRNA represents relative concentration of mRNA , Qmax is the maximum relative mRNA expression , φO is the transcription flux , QC represents the concentration of the regulatory molecule and kd depicts the rate of mRNA degradation . Similarly , positive expression control is described by the following equation: ( 18 ) where represents the maximum concentration of the regulator that results in the maximum fold-change in mRNA expression ( Qmax ) . The generation of protein is described as a linear relation between the relative concentration of mRNA and protein/enzyme quantity ( QP ) : ( 19 ) where kt represents the rate constant of mRNA translation and kd is the rate constant of protein degradation . The quantity of the regulatory molecule ( QC ) is usually controlled by activation or deactivation of the molecule by some metabolite quantity ( QM ) , which is described in a linear manner . The rate equation for protein activation is: ( 20 ) The rate equation for protein inactivation is: ( 21 ) Where QCI and QCA represent the pool of inactive and active protein , ki and ka are factors describing the activation or inhibition of the protein and the kd term in equations 20 and 21 accounts for the rate of protein degradation . Although the linear representation of translation and post-translational protein regulation provides a simplified depiction of the actual process , it sufficiently represents biological regulatory mechanisms and is considerably more informative compared to models without any feedback control . To determine if the SteatoNet correctly depicts biological phenomena , simulations were compared to experimental data from the literature . The model was translated by the Microsoft visual studio 10 . 0 C compiler and simulations were generated by the default multi-step dassl solver in Dymola with a tolerance set to 1e-009 . Model simulation is a multi-step numerical procedure that runs from time = 0 until arbitrary selected end time . In each step every equation of the model is evaluated . First , the derivatives of the model variables are calculated and next , the derivatives are used to change the values of the variables . The size of the simulation step is determined such that the derivatives of the variables are not too large since this would affect the simulation precision . In the case of the SteatoNet simulations , the dassl algorithm [60] was used . The selection of end time is crucial to observe the convergence to the new steady state and hence , must be selected such that all the model variables settle at a continual value , which is identified by observing the variables' time profiles . Taking into consideration the assumptions of the model , the initial steady state concentration of all metabolites and enzymes is 1 . 0 . On perturbation , the model variables are estimated based on the input values of r , f , w and φI . The simulations depict relative changes in the network components in response to triggers causing a shift from the initial steady state . The large number of model parameters in the SteatoNet presents an additional facet for model validation . In order to obtain further credibility of the model simulations , a parameter sensitivity analysis was performed to observe the sensitivity of simulation results with respect to f , as detailed in section 1 . 2 . 1 . The effect of r has been previously tested on smaller models showing a minimal effect on model variables than f . w only affects the dynamics of transition from one steady-state to another and hence , does not influence the variable values at a particular steady-state . For the various substrate influxes in the SteatoNet ( normalized as 1 at the initial steady state ) , several values of φI have been tested and these directly affect variable values , as observed in the fasting ( Fig . 4a ) and high-fat diet simulations ( Fig . 5 ) . Thus , the values of f and φI display the most prominent effect on model variable values in response to a disturbance . The dependence of model behaviour on parameters can be determined by sensitivity analysis , which is defined as the change in the model property versus the change in a parameter value . Metabolic control analysis ( MCA ) is an extension of local sensitivity analysis to determine the extent of change in metabolic flux or other systemic properties achieved by a fractional change in enzyme activity . MCA is quantified by control coefficients , which is termed as the flux control coefficient if the change in flux is considered as the model variable , or as the concentration control coefficient if the change in concentration is considered as the model variable . Relevant to this article and NAFLD-associated triglyceride accumulation , we can define the concentration control coefficient as the partial derivative of the change in triglyceride metabolite concentration with respect to small changes in the distribution of fluxes at various pathway branch-points . Thus , the sensitivity/concentration control coefficient is calculated by:where is the concentration control coefficient of parameter f with respect to hepatic triglyceride concentration . TG and TG* are the corresponding triglyceride concentrations at flux distribution values of f and f* . The second term in the equation i . e . the ratio between the initial flux distribution and triglyceride concentration , is incorporated to obtain relative sensitivity coefficients that are dimensionless . Graphically , the control coefficient is the tangent to the curve describing the relation between the metabolite concentration and model parameter variation and thus is dependent on the steady state under investigation . To determine the sensitivity of hepatic triglyceride concentration to the metabolic flux distribution , the glucose and triglyceride influx into the network was increased by 10-fold to simulate disturbance of the system on a high calorie diet . The distribution parameter for each branch point in the pathway model was varied by an interval of 10% . The corresponding changes in hepatic triglyceride synthesis were recorded and concentration control coefficients were calculated using equation 22 .
|
In this article we present SteatoNet , the most comprehensive computational network of hepatic metabolism and the interaction of the liver with extra-hepatic tissues . Generation of the SteatoNet involved the application of engineering strategies to resolve prevalent drawbacks in biological modelling and thus effectively understand basic biology . SteatoNet does not require detailed kinetic parameters , behaves representatively of biological observations and portrays systemic interactions . SteatoNet is simple and flexible , which is an important advantage over current computational models of complex metabolic disorders associated with the liver . To demonstrate the utility of SteatoNet as a hypotheses-generation tool , we studied candidate mechanisms of hepatic fat accumulation , the key characteristic of non-alcoholic fatty liver disease ( NAFLD ) , a common but poorly understood metabolic disorder influenced by genetic and lifestyle factors . Our data describe NAFLD as a network disease , with deregulated pathways such as inter-tissue transport of glucose , lipid and ketone bodies , cholesterol metabolism and “stiff” focal points in the fat tissue . These results indicate the involvement of systemic metabolic deregulations in the transformation of healthy to fatty liver in NAFLD . The SteatoNet highlights the utility of engineering approaches in systems biology to aid in solving complex biological questions .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"carbohydrate",
"metabolism",
"medicine",
"and",
"health",
"sciences",
"metabolic",
"processes",
"integrative",
"physiology",
"metabolic",
"networks",
"signaling",
"networks",
"liver",
"diseases",
"metabolites",
"network",
"analysis",
"gastroenterology",
"and",
"hepatology",
"lipid",
"metabolism",
"amino",
"acid",
"metabolism",
"lipids",
"computer",
"and",
"information",
"sciences",
"lipogenesis",
"fats",
"metabolic",
"pathways",
"regulatory",
"networks",
"metabolic",
"disorders",
"cholesterol",
"systems",
"biology",
"biochemistry",
"biochemical",
"simulations",
"computer",
"modeling",
"lipid",
"aggregates",
"biology",
"and",
"life",
"sciences",
"computational",
"biology",
"fatty",
"liver",
"metabolism"
] |
2014
|
SteatoNet: The First Integrated Human Metabolic Model with Multi-layered Regulation to Investigate Liver-Associated Pathologies
|
Close to 800 million people in the world are at risk of schistosomiasis , 85 per cent of whom live in Africa . Recent studies have indicated that female genital schistosomiasis might increase the risk of human immunodeficiency virus ( HIV ) infection . The aim of this study is to quantify and analyse the characteristics of the vasculature surrounding Schistosoma haematobium ova in the female genital mucosa . Cervicovaginal biopsies with S . haematobium ova ( n = 20 ) and control biopsies ( n = 69 ) were stained with immunohistochemical blood vessel markers CD31 and von Willebrand Factor ( vWF ) , which stain endothelial cells in capillary buds and established blood vessels respectively . Haematoxylin and eosin ( HE ) were applied for histopathological assessment . The tissue surrounding S . haematobium ova had a higher density of established blood vessels stained by vWF compared to healthy controls ( p = 0 . 017 ) . Immunostain to CD31 identified significantly more granulation tissue surrounding viable compared to calcified ova ( p = 0 . 032 ) , and a tendency to neovascularisation in the tissue surrounding viable ova compared to healthy cervical mucosa ( p = 0 . 052 ) . In this study female genital mucosa with S . haematobium ova was significantly more vascularised compared to healthy cervical tissue . Viable parasite ova were associated with granulation tissue rich in sprouting blood vessels . Although the findings of blood vessel proliferation in this study may be a step to better understand the implications of S . haematobium infection , further studies are needed to explore the biological , clinical and epidemiological features of female genital schistosomiasis and its possible influence on HIV susceptibility .
Schistosomiasis is the most important parasite infection in the world after malaria [1] . Of the estimated 780 million people exposed to this fresh water parasite , 85 per cent live in Africa . Schistosoma haematobium and human immunodeficiency virus ( HIV ) are co-endemic in large parts of this area [2] , [3] . In a recent cross-sectional study from Zimbabwe a near 3-fold increased odds for HIV infection was found in women with genital schistosomiasis [4] . The lower female reproductive tract is a major entry site in HIV transmission , and is also a common site for S . haematobium oviposition [5]–[7] . The S . haematobium infected cervix appears inflamed with abnormal mucosal blood vessels , contact bleeding and pathognomonic lesions named sandy patches [7]–[9] . It has been suggested that products from schistosome ova ( S . mansoni ) may induce endothelial cell proliferation and activation [10] , [11] . Similar to sexually transmitted infections , it has been postulated that female genital schistosomiasis may provide mucosal points of entry for HIV [12] , [13] . The aim of this study is to combine immunohistochemical protein detection of endothelial cells with histopathological evaluation to quantify and analyse the characteristics of the blood vessels surrounding S . haematobium ova in biopsies of the lower female genital tract .
Permissions for the histopathological and immunohistochemical investigations of anonymised archival Malawian and Norwegian biopsies , without additional consent from the study subjects , were granted by the National Health Science and Research Committee of Malawi ( 2009/NHSRC #620 ) and the Norwegian Regional Ethics Committee ( 2009/1250a ) . The permissions were based on the fact that the proposed analyses did not require identifiable information or history , have any direct relevance to the physical , mental or social well-being , or have any direct diagnostic or therapeutic implications for the study women . The majority of the Malawian women who volunteered in the 1994 study were illiterate [8] . Study information was provided in Yao and Chichewa ( the local languages ) and free informed oral consent was obtained . Following consent , all women who had urinary schistosomiasis were offered gynaecological examination . Consent was reascertained orally by the physician before each step of sampling and investigations . Treatment and follow-up for sexually transmitted infections , cancers and other complaints were done in collaboration with the physicians in Mangochi District Hospital [8] . The women were not asked for HIV testing . All women , including those who declined further investigations , were offered treatment with praziquantel . All non-endemic controls were followed up by the referring clinician in Norway . As described previously [8] , biopsies from the cervix and/or vagina were sampled from 61 women with urogenital S . haematobium infection in Mangochi District Hospital in Malawi in 1994 . In short , sexually active women between 15 and 49 years of age present in the out-patient department , irrespective of whether they were patients or next of kin , were invited to submit urine samples . Women with S . haematobium ova in the urine were invited for further interviews and gynaecological examinations . Samples including biopsies were taken from observed lesions or at random if no lesions were present . Women without urinary schistosomiasis were excluded . An overview of the study groups is given in Figure 1 . The cases with S . haematobium ova in genital tissue included 17 cervical and three vaginal biopsies . The histopathological changes and ova viability were similar in the cervical and vaginal biopsies , and the biopsies were therefore analysed together . Malawian women with urinary schistosomiasis who were found not to have S . haematobium ova in the biopsy specimen served as endemic negative controls . Non-endemic Norwegian control biopsies were selected by searching the database from 1998 for key word combinations of the anatomical site ‘cervix uteri’ and the morphological diagnoses ‘cervicitis’ and ‘normal morphology’ . Women included in the negative non-endemic control group had histologically normal uterine ectocervices with intact tissue architecture . Excluded were endocervical biopsies and biopsies showing signs of pathology , i . e . more than 10 inflammatory cells per high-power field , non-specific vessel proliferation , epithelial hyperplasia , polyps , cysts , atypical or dysplastic changes , or signs of human papilloma virus ( HPV ) infection ( i . e . clustered koilocytosis and dyskeratosis ) . Women included in the positive non-endemic control group had chronic non-specific cervicitis; i . e . biopsies with a distinct , generalised infiltrate dominated by lymphocytes and/or plasma cells . Excluded were endocervical biopsies with more than 20 granulocytes or 5 eosinophils per high-power field and specimens with signs of HPV infection , erosion , ulcerations , granulation tissue or extra-vascular erythrocytes . The biopsies were fixed in formalin , routinely processed , embedded in paraffin , sectioned and stained . Findings from the histological examination of haematoxylin and eosin ( HE ) -stained sections have been published previously [14] . For the analyses in this study , 3 . 5 µm thick serial sections of the included specimens were cut and placed on SuperFrost slides ( Menzel-Gläser , Braunschweig , Germany ) for HE-stain and on SuperFrost Plus slides ( Menzel-Gläser ) for immunohistochemical stains . The immunohistochemical stains were performed using Benchmark XT , Antibody diluent ( 251-018 ) and Detection Kit Ventana ultraView Universal DAB ( 760-500 ) ( Ventana Medical Systems , Inc . , Tucson , Arizona , USA ) , an automated immunostain system based on the ABC avidin-biotin-peroxidase method , including negative and positive controls . For identification of blood vessels , two endothelial cell markers were used; mouse monoclonal antibody CD31 ( clone JC70A , Dako Denmark AS , Glostrup , Denmark ) and rabbit polyclonal antibody von Willebrand Factor ( vWF ) ( Ventana ) . All slides were examined microscopically for immunohistochemical antigen detection combined with histological identification . The sections were examined using a Nikon Eclipse 80i microscope and photographed at 40× objective magnification , obtaining 1600 by 1200 pixels colour images using a SPOT Insight 2 Megapixel Firewire Color 3-shot digital camera attached to the microscope and a Hewlett-Packard Compaq stationary computer . The morphological and immunohistochemical analyses were performed in a standardised manner . First , the HE-stained sections were evaluated . Ova were defined as ‘viable’ if miracidia with eosinophilic glands or germinal cells were identified [15] , whereas ova containing dark purple stain identified histologically as calcification were defined as ‘calcified’ . The histopathological tissue reaction was defined as ‘granulation tissue’ if dominated by neovascularisation with activation of endothelial cells and by immature fibroblasts . The tissue reaction was defined as ‘fibrosis’ if dominated by collagen rich stroma with scant mature fibroblasts . A high-power field ( 40× objective magnification ) of each HE-stained section containing S . haematobium ova was photographed with the ovum or ova located centrally . The area of tissue surrounding the ovum or ova in one such photograph was defined as ‘periovular’ . In the controls , one high-power field of a subepithelial area was photographed , as this is the most common location for oviposition [15] , [16] . This area was representative for the pathologist's overall diagnosis of the section . In order to analyse the same area in the consecutive immunohistochemically stained serial sections of each biopsy , the near-exact same areas , identified by histological anatomical structures , were photographed . In areas where the morphology of the immunohistochemically stained section differed from the HE-stained section , the closest approximation was photographed . All selections were controlled by an experienced pathologist ( BR ) . Immunostained blood vessels ( by vWF and CD31 ) were counted in accordance with pre-established criteria [17] , [18] . ‘Capillary buds’ were defined as vessel structures without an identifiable lumen or periendothelial structures , whereas all other stained vessel structures were counted as ‘established blood vessels’ . The density of capillary buds and vessels were calculated per mm2 . In sections with more than 300 capillary buds per mm2 , the numbers were truncated to 300 . Each photograph was counted manually and one in ten randomly selected photographs were quality controlled . Discrepancies were resolved by consensus , if necessary after consulting a second senior pathologist . Finally , each photograph was recounted . The statistical analyses and sample size estimation were performed with SPSS version 16 . 0 and PS Power and sample size calculations version 2 . 1 . 31 . Most variables were not normally distributed and non-parametric tests were therefore used when studying associations . Medians and interquartile ranges were used to describe the results . The Mann-Whitney U and Kruskal-Wallis H tests were applied where appropriate . For the calculation of odds ratio ( OR ) , it was necessary to tertilise the not normally distributed variables . Spearman's rank correlation coefficient was used when studying associations between continuous variables . Intra-observer variability was determined by calculating the intra-class correlation coefficient ( ICC ) after log-transformation of the data . A 5% significance level was used throughout .
The median age of the Malawian patients was 23 . 5 years ( interquartile range ( IQR ) = 20 . 0–31 . 5 ) , and the median age of the Norwegian control patients was 44 . 0 years ( IQR = 30 . 5–55 . 5 ) . In the biopsies with S . haematobium ova , the median number of ova per high-power field was 3 ( IQR = 1–8 ) , of which 80 per cent were calcified . Most ova were localised in the subepithelial tissue , whereas only a few ova were found deeper in the stroma . Calcified and viable ova were found together in the same biopsy in one patient only . No schistosome worms were seen . Figures 2 and 3A show the distributions of established blood vessels and capillary buds respectively per mm2 genital tissue in the four study groups . The periovular tissue contained significantly more established blood vessels compared with non-endemic healthy cervices ( p = 0 . 017 ) . However , ova viability was not significantly associated with the density of established blood vessels ( p = 0 . 40 ) . There was no significant difference in the density of established blood vessels between endemic biopsies with and without S . haematobium ova ( p = 0 . 51 ) . As shown in Figure 3A , there were no significant differences in the density of capillary buds between the Malawian cases and the non-endemic or endemic negative controls ( p = 0 . 44 and p = 0 . 95 respectively ) . However , there was a large variation in periovular capillary bud density . As shown in Figure 3B , there was a tendency towards a higher density of capillary buds surrounding viable ova compared with non-endemic healthy cervical tissue ( OR = 13 . 0 , Confidence Interval ( CI ) = 1 . 0–172 . 9 , p = 0 . 052 ) . Furthermore , viable ova were more often found in granulation tissue compared to calcified ova ( p = 0 . 032 ) . There was no association between calcified ova and fibrous tissue ( p = 0 . 53 ) . The main histopathological periovular tissue reactions are shown in Figures S1 and S2 . In the endemic cases with cervicovaginal S . haematobium ova , age was neither associated with the number of ova ( p = 0 . 45 ) nor the viability status of the ova ( p = 0 . 50 ) . There was no significant difference in density of established blood vessels or capillary buds between tissue with viable ova and non-endemic tissue with chronic cervicitis ( p = 0 . 20 and p = 0 . 053 respectively ) . All other study groups had significantly lower densities of established blood vessels and capillary buds than tissue with chronic cervicitis ( data not shown ) . The intra-observer reliability of counting established blood vessels and capillary buds were 0 . 93 ( 95% Confidence Interval ( CI ) = 0 . 90–0 . 95 , p<0 . 001 ) and 0 . 88 ( 95% CI = 0 . 82–0 . 91 , p<0 . 001 ) , respectively .
To our knowledge , this is the first study to analyse the quantity and characteristics of the mucosal vasculature surrounding female genital S . haematobium ova . Similar to non-specific chronic cervicitis , the mucosa of the Malawian women diagnosed with genital schistosomiasis was significantly more vascularised than healthy cervical tissue of non-endemic controls . The denser vasculature consisted of established blood vessels as opposed to currently active neovascularisation . However , cases with viable ova contained granulation tissue rich in sprouting blood vessels significantly more often than cases with calcified ova . Similar to previous reports of genital and urinary schistosomiasis , we found a variety of tissue reactions surrounding S . haematobium ova; ranging from marked periovular granulation tissue to fibrosis [14]–[16] , [19]–[25] . Although fibrosis is regarded as the end-stage pathology in schistosome infected tissue [26] , there was no significant association between calcified ova and fibrosis in our study . This study has several limitations . Firstly , the sample size is small and the findings are prone to type 1 and 2 errors . Secondly , there may have been schistosome ova just outside the biopsy borders in presumed negative cases , which might explain why there was no significant difference in capillary density between endemic cases and controls in this study . As in most schistosomiasis studies , there was no true schistosomiasis negative endemic control group [27] . The differences between women with and without genital S . haematobium ova may therefore have been underestimated . Thirdly , the analyses may have been affected by the age of the endemic biopsies , and by endothelial cell activation or vWF which may be elevated in HIV positive individuals [28] . The HIV prevalence has subsequently been estimated to be approximately 10 per cent of the population in 1994 [2] , but individual HIV diagnosis was not a part of the original project and could therefore not be done . Lastly , although the specimens were evaluated several times by two investigators who attempted to be objective , blinding was not possible due to the presence of schistosome ova in the tissue . Previous studies on blood vessel proliferation and schistosomiasis have suggested that soluble egg antigens ( SEA ) from viable S . mansoni ova induce neovascularisation by stimulating endothelial cell activation and proliferation [10] , [11] . Recent experimental studies indicate that blood vessel proliferation in S . mansoni not only accompanies hepatic fibrogenesis , but possibly also regression of fibrosis after treatment [29] . To our knowledge , there have been no studies on S . haematobium and blood vessel proliferation . Female genital schistosomiasis is associated with genital mucosal bleeding tendency; a clinical finding that might support the suggested HIV susceptibility in these women [30] . Endothelial cell surface proteoglycans have been reported to serve as HIV-1 receptors , and endothelial cells have even been suggested to harbour HIV [31]–[34] . Finally , an increase in microvessel density may impair the original tissue structure and lead to easy disruption of the genital mucosal barrier [35] , [36] . Women living in endemic areas are prone to high burdens of infection and frequent reinfections with S . haematobium [37] . The histopathological correlates of the clinical signs in female genital schistosomiasis , e . g . abnormal blood vessel proliferation and contact bleeding , have not yet been studied in detail [38] . It is therefore not known which clinical manifestations may increase the risk of reproductive tract morbidity or possibly HIV acquisition . Hence it is not yet possible to give clinical advice related to the risks and consequences of the typical lesions in female genital schistosomiasis . In order to determine the implications for the patients , further studies are needed to correlate the clinical signs to morphological and immunohistochemical findings . In conclusion , this study shows that female genital mucosa infected with S . haematobium is significantly more vascularised than healthy cervical tissue . Viable schistosome ova are more often surrounded by highly vascularised granulation tissue compared with calcified ova . These results might contribute to improve the understanding of the pathophysiological mechanisms in female genital schistosomiasis and of the postulated association with HIV infection . However , further studies are needed to validate these findings , to study the immunological tissue reaction and to explore the clinical correlates of the various histopathological manifestations in female genital schistosomiasis .
|
Schistosomiasis is a fresh water parasite infection that affects millions of people , especially in Africa . Recent knowledge about the genital manifestations of schistosomiasis; especially its possible association with human immunodeficiency virus ( HIV ) infection , has led to increased focus on this neglected tropical disease . Millions of women remain undiagnosed for genital schistosomiasis , and may suffer from abnormal mucosal blood vessels , contact bleeding and lesions named sandy patches . This study analyses a unique selection of female genital biopsies containing parasite eggs . Protein detection and standard histopathological assessment are combined to quantify and study the characteristics of the mucosal blood vessels surrounding the eggs . Our results show that the genital mucosa with parasite eggs is more vascularised compared to healthy tissue , and that viable eggs tend to be surrounded by proliferating blood vessels . These findings have not yet been correlated directly to clinical manifestations . Further studies are needed in order to provide clinical advice on the risks and consequences of mucosal lesions particular to female genital schistosomiasis .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"reproductive",
"system",
"immunology",
"microbiology",
"anatomy",
"and",
"physiology",
"gynecologic",
"infections",
"parasitic",
"diseases",
"parasitology",
"parasite",
"physiology",
"cardiovascular",
"histology",
"women's",
"health",
"neglected",
"tropical",
"diseases",
"immunologic",
"techniques",
"immunohistochemical",
"analysis",
"infectious",
"diseases",
"medical",
"microbiology",
"biology",
"pathogenesis",
"schistosomiasis",
"physiology",
"vascular",
"biology"
] |
2011
|
Increased Vascularity in Cervicovaginal Mucosa with Schistosoma haematobium Infection
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Parkinson´s disease ( PD ) is characterized by the presence of proteinaceous inclusions called Lewy bodies that are mainly composed of α-synuclein ( αSyn ) . Elevated levels of oxidative or nitrative stresses have been implicated in αSyn related toxicity . Phosphorylation of αSyn on serine 129 ( S129 ) modulates autophagic clearance of inclusions and is prominently found in Lewy bodies . The neighboring tyrosine residues Y125 , Y133 and Y136 are phosphorylation and nitration sites . Using a yeast model of PD , we found that Y133 is required for protective S129 phosphorylation and for S129-independent proteasome clearance . αSyn can be nitrated and form stable covalent dimers originating from covalent crosslinking of two tyrosine residues . Nitrated tyrosine residues , but not di-tyrosine-crosslinked dimers , contributed to αSyn cytotoxicity and aggregation . Analysis of tyrosine residues involved in nitration and crosslinking revealed that the C-terminus , rather than the N-terminus of αSyn , is modified by nitration and di-tyrosine formation . The nitration level of wild-type αSyn was higher compared to that of A30P mutant that is non-toxic in yeast . A30P formed more dimers than wild-type αSyn , suggesting that dimer formation represents a cellular detoxification pathway in yeast . Deletion of the yeast flavohemoglobin gene YHB1 resulted in an increase of cellular nitrative stress and cytotoxicity leading to enhanced aggregation of A30P αSyn . Yhb1 protected yeast from A30P-induced mitochondrial fragmentation and peroxynitrite-induced nitrative stress . Strikingly , overexpression of neuroglobin , the human homolog of YHB1 , protected against αSyn inclusion formation in mammalian cells . In total , our data suggest that C-terminal Y133 plays a major role in αSyn aggregate clearance by supporting the protective S129 phosphorylation for autophagy and by promoting proteasome clearance . C-terminal tyrosine nitration increases pathogenicity and can only be partially detoxified by αSyn di-tyrosine dimers . Our findings uncover a complex interplay between S129 phosphorylation and C-terminal tyrosine modifications of αSyn that likely participates in PD pathology .
Parkinson´s disease ( PD ) is one of the most common neurodegenerative diseases and affects about 1% of the population older than 60 years [1] . PD proceeds with selective loss of dopamine-producing neurons of the substantia nigra pars compacta in the ventral midbrain [2 , 3] . Degeneration also occurs in other neuron types . Particularly , the mid-section of the substantia nigra ( zona compacta ) is affected by neurodegeneration , which is accompanied by the loss of neuromelanin pigment neurons leading to depigmentation [4 , 5] . Loss of nigral dopaminergic neurons consequently results in dopamine depletion in the striatum and generates a wide range of motoric malfunctions [6] . PD is also described to be associated with non-motoric and non-dopaminergic symptoms that extend beyond the nigrostriatal dopamine pathway and often occur years or even decades prior to the clinical diagnosis [7 , 8] . Typical hallmark of PD is the formation of Lewy bodies that can be observed in post mortem brain histology . Lewy bodies are intracellular proteinaceous inclusions with α-synuclein ( αSyn ) as major constituent [9–11] . Several independent point mutations in the αSyn encoding gene , as well as duplications or triplications of the wild-type αSyn locus , have been found in rare familial inherited forms of PD [12–18] . This makes αSyn a hallmark protein for PD and other related diseases , which are summarized as synucleinopathies . αSyn is a natively unfolded protein , enriched at presynaptic nerve terminals . The nuclear localization of αSyn remains under debate , since conflicting results have been obtained for the existence of αSyn in nuclei of mammalian brain neurons [19–23] . αSyn was also reported to be localized in the nucleus of cultured neurons , where it may impair histone acetylation and thereby promote neurotoxicity [24 , 25] . αSyn is involved in the modulation of synaptic activity through regulation of SNARE-complex assembly of presynaptic vesicles , regulation of neurotransmitter release , regulation of cell differentiation and phospholipid metabolism [26–31] . Posttranslational modifications ( PTMs ) play an important role in regulating αSyn aggregation propensity and cytotoxicity . Major PTMs of αSyn include phosphorylation , ubiquitination , sumoylation or nitration [32–36] . The predominant αSyn modification is phosphorylation at serine 129 ( S129 ) . More than 90% of αSyn in Lewy bodies is phosphorylated at this residue , whereas only 4% of the soluble protein is accordingly modified [37] . The molecular function of phosphorylation at S129 is still under debate [38] . This modification modulates clearance of αSyn inclusions in a yeast model of PD [39 , 40] . In addition , phosphorylation at S129 can suppress the defects induced by impaired sumoylation such as increased number of cells with inclusions and reduced yeast growth [41] . These findings support a protective function for S129 phosphorylation in this model . Nitrated αSyn represents another PTM discovered in Lewy bodies [33 , 34] . Nitration might be involved in αSyn aggregation , thereby modulating αSyn-induced cytotoxicity . Oxidative and nitrative stresses are implicated in the pathogenesis of PD [33 , 34 , 42–45] . Neuroinflammation followed by nitration of αSyn causes accumulation of αSyn aggregates and neurodegeneration in mice [46] . Moreover , nitrated αSyn was observed to induce adaptive immune responses that exacerbate PD pathology in the MPTP mouse model [47] . Increased nitrated αSyn is present in peripheral blood mononuclear cells of idiopathic PD patients compared to healthy individuals [48] . These studies provide evidence for a direct link between nitrative damage and the onset and progression of neurodegenerative synucleinopathies . However , the precise molecular mechanism that leads to the formation of pathological inclusions is still elusive . Exposure of αSyn to nitrative agents results in the formation of αSyn oligomers and higher molecular weight αSyn species that are resistant to strong denaturing conditions , suggesting that αSyn proteins are covalently crosslinked [42 , 49–52] . This oligomerization can be abolished in vitro when αSyn lacks the four tyrosine residues at positions 39 , 125 , 133 and 136 [53] . Three of these four tyrosine residues are located at the C-terminal end of αSyn in close neighborhood to the protective S129 phosphorylation site . Nitrating agents such as peroxynitrite/CO2 ( PON ) can nitrate tyrosine to generate 3-nitrotyrosine . Alternatively , highly stable o , o’-di-tyrosine oligomers can be formed , including dimers , trimers and higher oligomeric species [42 , 54–56] . However , the majority of the studies were performed in vitro after exposure of αSyn to nitrating agents leading to non-specific nitration at all tyrosine residues . It is still unclear , whether the nitration-modified αSyn intermediates are toxic and what are the functional consequences of these modifications . Even the precise positions or preferred combinations for the tyrosines involved in di-tyrosine formation in vivo are yet unknown . The yeast Saccharomyces cerevisiae is an established eukaryotic model system used to uncover the correlation between structural features of αSyn and its toxicity . It provides a unique tool to study the molecular basis of PD in vivo [57 , 58] . 44% of the yeast genes reveal significant sequence similarities that suggest homology to human genes [59] . The basic molecular machinery necessary for neuronal function is conserved between yeast and humans . Strikingly , heterologous expression of different forms of human αSyn in yeast cells recapitulates central features of PD , including dose-dependent toxicity and aggregation . Expression of wild-type αSyn , E46K and A53T mutant results in significant growth inhibition and formation of inclusions [60 , 61] . An unusual feature of the yeast system , which is different from PD and other models , is that the A30P variant only forms inclusions when highly expressed and fails to display a growth inhibition in yeast , because aggregation of A30P is only transient [62 , 63] . Aggregation of αSyn in yeast cells causes mitochondrial dysfunction or formation of chemically reactive molecules , such as reactive oxygen species ( ROS ) and reactive nitrogen species ( RNS ) , which is similar to mammalian cells [60 , 64–69] . In this study , we analyzed the effects of nitration of wild-type and A30P mutant αSyn in yeast cells . We observed that the C-terminus of αSyn is preferentially modified by nitration compared to the N-terminus in soluble monomers as well as in dimers . Nitro- and phosphate-groups were found at Y133 . Tyrosine nitration leads to increased aggregation and cytotoxicity of αSyn in yeast and confers toxicity to the non-toxic A30P mutant . The yeast nitric oxide oxidoreductase Yhb1 as well as human neuroglobin , which are encoded by homologous genes conserved from yeast to man , reduced the number of cells with inclusions and protected yeast from A30P-induced mitochondrial damage . One target of these enzymes is the C-terminal Y133 and we could show that it is required for protective phosphorylation of αSyn at S129 . Our data revealed a complex choreography of posttranslational events at the αSyn C-terminus and suggest that tyrosine modifications promote subsequent S129 phosphorylation as part of a cellular control system , which contributes to the pathogenesis of the disease .
Exposure of αSyn to nitrating agents causes tyrosine nitration in vitro and leads to formation of covalently crosslinked αSyn dimers and inclusions [42 , 49 , 50 , 53 , 56] . High levels of αSyn with C-terminal HIS6-tags were heterologously expressed in yeast cells to uncover how nitration influences in vivo αSyn toxicity and aggregate formation . The first approach was to examine whether αSyn and A30P form dimers in vivo without additional exposure of the cells to nitrating or oxidative agents . αSyn and A30P expression was driven by the GAL1-promoter which was repressed in the presence of glucose and induced when shifted to 2% galactose-containing medium for 12 hours ( h ) . High copy number expression of the HIS6-tagged αSyn resulted in growth inhibition whereas high expression of the A30P mutant resulted in a similar growth rate as the yeast control without any αSyn ( Fig 1A ) . Similar results were previously reported with untagged or GFP-tagged αSyn and corroborate that the HIS6-tag does not interfere with the behavior of αSyn in yeast [60 , 63] . αSyn proteins were enriched by Ni2+ pull-down under denaturing conditions in the presence of urea . Immunoblotting with anti-αSyn antibody revealed distinct bands , corresponding to monomeric ( ~17 kDa ) , dimeric ( ~35 kDa ) and higher molecular weight αSyn species ( oligomers ) , detected from in vivo samples ( Fig 1B ) . This supports that αSyn and the A30P mutant form dimers and oligomers in vivo even without additional exposure of the cells to nitrating or oxidative agents . Dimer and oligomer formation of αSyn in vivo was further analyzed by comparison to additional in vitro nitration [42] . PON ( ONOO- ) was applied as nitrating agent for αSyn tyrosine residues because it leads to the formation of stable αSyn oligomers . PON is formed by the reaction of superoxide ( ∙O2- ) with the free radical nitric oxide ( ∙NO ) . PON represents a major nitrating agent that causes tissue injury in several neurological disorders [70 , 71] . αSyn and A30P proteins were expressed in yeast , pulled-down using Ni2+ and exposed to PON . Immunoblotting of the in vitro nitrated proteins revealed that the abundance of dimers and oligomers was significantly increased with the same pattern as for in vivo isolated αSyn species ( Fig 1B ) . The major distinct band corresponds to the αSyn dimer species . Quantification of the dimer band intensities of in vivo isolated probes revealed that A30P forms approximately twice as many dimers relative to monomers as wild-type αSyn . In vitro nitration of αSyn and A30P increased the total amount of dimers ( Fig 1C ) . However , the dimer to monomer ratios between αSyn and A30P were not changed when the in vivo samples were enhanced by additional in vitro nitration ( Fig 1C ) . Our results suggest that the high molecular weight variants of αSyn , which can be isolated from yeast cells and which withstand strong denaturing conditions during the pull-down ( 8 M Urea , 2% SDS ) , represent covalently crosslinked αSyn species . These data support the formation of αSyn dimers in living cells . A remarkable result is that the toxicity of αSyn , which correlates to a high protein aggregation rate [63] , results in a reduced amount of αSyn dimer relative to monomer formation . In contrast , the non-toxic A30P mutant that does not inhibit cellular growth ( Fig 1A ) , and has a reduced aggregation rate , produces twice as many dimers relative to monomers in comparison to wild-type αSyn . This suggests that αSyn dimer formation is a molecular mechanism which can be used by the cell as salvage pathway for detoxification . Liquid chromatography–mass spectrometry ( LC-MS ) analysis was performed to analyze αSyn and A30P nitration sites in vivo . Single trypsin or AspN digestions were employed and the resulting peptides were analyzed by LC-MS . In addition to single digestions , a combined proteolytic approach by double digestion of the proteins with trypsin and AspN was employed that enabled 100% sequence coverage . The modifications of the tyrosine residues identified from fragment spectra are summarized in Fig 2 . MS data revealed nitration of wild-type αSyn at all three C-terminal tyrosines ( Y125 , Y133 , Y136 ) . Nitration of A30P was restricted to Y125 and absent at Y133 or Y136 . Nitration of the additional tyrosine residue Y39 in the N-terminal domain of αSyn could not be identified from any in vivo samples by MS . Pull-down and additional PON exposure , however , resulted in Y39 nitration in all samples . This suggests that Y39 is not a primary in vivo nitration target within cells . Additional PON-exposure after pull-down also revealed that the αSyn dimers can be potentially nitrated in vitro . The increased in vitro PON-mediated nitration of the A30P in comparison to wild-type could be due to the higher amounts of the dimer in this mutant strain . Beyond nitration we could also identify phosphorylation in αSyn as well as A30P at S129 , Y125 or Y133 but not at Y39 or Y136 ( Table 1 ) . The probabilities for possible phosphorylation sites were calculated with the phophoRS algorithm [72] . Phosphorylation of Y125 was identified with only low probability scores ( Table 1 ) . In contrast , S129 and Y133 were almost completely co-phosphorylated with scores of 100% for S129 and 99% for Y133 , respectively . The LC-MS spectra of αSyn and A30P migrating in SDS-PAGE with the size of the dimer band were analyzed to assess whether di-tyrosines cause dimer formation of αSyn or A30P . The presence of di-tyrosine peptide crosslinks was validated using StavroX2 . 3 . 4 . 5 software [73] . This software compares the masses of all potential crosslinked peptides with the precursor ion masses , calculates b- and y-type ions for all possible crosslinks and compares them to MS2 data of the precursor ion . Different combinations of crosslinked peptides with an identical mass are possible when multiple tyrosine residues are located on one and the same peptide . The crosslinked tyrosine pairs were assigned according to the scores calculated by StavroX based on the fragment ion series of the MS2 spectra . The MS data analysis verified that αSyn dimers are crosslinked by tyrosine residues . The detected combinations of crosslinked tyrosines are depicted in Fig 3A . The results indicate a strong preference for crosslinking of defined combinations of tyrosines ( Figs 3A , 3B , 3C and S1 ) . The most frequent combinations for either wild-type αSyn or A30P are Y125-Y136 and Y133-Y136 dimers which are all located in the C-terminus . Only the C-terminal tyrosine residues can mutually interact . Only a small fraction of Y39-Y39 dimers were found and there are no tyrosine dimers between the N-terminal Y39 and the C-terminal tyrosines of αSyn or A30P . These data suggest that the C-terminus of αSyn or A30P has an increased susceptibility for nitration and di-tyrosine formation compared to the N-terminus . Only Y125 is a major nitration site of A30P . In contrast , all three C-terminal tyrosines Y125 , Y133 and Y136 of the wild-type αSyn are putative targets for nitration . Y133 is an additional strong and Y125 a weak phosphorylation site , respectively . Dimer formation through di-tyrosine follows a specific pattern for both tested αSyn proteins with predominant forms including Y136 interacting either with Y125 ( Y125-Y136 ) or with Y133 ( Y133-136 ) . The codons for the four tyrosine sites of αSyn and A30P ( Y39 , Y125 , Y133 and Y136 ) were replaced in the corresponding genes by phenylalanine codons ( 4 ( Y/F ) ) to analyze the role of the tyrosine residues on αSyn dimer formation , cytotoxicity or aggregation . Fusion genes with GFP-tags or HIS6-tags were constructed and expressed . We assessed whether the quadruple Y to F replacements influence the dimerization of αSyn and A30P . Expression of αSyn and A30P as well as their 4 ( Y/F ) mutants was induced for 12 h . Tagged proteins were enriched by Ni2+ pull-down under denaturing conditions . Immunoblotting using αSyn antibodies as well as antibodies that specifically recognize di-tyrosines revealed that 4 ( Y/F ) mutants of αSyn or A30P had lost the potential to form dimers in vivo ( Fig 4A ) . Additional in vitro nitration with PON did also not result in any dimer or oligomer formation and served as control ( Fig 4A ) . Immunoblotting analysis was carried out to determine in vivo nitrated αSyn using 3-nitro-tyrosine specific antibodies ( Fig 4B ) . The results demonstrated that the 4 ( Y/F ) variants of wild-type αSyn or A30P did not result in any nitration signal even after additional PON treatment . This is in contrast to wild-type αSyn with its four original tyrosine residues as control where nitration is present in vivo and can be further increased by additional PON treatment . The growth impact of wild-type αSyn or the A30P variant with that of the additional 4 ( Y/F ) substitutions were compared by spotting analysis and in liquid medium , respectively ( Fig 4C and 4D ) . Substitutions of the four tyrosine residues in wild-type αSyn significantly improved growth on solid medium , whereas A30P αSyn growth was similar with tyrosine or instead with phenylalanine residues ( Fig 4C ) . Growth in liquid medium resulted in similar effects , revealing significantly reduced growth inhibition of the 4 ( Y/F ) mutant strain in comparison to wild-type αSyn , whereas A30P and its A30P/4 ( Y/F ) derivative were growing similarly ( Fig 4D ) . Next , we assessed whether the decrease in wild-type αSyn toxicity was related to the formation of αSyn inclusions ( Fig 4E and 4F ) . No change in inclusion formation could be monitored when A30P was compared to A30P/4 ( Y/F ) . However , yeast cells expressing the 4 ( Y/F ) αSyn variant formed less inclusions in comparison to wild-type αSyn ( Fig 4E and 4F ) . Immunoblotting with αSyn antibody revealed that the protein levels of the different αSyn variants were similar ( Fig 4G ) . Taken together , only tyrosine replacements by phenylalanine in case of wild-type αSyn but not in case of an additional A30P substitution reduce αSyn-induced toxicity and inclusions formation . Accordingly , there is only growth improvement in the absence of an A30P substitution that correlates with decrease of intracellular accumulations of αSyn fluorescent foci . This supports that tyrosine residues that are responsible for nitration of αSyn contribute to the cytotoxic effect and inclusion formation of αSyn in yeast . This tyrosine-dependent effect is significantly less pronounced in the presence of an A30P codon mutation suggesting that A30P suppresses the tyrosine effect , which can be observed in wild-type αSyn . The presence of tyrosine residues in wild-type αSyn favor nitration and di-tyrosine-crosslinking but offer only a minor contribution to inclusion formation . The effect of nitrative stress on the toxicity and aggregation of wild-type and A30P mutant αSyn was examined . A yeast strain carrying a deletion in the yeast flavohemoglobin gene ( YHB1 ) , responsible for stress signaling , was used for enhancement of nitrative stress . Yhb1 is a nitric oxide oxidoreductase , which protects against nitration of cellular targets and against cell growth inhibition under aerobic or anaerobic conditions . Deletion of YHB1 abolishes the nitric oxide ( NO ) consuming activity of yeast cells [74] . The compound DETA-NONOate causes nitrative stress by acting as a NO donor . The absence of the flavohemoglobin results in a growth impairment of the hypersensitive yhb1 deletion strain in comparison to wild-type under NO nitrative stress conditions ( Fig 5A ) . The genes encoding wild-type or A30P αSyn , or GFP as a control , were expressed in Δyhb1 or the isogenic wild-type background . Cell growth was compared in the absence of nitrative stress by spotting assays ( Fig 5B ) . Wild-type αSyn was as well cytotoxic in the presence or absence of YHB1 . This was different for A30P , where no cytotoxicity was observed in the presence of YHB1 . However , expression of A30P in Δyhb1 cells inhibited cell growth . This effect was verified by low copy plasmid expression of YHB1 . Cells rescued with YHB1 showed the same growth phenotype as the original A30P or the GFP control in the YHB1 background ( Fig 5B ) . The correlation between growth inhibition and aggregate formation of αSyn variants was examined . Cells expressing αSyn or A30P were imaged by fluorescence microscopy and the cells displaying aggregates were counted . Deletion of YHB1 resulted in increased percentage of cells with A30P inclusions , whereas no significant difference was observed in cell expressing wild-type αSyn ( Fig 5C ) . In agreement with the growth analysis , the complementation of the Δyhb1 deletion by YHB1 rescued the lower aggregation potential of A30P . Deletion of YHB1 constitutes an internal stress signal . The effect of nitrative stress on A30P was further investigated by adding external nitrative stress conditions . Growth tests in liquid culture were performed using DETA-NONOate , which reduces growth of the Δyhb1 mutant but not of the wild-type strain ( Fig 5A ) . Cells expressing A30P αSyn grew uninhibited in the YHB1 wild-type background , whereas Δyhb1 cells expressing A30P were less inhibited than αSyn , thus recapitulating the growth phenotype in solid medium ( Fig 5B and 5D ) . In contrast , Δyhb1 cells expressing both αSyn variants were equally impaired in growth under nitrative stress conditions ( Fig 5D ) . This indicates that increase in nitrative stress changes A30P to a toxic protein in yeast cells comparable to wild-type αSyn . αSyn toxicity is dependent on the expression levels [60 , 63] . Thus , it was tested whether the A30P expression level is equal in Δyhb1 mutant compared to YHB1 yeast . Immunoblotting analysis revealed that the A30P variant was expressed at similar levels in both yeast backgrounds 6 h after induction of gene expression ( Fig 5E and 5F ) , excluding that differences in toxicity are due to different A30P expression levels . These results suggest a specific suppressive function of the nitric oxide oxidoreductase Yhb1 on A30P-induced aggregate formation and growth inhibition in yeast . The removal of the four tyrosines of αSyn as possible cellular nitration sites ( 4 ( Y/F ) ) might affect αSyn toxicity in yeast when the intracellular nitrative stress level is increased using a Δyhb1 strain defective in stress protection . This was examined by comparing αSyn , A30P and their 4 ( Y/F ) derivatives which were expressed in yeast with wild-type YHB1 or Δyhb1 deletion background . Growth was analyzed by spotting analysis and in liquid medium ( Fig 6A and 6B ) . Expression of 4 ( Y/F ) αSyn with an intact YHB1 gene resulted in improved growth , whereas A30P toxicity was not affected ( Figs 6A , 6B , 4C and 4D ) . In the absence of the YHB1 gene , A30P delayed growth . However , 4 ( Y/F ) A30P grew similar to the GFP control . A30P-mediated toxicity was related to the formation of inclusions ( Fig 6C ) . These results corroborate that increased nitrative stress contributes to A30P toxicity by nitration of tyrosine residues . Nitration-deficient wild-type or A30P αSyn were less toxic and aggregated less , whereas an increase of intracellular nitrative stress resulted in growth retardation and increased aggregate formation of A30P variant only when tyrosine resides were present . Oxidative and nitrative stresses are implicated in the pathogenesis of PD [75 , 76] . These stresses emerge from the accumulation of reactive intermediates such as ROS and RNS . ROS and RNS production were visualized in yeast cells . αSyn and A30P expression was induced for 6 h and ROS and RNS specific dyes were applied to compare the production of the reactive species in YHB1 and Δyhb1 cells by fluorescence microscopy and flow cytometry . Dihydrorhodamine 123 ( DHR123 ) was used for ROS detection . The dye accumulates in cells , where it is oxidized by free radicals to the bright red fluorescent product rhodamine 123 ( Fig 7A–7C ) . Expression of A30P and its derivative A30P/4 ( Y/F ) did not significantly increase the number of cells accumulating ROS . In contrast , expression of wild-type αSyn as well as its 4 ( Y/F ) derivative strongly increased the number of cells that accumulate red fluorescence indicative for ROS ( Fig 7C ) . No difference in ROS accumulation was observed between the YHB1 wild-type and the Δyhb1 mutant strain . DAF-2 DA ( 4 , 5-Diaminofluorescein diacetate ) dye was used as a sensitive and highly specific fluorescent indicator for detection of NO ( Fig 7D–7F ) . Expression of both αSyn and A30P induced accumulation of reactive nitrogen species ( Fig 7F ) . Interestingly , deletion of YHB1 significantly increased the number of cells exhibiting RNS when A30P variant was expressed . This effect was dependent on tyrosine residues since RNS accumulation in cells expressing A30P/4 ( Y/F ) did not differ from empty vector control . The results show that toxic wild-type αSyn expression induces significantly more ROS accumulation in yeast cells than non-toxic A30P . Accumulation of ROS species was not dependent on the presence of tyrosine residues or YHB1 gene . In contrast , both αSyn as well as A30P induce the accumulation of RNS for nitrative stress in yeast cells . The levels of RNS in A30P but not wild-type αSyn expressing cells are dependent on the presence of tyrosine residues and YHB1 . The results suggest that Yhb1 attenuates the accumulation of RNS of A30P expressing cells . Overexpression of αSyn and A30P leads to increased levels of RNS and higher sensitivity to NO stress in Δyhb1 yeast . The Yhb1 protein is translocated into yeast mitochondria under hypoxic conditions where it detoxifies NO [77] . Mitochondria are a major source of free radicals in the cells . Yhb1 is consuming NO , which inhibits mitochondrial respiration and thus increases the level of ROS . αSyn toxicity results in mitochondrial dysfunction and generation of ROS [65] . Overexpression of αSyn in mammalian cells results in mitochondrial fragmentation and involves a direct interaction of αSyn with mitochondrial membranes [78] . We examined whether deletion of YHB1 influences the mitochondrial morphology in αSyn and A30P αSyn expressing yeast cells . αSyn expression in the wild-type and Δyhb1 background was induced for 6 h in galactose medium and the mitochondria were visualized with a mitochondrial specific dye ( MitoTracker Red ) . Cells expressing GFP were used as a control ( Fig 8A ) . We could not detect co-localization of αSyn with mitochondria , which suggests that the described mitochondrial fraction of the protein might be small [78] . The mitochondrial morphology was classified as tubular , partially fragmented or fully fragmented . In the control cells , the mitochondria revealed a ribbon-like tubular architecture , typical for healthy mitochondria . Cells expressing αSyn showed a dramatic increase in the percentage of cells with fully fragmented mitochondria ( Fig 8A and 8B ) . Cells with and without inclusions were considered separately for statistical evaluation . Cells with plasma-membrane localization of the GFP-signal , typical αSyn localization for early stages of expression or lower expression levels , revealed partially fragmented mitochondrial architecture . αSyn expressing cells with aggregates had fully fragmented mitochondria . In contrast to αSyn , A30P expressing cells with aggregates had mainly tubular mitochondria , similar to the control cells ( Fig 8A ) . Deletion of YHB1 increased the percentage of cells with fully fragmented mitochondria almost two-fold ( Fig 8B ) . Thus , the disrupted mitochondrial morphology in Δyhb1 correlates with the increased levels of RNS ( Fig 7D–7F ) and diminished growth behavior of A30P expressing cells ( Fig 5B and 5D ) . Complementation of the Δyhb1 phenotype in A30P expressing cells rescued the defect ( Fig 8C ) , as mitochondrial morphology was recovered by expression of YHB1 on a low-copy vector . The result suggests that Yhb1 protects against A30P-induced cytotoxicity by preventing the mitochondrial fragmentation . A BLAST search for human genes corresponding to yeast YHB1 revealed 49% similarities of the YHB1 globin domain to human neuroglobin ( NGB ) as a putative homolog . We analyzed whether the human counterpart of yeast YHB1 can affect αSyn aggregation . Neuroglobins are oxygen-binding proteins that are highly conserved among vertebrates and are expressed in the central and peripheral nervous system . They provide protection against hypoxic induced cell injury in the brain , which is associated with ROS and RNS accumulation [79] . Both Yhb1 and neuroglobin contain a globin domain and are members of the globin gene family . NGB was shown to diminish beta-amyloid-induced neurotoxicity in vitro and to attenuate the phenotypes in a transgenic mouse model of Alzheimer’s disease [80] and to act as an oxidative stress-responsive sensor for neuroprotection [81] . Here we examined , whether human NGB affects αSyn or A30P growth and aggregate formation in yeast . Growth and aggregation of αSyn was not changed by the expression of the human NGB ( Fig 9A and 9B ) . However , NGB expression in Δyhb1 deletion strain rescued A30P yeast growth ( Fig 9C ) and reduced the number of cells with A30P aggregates ( Fig 9D ) . The effect of NGB in yeast is similar to the impact of YHB1 on αSyn and A30P growth and aggregate formation ( Fig 5C ) . Next , we examined whether NGB has not only a protective role against αSyn aggregate formation in yeast but also in mammalian cells . Human Neuroglioma cells ( H4 ) served as established αSyn aggregation model , where aggregation of αSyn is induced by co-expressing C-terminally modified αSyn ( SynT ) and synphilin-1 , αSyn-interacting protein that was also found in LBs [61 , 82] . H4 cells were co-transfected with SynT , synphilin-1 and NGB or empty vector and aggregate formation of SynT was monitored ( Fig 9E and 9F ) . Expression of NGB reduced the number of cells with aggregates almost two-fold in comparison to the control and reduced the number of aggregates per cell . Lactate dehydrogenase ( LDH ) measurements were performed to determine , whether there is an effect of NGB on cell toxicity . LDH release into the cell culture medium is an indicator of damages of the plasma membrane and is used as cytotoxicity marker . LDH measurements were similar for all tested H4 cells and support that NGB does act as suppressor of αSyn aggregation without significantly causing cytotoxicity . We assessed whether the different cytotoxicity of αSyn and A30P in yeast correlates with different nitration levels of the two variants in wild-type yeast background and under increased nitrative stress in Δyhb1 strain . Immunoblotting analysis performed with two specific antibodies against nitro-tyrosine ( 3-nitrotyrosine and nitro-Y39 αSyn ) showed that αSyn and A30P are nitrated in the YHB1 as well as in Δyhb1 yeast ( Fig 10A ) . Quantification of band intensities of both nitrated αSyn variants revealed significantly higher nitration level of αSyn in comparison to A30P . Deletion of YHB1 resulted in increase of A30P nitration level , whereas αSyn nitration level was not affected ( Fig 10B ) . The increased nitration level does not correlate with an increased dimerization level of both αSyn variants . The dimerization was not influenced by nitrative stress enhancement ( Fig 10C and 10D ) . The results suggest that nitration of αSyn contributes to the cytotoxicity of the protein , whereas dimer formation is in reverse correlation to toxicity . Phosphorylation of Y125 is required in vitro as a priming event for efficient phosphorylation of S129 by casein kinase CK1 [83] . Phosphorylation of S129 is the major PTM of αSyn , found in 90% of the aggregated protein in neuronal inclusions of PD patients [37] . αSyn and A30P are phosphorylated at S129 in yeast by endogenous kinases and phosphorylation has a protective role against αSyn-induced toxicity and aggregate formation [39 , 41] . Given the importance of these PTM and the close proximity of serine and tyrosine residues at the C-terminus , we assessed whether there is a cross-talk between modifications of tyrosine residues and phosphorylation of αSyn at S129 in vivo . Immunoblotting with an antibody that specifically recognizes αSyn phosphorylated at Y133 showed that both αSyn and A30P are phosphorylated at these residues , in accordance with our results from MS analysis ( Fig 11A ) . Quantification of Y133 phosphorylation revealed similar phosphorylation level of αSyn and A30P variant both in presence and absence of Yhb1 ( Fig 11B ) . We analyzed whether there is a difference between S129 phosphorylation level of αSyn and A30P . S129 phosphorylation level of αSyn was much higher than that of A30P ( Fig 11A and 11B ) . Tyrosine to phenylalanine ( Y/F ) substitutions were analyzed for their effects on the phosphorylation level at S129 . Y/F mutation of the N-terminal tyrosine 39 as well as of the C-terminal Y125 and Y136 did not affect the phosphorylation status of S129 . In contrast , mutation of Y133 had a drastic impact and resulted in complete loss of phosphorylation at S129 ( Fig 11C ) . Yeast growth was compared in spotting assay as well as in liquid culture between yeast cells , expressing Y/F single mutants and S129A phosphorylation deficient mutant ( Fig 11D and 11F ) . Yeast growth was measured after 20 h induction of protein expression . Expression of Y133F resulted in significant growth inhibition in comparison with αSyn and other tyrosine mutants . S129A showed slight growth inhibition ( Fig 11D and 11F ) and significant increase in the number of cells with aggregates ( Fig 11E ) . In addition to growth analysis , membrane integrity of the Y/F single mutants and S129A phosphorylation deficient mutant was examined to assess the cell viability of the mutants ( Fig 11G and S2 Fig ) . Propidium iodide ( PI ) staining was used after 20 h of protein induction as a sensitive method to determine the fraction of cells with compromised membrane integrity . Expression of Y133F and S129A significantly diminished membrane integrity , corroborating that expression of these mutants results in increased cytotoxicity . Flow cytometry measurements were performed to determine the accumulation of ROS and RNS in yeast cells , expressing the single mutants . DHR123 was used for detection of ROS ( Fig 12A ) and DAF-2 DA was used for detection of RNS ( Fig 12B ) . Expression of all mutants revealed a significant increase in the levels of ROS and RNS in comparison with the control; however , no significant differences were observed between the single mutants revealing that the enhanced toxicity of Y133F and S129A mutant is not due to higher accumulation of ROS or RNS . These results indicate that Y133 is required for the protective effect of αSyn S129 phosphorylation in vivo . Expression of Y133F was more toxic than expression of S129A phosphorylation deficient mutant , suggesting additional protective contribution of Y133 modifications against αSyn cytotoxicity . The data support a complex cross-talk between nitration and phosphorylation of the C-terminal tyrosine residues and S129 phosphorylation of αSyn and A30P . GAL1 promoter shut-off experiments were performed to study the role of αSyn PTMs on autophagy/vacuole and proteasome-mediated aggregate clearance of αSyn . The impact of blocking these systems by drug treatments was examined . Expression of αSyn was induced for 4 h in galactose-containing medium and the cells were then shifted to glucose-containing medium in order to repress the promoter . Cells were imaged 4 h after promoter shut-off and the percentage of cells with inclusions was determined . Shut-off studies were performed with wild-type αSyn and the mutants 4 ( Y/F ) , S129A and Y133F . PMSF was used as an inhibitor of autophagy/vacuole to study the contribution of this pathway for aggregate clearance [63] . PMSF impairs the activity of many vacuolar serine proteases without affecting proteasome function [84 , 85] . Inhibition of autophagy resulted in inefficient aggregate clearance of αSyn , as shown previously [41 , 63] . Mutations of the codons for the four tyrosines as well as the S129 and Y133 single exchanges resulted in similar aggregate clearance by inhibition of the autophagic proteases as in the control cells without drug ( ethanol ) ( Fig 13A ) . This suggests that autophagy is less involved in aggregate clearance of these mutants and shows that autophagy-mediated aggregate clearance requires modifications of the tyrosines and S129 . The contribution of the proteasome on 4 ( Y/F ) , S129A and Y133F αSyn aggregate clearance was analyzed by applying the proteasome inhibitor MG132 [86] . In contrast to autophagy impairment , cells expressing 4 ( Y/F ) and S129A αSyn cleared inclusions equally as the wild-type αSyn ( Fig 13B ) when the proteasome system was impaired . These results corroborate our previous findings showing a minor contribution of the proteasome to αSyn aggregate clearance [63] . However , cells expressing the Y133F mutant were unable to clear inclusions in a same manner as the wild-type , 4 ( Y/F ) and S129A αSyn . This indicates that αSyn , which is not modified at Y133 , is degraded by the proteasomal pathway . The results suggest that PTMs of tyrosine residues and S129 promote the autophagy-mediated aggregate clearance , whereas non-modified Y133 residue is a key determinant for the targeting of the protein to the proteasome . Inhibition of autophagy of A30P expressing cells revealed diminished clearance of aggregates of A30P as well as the A30P/4 ( Y/F ) mutant indicating that degradation of the A30P/4 ( Y/F ) aggregates depends on the autophagy/vacuole system similarly to wild-type αSyn and A30P ( Fig 13C ) . A30P/S129A and A30P/Y133F mutants were able to degrade aggregates efficiently upon autophagy inhibition , similar to S129A and Y133F . Proteasome impairment resulted in inefficient clearance of the A30P/Y133F mutant ( Fig 13D ) . However , this impact was not as strong as in the αSyn Y133F mutant , confirming that the wild-type αSyn is strongly dependent on Y133 modification as a determinant for aggregate clearance .
Phosphorylation at serine residue S129 represents the major protective phosphorylation site of αSyn which is conserved from man to the baker’s yeast as a eukaryotic Morbus Parkinson cell model . The effect of nitrative modifications of αSyn and their contribution towards αSyn-induced cytotoxicity was investigated . A complex interplay was discovered between modifications of the C-terminal tyrosine residues and S129 phosphorylation ( Fig 14 ) . These tyrosine residues of αSyn can be phosphorylated or nitrated with drastic consequences for cellular growth . There is a strong preference of the C-terminus of αSyn for nitration or di-tyrosine formation . Nitration interferes with protective phosphorylation of S129 , whereas di-tyrosine formation protects yeast cells . The yeast nitric oxidoreductase Yhb1 as well as its related human protein neuroglobin play protective roles against αSyn aggregation . Yhb1 decreases the nitration level of the A30P variant of αSyn by reducing the accumulation of reactive nitrogen species , resulting that yeast cells can tolerate increased levels of this αSyn variant without significant growth inhibition . In yeast , expression of αSyn triggers accumulation of chemically reactive molecules such as ROS and RNS that damage the cell by causing oxidative and nitrative stress which in turn contributes to cell death [66] . Nitration reduces the binding affinity of αSyn to lipid vesicles and therefore disrupts αSyn-membrane interaction [87] . Numerous studies show that oxidative injury of αSyn , specifically nitration of tyrosine residues , contributes directly to the pathology of PD . Nitrative stress was proposed to induce αSyn aggregation as well as αSyn-induced pathology [34 , 47 , 50 , 88–90] . However , also opposing influence of nitrated αSyn has been shown [51 , 52] . Thus , the effect of nitrative αSyn modifications and their influence on the toxicity and aggregation of αSyn is still controversial . In this study , we show that PTMs on αSyn tyrosine residues have a dual impact on αSyn mediated growth inhibition of yeast cells . Previous studies suggested that nitration of αSyn might be responsible for the formation of the proteinaceous inclusions observed in PD brains and for the neuronal loss in the substantia nigra [34 , 88] . Here , we showed that nitration increases the growth defect induced by αSyn in yeast cells . Tyrosine nitration increases aggregation , mitochondrial fragmentation and growth inhibition . We demonstrated a correlation between the growth impairment , mediated by αSyn and A30P and their nitration level . αSyn , which inhibits yeast growth and has a high aggregation level , is strongly nitrated . In contrast , A30P , which is not toxic to yeast and aggregates to a lesser extent , is weakly nitrated . Notably , we showed that C-terminal tyrosine 133 is required for the protective phosphorylation of αSyn at S129 . αSyn C-terminal tyrosines Y125 , Y133 and Y136 are in close proximity to S129 , which raises the question whether there is an interplay between different PTMs at these residues . S129-phosphorylated αSyn is abundantly found in Lewy bodies [35 , 37] . This phosphorylation site is conserved in yeast and can be used by several endogenous kinases [91] . The effects of S129 phosphorylation are complex and the role of this modification on αSyn-induced toxicity and aggregation is still controversial [38] . In yeast , S129 phosphorylation has a protective role against growth impairment and aggregate formation [39–41] . Phosphorylation of the tyrosine residues of αSyn is less explored and the effects of this modification are still unclear , varying from protective to no impact on neurotoxicity and oligomerization [83 , 92–97] . We show for the first time that tyrosine 133 is strictly required for phosphorylation at S129 in yeast . Tyrosine 133 can be nitrated or phosphorylated , as demonstrated by immunoblotting and MS analysis . These two PTMs might have opposing roles on the cellular toxicity of the protein . Phosphorylation might prevent tyrosine residues from nitration and vice versa . Here , we used tyrosine to phenylalanine exchanges that abolishes both phosphorylation as well as nitration . There are no natural amino acids that mimic the phosphorylation or nitration state of the tyrosine residue , thus restricting the investigation of the contribution of a single posttranslational modification at one and the same tyrosine residue in vivo . Our results reveal Y133 as major tyrosine phosphorylation site in yeast , and we observe only insignificant phosphorylation at Y125 . Our data reveal a correlation between tyrosine nitration and the cellular S129 phosphorylation level . The wild-type αSyn has significantly higher nitration levels as well as increased protein populations with S129 phosphorylation but similar levels of Y133 phosphorylation when compared to its A30P variant . These results suggest that rather nitration than phosphorylation at Y133 promotes S129 phosphorylation in yeast . Alternatively , nitration at Y133 might change the protein conformation and make S129 more accessible for protein kinases . The protective effect of Y133 was not accompanied with changes in the potential to form aggregates . The discrepancy between the clear protective effect of Y133 on yeast growth without significant effect on inclusion formation suggests that additional yet elusive protective mechanisms exist in the cell , which do not depend on aggregate formation but support cellular survival . This suggests an even more complicated interplay between different α-synuclein modifications . Recently , the role of site-specific nitration of αSyn was investigated using site-specifically nitrated synthetic proteins at Y39 and Y125 [98] . The authors assessed the influence of nitration at Y125 and Y39 on PLK3-mediated in vitro phosphorylation at S129 and showed that tyrosine nitration does not prevent recognition of the protein by PLK3 and the subsequent phosphorylation at S129 . These results strengthen the link between the C-terminal tyrosine and serine modifications , revealing a complex cross-talk between PTMs with different contributions to the cytotoxicity of αSyn . Previously , we have shown that autophagy is the major pathway for aggregate clearance in yeast [63] . The phosphorylation state of αSyn influences the clearance mechanism of the protein . Blocking of S129 phosphorylation in yeast leads to impaired aggregate clearance by autophagy [39] . Increased levels of S129 phosphorylation can suppress the defect of impaired αSyn sumoylation by rescuing the autophagic aggregate clearance and promoting the proteasomal clearance of αSyn [41] . Here , we show that posttranslational modifications of the four tyrosine residues , similar to S129 phosphorylation , promote the autophagic clearance of αSyn aggregates . Inhibition of autophagy rendered yeast cells unable to clear αSyn aggregates , however had no effect on the clearance of S129A , 4 ( Y/F ) and Y133F mutants . Interestingly , Y133F mutant could not be degraded upon inhibition of the proteasome . Similarly , proteasome impairment resulted in inefficient clearance of the A30P/Y133F mutant , however the impact was not as strong as in the αSyn Y133F mutant . Therefore , Y133 represents a key determinant for the degradation fate of αSyn . Phospho- and nitro-modifications at Y133 promote the autophagic clearance of the aggregates , whereas the non-modified Y133 protein is directed to the proteasome . Beside nitration , the reaction between tyrosine and RNS can result in the formation of di-tyrosine bonds , leading to the formation of stable αSyn oligomers , including dimers and higher oligomeric structures . At low peroxynitrite level , di-tyrosine formation outcompetes the reaction of tyrosine nitration [54 , 55] indicating a forced reaction to tyrosine nitration under nitrative stress . We observed formation of dimers and oligomers in vivo without exposure of yeast cells to nitrating agents; therefore , they represent the consequence of endogenous nitrative stress . Previous studies already demonstrated nitration-induced oligomerization of αSyn [42 , 53] . There , treatment with nitrating agent resulted in the formation of αSyn dimers and oligomers crosslinked by di-tyrosine bonds . Using MS , we characterized the tyrosine residues involved in covalent dimer formation and nitration and identified to what extend the different tyrosine residues are involved in the di-tyrosine formation and what are the precise positions of the respective tyrosines . To the best of our knowledge , this is the first characterization of αSyn dimer species , formed in vivo and without additional exposure to nitrative agents . Our data reveal strong preference of the C-terminal tyrosine residues for dimer formation with predominant forms including Y136 interacting either with Y125 ( Y125-Y136 ) or with Y133 ( Y133-136 ) . Y39 was hardly involved in dimer formation under physiological conditions in yeast or in vitro after PON treatment . Recently , in vitro studies on αSyn oligomerization and di-tyrosine formation upon treatment with tetranitromethane ( TMN ) demonstrate a predominant formation of di-tyrosine dimers when Y39 is not available for nitration ( Y39F ) and suggest that the N-terminal region of αSyn plays a role in TMN-induced di-tyrosine formation of higher order oligomers [98] . There are studies which described an increase of nitration at Y39 of αSyn in an oxidative cellular model of PD [99] , whereas we observe resistance of Y39 to 3-NT modification in comparison to the C-terminal tyrosine residues which are nitrated in vitro as well as in vivo . These results are corroborated by previous findings , where treatment of purified αSyn with PON did not result in nitration of Y39; however , all C-terminal residues were nitrated [100] . Higher nitration levels of αSyn compared to A30P were also detected by mass spectrometry data confirming the results from western blot analyses . There , only one tyrosine residue is nitrated in A30P , whereas three αSyn residues are nitrated . Our data reveal that αSyn dimers originating from di-tyrosine crosslinking are non-toxic species in contrast to nitrated αSyn . Covalent binding of the di-tyrosines stabilizes the dimeric structures and consequently removes αSyn molecules from aggregation to toxic aggregates . αSyn protein lacking all four tyrosine residues forms less aggregates , however , the aggregate formation is not prevented . This indicates that tyrosine residues are not crucial for in vivo assembly of the protein to aggregates and suggests an independent pathogenic mechanism of αSyn aggregation . Similarly , nitration of αSyn was shown to promote formation of stable off-pathway oligomeric species that inhibit αSyn fibrillation [51 , 52] . Thus , the formation of stable oligomers under oxidative stress conditions redirects the monomers to oligomers that do not contribute to fibril formation . Expression of A30P in yeast has different toxicity properties from wild-type αSyn [60 , 63] . A30P is located in the cytoplasm , whereas αSyn is delivered to the plasma membrane . Overexpression from a high-copy number plasmid results in formation of A30P fluorescent foci , however , the aggregation is transient and yeast cell growth is not affected . Using the Δyhb1 mutant lacking the flavohemoglobin Yhb1 , we could demonstrate that nitration plays also a role for A30P and confirmed that nitration increases aggregation and growth inhibition . Deletion of the yhb1 yeast gene results in a deficient cellular detoxification machinery towards NO and makes A30P as toxic as wild-type αSyn . This shows that the A30P nitration level is a crucial factor for gaining toxicity . Consequently , elimination of its putative NO-detoxifier Yhb1 results in stronger formation of toxic αSyn aggregates . Yhb1 reduced the level of nitrative stress in A30P expressing cells . Moreover , Yhb1 protected the A30P-expressing cells from mitochondrial fragmentation . αSyn-induced fragmentation of mitochondrial structure caused by direct interaction of αSyn with the mitochondrial membranes was already demonstrated [78] . It was proposed that ROS accumulation induced by αSyn expression is an indirect effect due to mitochondrial dysfunction [65] . We found a connection between increased nitrative stress and mitochondrial fragmentation . This suggests a mechanistic model based on the specific ability of αSyn and A30P to form aggregates and damage mitochondria induced by nitrative stress . Analysis of neuroglobin ( NGB ) , the human homologue of YHB1 , in human cell lines ( H4 cells ) confirmed that this protein modulates αSyn aggregation . Expression of neuroglobin in mammalian cells reduced the number inclusions per cell . Neuroglobin is expressed primarily in neurons and protects against hypoxic neuronal death and ischemic brain injury [101] . Furthermore , expression of neuroglobin protects against beta-amyloid-induced neurotoxicity in transgenic mice in vivo [80] . Recent reports revealed that overexpression of neuroglobin prevents tau hyperphosphorylation at multiple AD-related sites [102] . These data support our findings and imply NGB as a new therapeutic target in PD and other neurodegenerative diseases . A complex interplay between nitration and phosphorylation of αSyn C-terminal residues , deeply interconnected with nitrative stress , determines the aggregate clearance by autophagy or ubiquitin-dependent 26S proteasome pathways ( Fig 14 ) . The proposed model derived from yeast as unicellular eukaryotic cell should provide interesting hints and insights for the study of αSyn posttranslational communications as it happens in the human brain and its connection to oligomer or aggregate formation and clearance .
Plasmids ( Table 2 ) and Saccharomyces cerevisiae strains ( Table 3 ) used in this work are listed below . Human αSyn cDNA sequence and the corresponding A30P sequence were expressed from yeast high expression vector ( 2μ ) under the GAL1 promoter and CYC1 terminator as described previously [63] . YHB1 sequence was amplified on genomic DNA from Saccharomyces cerevisiae and NGB was amplified on cDNA sequence and cloned into pME2788 low copy vector ( CEN/ARS ) proceeded by GAL1 promoter and followed by CYC1 terminator , respectively . The 4 ( Y/F ) αSyn mutant constructs were generated by site-directed mutagenesis using QuickChange II Site-Directed Mutagenesis Kit ( Agilent Technologies ) . Plasmids pME3763 , pME3764 , pME4095 and pME4101 were used as templates to substitute the four tyrosines ( Y39 , Y125 , Y133 , Y136 ) by phenylalanine . The same plasmids were used as templates to substitute serine 129 by alanine . For growth and microscopy studies , αSyn variants were used that are C-terminally tagged with GFP via the KLID linker [63] . For Ni2+-NTA affinity chromatography , αSyn and A30P were C-terminally fused to His6-tag using pME3760 and pME3761 as templates . All constructs were verified by DNA sequencing . Saccharomyces cerevisiae strains , BY4741 and Δyhb1 , were grown in non-selective medium ( YEPD ) at 30°C and transformed by standard lithium acetate protocol [103] . For cultivation of the Δyhb1 strain , 200 μg/ml G418 were added to the medium . Transformants harboring αSyn constructs were selected on solid Synthetic Complete medium ( SC ) lacking uracil ( SC-Ura ) supplemented with 2% glucose for 2 days at 30°C . For growth of cells co-expressing αSyn with YHB1 and NGB , respectively , SC medium lacking uracil and histidine ( SC-Ura-His ) was used . Expression of αSyn was induced by shifting overnight cultures from 2% raffinose- to 2% galactose-containing medium ( A600 = 0 . 1 ) . To investigate growth on solid medium , cells were pre-grown in selective SC medium containing 2% raffinose . After normalizing the cells to equal densities ( A600 = 0 . 1 ) , 10-fold dilution series were prepared and spotted in a volume of 10 μl on SC-Ura or SC-Ura , -His agar plates supplemented with either 2% glucose or 2% galactose . The growth was documented after incubation for 3 days at 30°C . For growth test in liquid cultures , cell cultures were pre-grown as described above , adjusted to equal densities of A600 = 0 . 1 and shifted to galactose-containing SC-Ura medium . Optical density measurements of 200 μl cell cultures were performed in 96-well plates for 48 h using a microplate reader ( Infinite M200; TECAN Group Ltd ) . Growth analyses under nitrative stress conditions were performed using either 600 μM or 1 mM DETA-NONOate ( Cayman Chemical Company ) as NO donor in SC-Ura medium at pH 7 . 4 . Cells were pre-grown in selective SC medium containing raffinose and inoculated in galactose-containing SC medium to an A600 = 0 . 1 . αSyn expression was induced for 6 h and fluorescent images were obtained with 63x magnification using a Zeiss Observer . Z1 microscope ( Zeiss ) equipped with a CSU-X1 A1 confocal scanner unit ( YOKOGAWA ) , QuantEM:512SC digital camera ( Photometrics ) and SlideBook 6 . 0 software package ( Intelligent Imaging Innovations ) . Depending on the fluorescent agent , ssGFP or sdRFP filter were applied . To quantify aggregation , at least 300 cells were counted per strain and experiment and the number of cells displaying αSyn aggregation was referred to the total number of counted cells . To label mitochondria , the cells were incubated for 45 minutes ( min ) in the presence of 50 nM MitoTracker Red ( Molecular Probes , Invitrogen ) , washed once with fresh medium and imaged . To test yeast for production of ROS , cells pre-grown overnight in raffinose-containing SC medium were transferred to galactose-containing SC medium at A600 = 0 . 1 and incubated for 6 h at 30°C . After washing the cells , dihydrorhodamine 123 ( DHR123 ) Cayman Chemical Company ) was added to a final concentration of 5 μg/ml and the cells were incubated for 1 . 5 h at 30°C . After washing , the cells were re-suspended in H2O and microscopy was performed using RFP filter . To test yeast for RNS production , cells pre-grown overnight in raffinose-containing SC medium were transferred to galactose-containing SC medium at A600 = 0 . 1 . After 6 h induction cells were washed and diluted in PBS buffer , pH 7 . 5 to A600 = 0 . 1 . DAF-2 DA ( Genaxxon bioscience GmbH ) was added to a final concentration of 25 μg/ml and cells were incubated for 1 h at 30°C in the dark . Before microscopy , the cells were washed and RNS was visualized using GFP filter . For testing yeast cells for production of ROS and RNS , flow cytometry was performed . Cells were grown as above . Before measuring , cells were re-suspended in 50 mM trisodium citrate buffer , pH 7 . 0 . Flow cytometry analysis was performed on a BD FACSCANTO II ( Becton Dickinson ) . 100 000 events were counted for each experiment . Data analysis was performed using the BD FACSDIVA software ( Becton Dickinson ) . Representative examples that are shown in the Figures were repeated at least 3 times . Yeast cell membrane integrity was analyzed with PI staining . Yeast cells were incubated with 12 . 5 μg/ml PI for 30 min . As a positive control , cells were boiled for 10 min at 95°C . Yeast cells carrying αSyn were pre-grown in selective SC medium containing 2% raffinose overnight and shifted to 2% galactose-containing selective SC medium to induce the αSyn expression for 4 h . Afterwards , cells were shifted to SC medium supplemented with 2% glucose to shut-off the promoter . 4 h after promoter shut-off , cells were visualized by fluorescence microscopy . The reduction of number of cells displaying αSyn inclusions was recorded and plotted on a graph . To study the lysosome/vacuole degradation pathway ( autophagy ) phenylmethanesulfonyl fluoride ( PMSF ) in ethanol ( EtOH ) was applied to the suspension in a final concentration of 1 mM [105] . For impairment of the proteasomal degradation system Carbobenzoxyl-leucinylleucinyl-leucinal ( MG132 ) dissolved in dimethyl sulfoxide ( DMSO ) was added to the cell suspension in a final concentration of 75 μM and in parallel , equal volume of DMSO was applied to the cells as a control . For drug treatment with MG132 induction-medium containing galactose and shut-off-medium containing glucose was supplemented with 0 . 003% SDS and 0 . 1% proline [106] . Purification of His6-tagged recombinant αSyn and A30P was performed using yeast cells carrying the 2μ high-copy vectors pME4095 and pME4101 , pME4353 , pME4354 and pME2795 . Cells cultured overnight in 200 ml selective SC medium supplemented with 2% glucose were collected by centrifugation , washed and inoculated in 1 . 5 liter selective medium containing 2% galactose for 12 h at 30°C . Cells were lysed by 50 ml 1 . 85 M NaOH containing 7 . 5% ß-mercaptoethanol on ice for 10 min . For precipitation , the protein crude extracts were incubated for 30 min in 50 ml 50% trichloroacetic acid ( TCA ) on ice , washed afterwards with acetone and dissolved in 50 ml buffer A ( 6 M Guanidine hydrochloride , 100 mM NaH2PO4 , 10 mM Tris-HCl , pH 8 . 0 ) . The mixture was shaken for 1 h at 25°C , the collected supernatant was calibrated to pH 7 . 0 with 1 M Tris base ( pH 8 . 5 ) and supplemented with imidazole to final concentration of 20 mM . Before the protein crude extract was applied to a His GraviTrap column ( GE Healthcare Life Science ) , the columns were washed with 10 ml buffer A containing 20 mM imidazole and equilibrated with 5 ml buffer B ( 8 M Urea , 100 mM Na3PO4 , 10 mM Tris , pH 6 . 3 ) . Elution of the protein was carried out using 4 times 1 ml of 200 mM imidazole resolved in buffer B . The protein content of the elution fractions were determined by Bradford protein concentration assay and subjected to western blot analysis . In vitro nitration of αSyn was performed using the highly reactive nitrating agent peroxynitrite ( PON ) ( Cayman Chemical Company ) in the presence of 0 . 3 M HCl to adjust the pH value . 20 μl of the protein were mixed with 1 μl of both reagents for approximately 10 seconds . Yeast cells harboring different αSyn constructs were grown in selective SC medium containing 2% raffinose overnight and transferred to 2% galactose-containing medium for induction of αSyn expression . After expression of αSyn for 6 h , protein crude extracts were prepared and protein concentration was determined via Bradford protein concentration assay . For electrophoretic separations of the protein , 10 μg protein extract was applied to a 12% SDS-polyacrylamide-gel and transferred afterwards onto a nitrocellulose or polyvinylidenflouride ( PVDF ) membrane . The membrane was incubated with primary antibody diluted in TBST buffer with 5% milk powder overnight . As primary antibodies , rabbit anti α/β/γSyn polyclonal antibody ( 1:2000 , Santa Cruz Biotechnology ) , mouse anti 3-nitrotyrosine monoclonal ( 1:1400 , Abcam ) , mouse anti nitro-α/ßSyn ( Tyr39 ) monoclonal antibody ( 1:2000 , MERCK ) , mouse anti di-tyrosine monoclonal antibody ( 1:1000 , JaICA ) , mouse anti phospho Ser-129 αSyn antibody ( 1:2500 , Wako Chemicals USA ) , rabbit anti phospho Tyr-133 αSyn polyclonal antibody ( 1:1000 , Abcam ) and mouse anti GAPDH monoclonal antibody ( 1:5000 , Thermo Fisher Scientific ) were used . Peroxidase-coupled goat anti mouse ( 1:2000 , Jackson ImmunoResearch Laboratories ) or goat anti rabbit ( 1:5000 , Mobitec ) immunoglobulins G was applied as secondary antibody . Pixel density values for Western Blot quantification were obtained from TIFF files generated from digitized x-ray films ( Kodak ) and analyzed with the ImageJ software ( NIH , Bethesda ) . Sample density values were normalized to the corresponding loading control . For quantification of the signals , at least three independent experiments were performed . The significance of differences was calculated using Student’s t test or one-way ANOVA test . p value < 0 . 05 was considered to indicate a significant difference . Protein samples were separated by 12% SDS-PAGE . Excised polyacrylamide gel slices of Coomassie stained proteins were digested with the proteases trypsin ( Promega ) and AspN ( Sigma-Aldrich ) according to the protocol of Shevchenko [107] and supplier’s instructions . After digestion and peptide elution the samples were resolved in 20 μl 2 . 8% acetonitrile containing 0 . 1% formic acid . The single digestions as well as the double digested AspN/tryptic peptides were then analyzed by LC-MS . Peptides of 1–5 μl sample solution were trapped and washed with 0 . 07% trifluoroacetic acid 2 . 6% acetonitrile on an Acclaim PepMap 100 column ( 100 μm x 2 cm , C18 , 3 μm , 100 Å , P/N164535 Thermo Scientific ) at a flow rate of 25 μl/min for 5 min . Analytical peptide separation by reverse phase chromatography was performed on an Acclaim PepMap RSLC column ( 75 μm x 25 cm , C18 , 3 μm , 100 Å , P/N164534 Thermo Scientific ) running a 40 min gradient from 100% solvent A ( 0 . 1% formic acid ) to 65% solvent B ( 80% acetonitrile , 0 . 1% formic acid ) and further to 95% solvent B within 1 min at flow rates of 300 nl/min ( Fisher Chemicals ) . Chromatographically eluting peptides were on-line ionized by nano-electrospray ( nESI ) using the Nanospray Flex Ion Source ( Thermo Scientific ) at 2 . 4 kV and continuously transferred into the mass spectrometer . Full scans within m/z of 300–1850 were recorded with the Orbitrap-FT analyzer at a resolution of 30 . 000 with parallel data-dependent top 10 MS2-fragmentation in the LTQ Veleo Pro linear ion trap . LC-MS method programming and data acquisition was performed with the software Xcalibur 2 . 2 ( Thermo Fisher ) . MS/MS2 data processing for protein analysis and PTM identification was done with the Proteome Discoverer 1 . 4 ( PD , Thermo Scientific ) software using the SequestHT search engine ( Thermo Scientific ) and Saccharomyces cerevisiae protein database extended by the most common contaminants with the following criteria: peptide mass tolerance 10 ppm , MS/MS ion mass tolerance 0 . 8 Da , and up to two missed cleavages allowed . Only high confident peptides with a false discovery rate less than 0 . 01 were considered . The MS data of crosslinked peptides were analyzed with StavroX2 . 3 . 4 . 5 [73] . MS data in the Mascot generic file ( mgf ) format containing all MS/MS data of precursor ions were loaded into the program . The following parameters were used for the StavroX analysis: ( i ) cleavage sites: C-terminal: K , R; N-terminal: D; ( ii ) number of missed cleavages = 2; ( iii ) variable modifications: oxidation of methionine; nitration of tyrosine; cysteine-to-cysteine acetamid; ( iv ) mass of crosslinker: -H2; ( v ) crosslinks only between two tyrosines; ( vi ) precision precursor comparison = 10 ppm . The false-positive rate was evaluated by decoy analysis using the reversed protein sequence . The frequency of occurrence of candidates from the data analysis and decoy analysis was compared for each sample . Only scores with decoy frequencies below 8% of the data frequency were considered as possible crosslinks . The data were filtered for unique scans and each scan was considered only once with its highest score . Since multiple tyrosine residues are located on one and the same peptide , different combinations of crosslinked peptides with equal masses were possible . For each scan the crosslinked tyrosine dimers were assigned according to the score calculated by the program based on the fragment ions series . Neuroglioma ( H4 ) cells were plated 24 h prior to transfection , in 12-well plates ( Costar ) . Cells were transfected with FuGENE6 Transfection Reagent ( Promega ) using equal amounts of plasmid DNA encoding for αSyn , synphilin-1 [108] and Neuroglobin-mCherry , or the empty mammalian expression vector pcDNA 3 . 1 , according to the manufacturer’s instructions . 48 h after transfection , cells were washed with PBS and fixed with 4% paraformaldehyde for 10 min at room temperature ( RT ) . After washing with PBS , cells were permeabilized with 0 . 5% Triton X-100/PBS ( Sigma-Aldrich ) for 20 min at RT , and blocked in 1 . 5% normal goat serum ( PAA ) /PBS for 1 h . Cells were incubated with a mouse anti αSyn antibody ( 1:1000 , BD Transduction Laboratory ) overnight and then with a secondary antibody ( Alexa Fluor 488 donkey anti-mouse IgG ) for 2 h at RT . Finally , cells were stained with Hoechst 33258 ( 1:5000 in PBS , Life Technologies- Invitrogen ) for 5 min and maintained in PBS prior to epifluorescence microscopy . Transfected cells were scored based on the αSyn inclusions pattern and classified into: cells without inclusions , less than ten inclusions ( <10 inclusions ) , and more than ten inclusions ( ≥10 inclusions ) , as described [61] . The total number of transfected cells was expressed in percentage , as the average from three independent experiments . The lactate dehydrogenase ( LDH ) cytotoxicity assay ( Roche Diagnostics ) was performed according to the manufacturer’s instructions . Growth media from cells were applied in triplicates in a 96-well plate , in a ratio 1:1 with the reaction mixture . The measurements were performed in a TECAN Infinite 200 Pro plate reader at 490 nm . The percentage of toxicity was calculated as indicated by the manufacturer . Data were analyzed using GraphPad Prism 5 ( San Diego , California , USA ) Software and were presented as mean ± SEM of at least three independent experiments . The significance of differences was calculated using Students t-test , one-way ANOVA test with Bonferroni’s multiple comparison test or Dunnett’s multiple comparison test . P value < 0 . 05 was considered to indicate a significant difference .
|
Parkinson’s disease is characterized by loss of dopaminergic neurons in midbrain and the presence of αSyn protein inclusions . Human αSyn mimics the disease pathology in yeast resulting in cytotoxicity and aggregate formation . αSyn is abundantly phosphorylated at serine S129 and possesses four tyrosines ( Y39 , Y125 , Y133 , and Y136 ) that can be posttranslationally modified by nitration or phosphorylation . The consequence of each of these possible modifications is still unclear . Nitration as consequence of oxidative stress is a hallmark for neurodegenerative diseases . Here , we addressed the molecular mechanism , how tyrosine posttranslational modifications affect αSyn cytotoxicity . Tyrosine nitration can contribute to αSyn toxicity or can be part of a cellular salvage pathway when di-tyrosine-crosslinked dimers are formed . The Y133 residue , which can be either phosphorylated or nitrated , determines whether S129 is protectively phosphorylated and αSyn inclusions are cleared . This interplay with S129 phosphorylation demonstrates a dual role for C-terminal tyrosine residues . Yeast flavohemoglobin Yhb1 and its human counterpart neuroglobin NGB protect cells against cytotoxicity and aggregate formation . These novel insights into the molecular pathways responsible for αSyn cytotoxicity indicate NGB as a potential target for therapeutic intervention in PD .
|
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2016
|
C-Terminal Tyrosine Residue Modifications Modulate the Protective Phosphorylation of Serine 129 of α-Synuclein in a Yeast Model of Parkinson's Disease
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Aurora B kinase ( AURKB ) is the catalytic subunit of the chromosomal passenger complex ( CPC ) , an essential regulator of chromosome segregation . In mitosis , the CPC is required to regulate kinetochore microtubule ( K-MT ) attachments , the spindle assembly checkpoint , and cytokinesis . Germ cells express an AURKB homolog , AURKC , which can also function in the CPC . Separation of AURKB and AURKC function during meiosis in oocytes by conventional approaches has not been successful . Therefore , the meiotic function of AURKC is still not fully understood . Here , we describe an ATP-binding-pocket-AURKC mutant , that when expressed in mouse oocytes specifically perturbs AURKC-CPC and not AURKB-CPC function . Using this mutant we show for the first time that AURKC has functions that do not overlap with AURKB . These functions include regulating localized CPC activity and regulating chromosome alignment and K-MT attachments at metaphase of meiosis I ( Met I ) . We find that AURKC-CPC is not the sole CPC complex that regulates the spindle assembly checkpoint in meiosis , and as a result most AURKC-perturbed oocytes arrest at Met I . A small subset of oocytes do proceed through cytokinesis normally , suggesting that AURKC-CPC is not the sole CPC complex during telophase I . But , the resulting eggs are aneuploid , indicating that AURKC is a critical regulator of meiotic chromosome segregation in female gametes . Taken together , these data suggest that mammalian oocytes contain AURKC to efficiently execute meiosis I and ensure high-quality eggs necessary for sexual reproduction .
Haploid gametes are generated by meiosis , a unique cell division process that consists of a single round of DNA replication followed by two successive cell divisions . In the first division , meiosis I ( MI ) , homologous chromosomes segregate . The second division , meiosis II ( MII ) , is more similar to mitosis because sister chromatids segregate . An error in chromosome segregation can result in aneuploidy , the leading genetic cause of infertility and congenital birth defects in humans [1] , [2] . It is now well appreciated that the incidence of aneuploidy is at least 10-fold higher in female gametes ( oocytes ) than it is in male gametes ( sperm ) [3] . Thus , understanding the underlying causes of oocyte aneuploidy could help address a majority of clinical aneuploidies in humans . During meiosis there are a number of possible mistakes that could result in aneuploidy . These mistakes include , but are not limited to , defects in kinetochore-microtubule ( K-MT ) attachments , a faulty spindle assembly checkpoint ( SAC ) , improper cytokinesis , or loss of sister chromatid cohesion [4]–[10] . In mitosis , the chromosomal passenger complex ( CPC ) is essential for steering the chromosomes through these obstacles [11]–[16] . The CPC does this through a sophisticated pattern of synchronized movements . At metaphase , the CPC localizes to kinetochores , and at anaphase , it relocates to the spindle midzone . This dynamic localization pattern ensures that the CPC phosphorylates the right substrates at the right time and place . Perturbing the CPC in oocytes often leads to errors in MI , thereby resulting in aneuploidy [6] , [17] . In mitotically dividing cells , the CPC consists of a catalytic subunit , Aurora B kinase ( AURKB ) , and regulatory subunits Inner Centromere Protein ( INCENP ) , Survivin , and Borealin [18]–[20] . Meiotic cells , however , contain another enzymatic subunit , Aurora C kinase ( AURKC ) , that can function in the CPC in place of AURKB [6] , [21]–[25] . AURKB and AURKC are members of a conserved serine-threonine protein kinase family , and are highly similar in sequence within their catalytic domains . Both AURKs bind the IN box region of INCENP , but not at the same time [22] . This binding is essential to stimulate kinase activity and for subsequent phosphorylation of INCENP [26] . Because they are highly similar in sequence , AURKC can compensate for loss of AURKB when ectopically expressed in somatic cells and supports mitosis in preimplantation mouse embryos that lack AURKB [27]–[29] . Furthermore , AURKB compensates for the loss of AURKC in oocytes from Aurkc−/− mice [23] . The sequence similarities between AURKB and AURKC have hindered our understanding of their functions during meiosis . For example , small molecule inhibitors do not selectively inhibit the kinases [17] , [22] , [24] , [25] , [30] , siRNA knockdown approaches are inefficient and lack specificity [17] , [22] , and , as mentioned , genetic knockout strategies allow for functional compensation [23] , [29] . We hypothesized that expression of a dominant negative allele of AURKC ( AURKC-DN ) perturbed both AURKB and AURKC functions in oocytes [6] . To further understand the molecular mechanisms that lead to the high incidence of aneuploidy in mammalian oocytes , we sought to develop a tool to selectively disrupt AURKC function . Mutation of the gatekeeper leucine residue in the ATP-binding pocket of AURKB inactivates the kinase in vivo and in vitro [31] . Here , we devised a similar strategy to inhibit AURKC activity and demonstrate that an AURKC-L93A gatekeeper mutant selectively disrupts AURKC , but not AURKB , function during oocyte meiosis . Using this strategy , we show that AURKC has non-overlapping functions with AURKB during MI . We find that loss of AURKC function results in misalignment of chromosomes and arrest at metaphase I ( Met I ) , and , in oocytes that failed to arrest , aneuploidy at metaphase II ( Met II ) . Oocytes expressing the AURKC mutant failed to correct erroneous K-MT attachments , which is the likely cause of the misaligned chromosomes and aneuploidy . We also find that AURKC-CPC is not uniquely required to maintain an active SAC or to execute cytokinesis . These events may be AURKB-CPC specific or require the activity of both AURKB and AURKC . This study is the first to ascribe non-overlapping functions of AURKC from AURKB during MI in mouse oocytes .
Expression of dominant negative AURKC ( AURKC-T171A , T175A ) ( referred hereafter as AURKC-DN ) in mouse oocytes causes cytokinesis failure and misaligned univalent chromosomes at MI ( [6] and Figure S1 ) . These phenotypes are identical to that of oocytes cultured in high concentrations of small molecule inhibitors of AURKB ( ZM447439 and AZD1152 ) which likely also inhibit AURKC [17] , [25] , [32] . Two models could explain the similarity in phenotype between the two perturbations: 1 ) Either AURKB is not expressed in mouse oocytes or 2 ) AURKC-DN disrupts both AURKB and AURKC function . To investigate the first model , we assessed the protein expression of AURKB in oocytes undergoing meiosis via immunocytochemistry with an antibody previously validated to detect AURKB in mouse preimplantation embryos [29] . AURKB localized within the nucleus of prophase-arrested oocytes and with the meiotic spindle at Met I and II ( Figure 1A ) . This localization pattern is different than that of AURKC , which localizes to kinetochores and the Met I inter-chromatid axis [23] , [24] . To further confirm the specificity of the antibody , we examined AURKB in oocytes from Aurkc−/− mice . In oocytes from WT littermates , AURKB localized to the meiotic spindle , but in Aurkc−/− oocytes at Met II AURKB localized to kinetochores ( Figure 1B ) . Exogenous AURKB-GFP also localized to kinetochores in Aurkb−/− and Aurkc−/− oocytes at Met II , as previously demonstrated [23] where it co-localized with Survivin , a CPC subunit ( Figure 1C ) . In addition , this co-localization occurred at Met I ( Figure S2 ) . Similar to overexpression of AURKB-GFP in WT oocytes , we still detect AURKB-GFP in the spindle region in the single knockout oocytes ( Figure S2 ) . We also assessed the specificity of the antibody by immunoblot analysis . We microinjected wild-type oocytes with cRNAs encoding Aurka-Gfp , Aurkb-Gfp , or Aurkc-Gfp . Probing with anti-GFP antibody , confirmed expression in each group ( Figure 1D , top panel ) . When we probed the membrane with the anti-AURKB antibody , it cross-reacted only with oocytes injected with Aurkb-Gfp ( Figure 1D , middle panel ) . We , and others , previously demonstrated the presence of Aurkb mRNA in oocytes [6] , [23]–[25] , and another report documents AURKB protein in mouse oocytes [33] . Furthermore , when we probed pooled whole-cell lysates from 150 non-injected oocytes , we detected a band at ∼40 kDa , which is the expected size of endogenous AURKB ( Figure 1D , middle panel , right-most lane ) . Taken together , these data support the model that AURKB is expressed in mouse oocytes . Furthermore , the localization of endogenous AURKB at kinetochores in oocytes from Aurkc−/− mice supports the hypotheses that either AURKB compensates for the loss of AURKC [23] and/or endogenous AURKC competes with AURKB for localization at kinetochores and the inter-chromatid axis . To investigate whether AURKC-DN ( Figure 2A ) disrupts both AURKB and AURKC function in oocyte meiosis , we eliminated issues with redundancies by using oocytes from Aurkc−/− mice ( i . e . containing only AURKB ) [23] . As anticipated , microinjection of WT or Aurkc−/− oocytes with Aurkc-DN cRNA resulted in the same phenotypes . These oocytes did not have detectable phosphorylated INCENP ( pINCENP ) ( Figure 2B–C ) . They contained misaligned univalent chromosomes , and they failed to divide , as examined by polar body ( PB ) extrusion ( Figure 2B , D ) . These data support the second model: AURKC-DN perturbs the function of both AURKB and AURKC , making it inadequate for assigning specific function to AURKC . Because AURKC-DN disrupts the function of both AURKB and AURKC , and because AURKB compensates for loss of AURKC [23] , we sought to develop a tool to selectively perturb AURKC function . The dominant negative mutation involves changing two threonines in the activation loop to non-phosphorylatable alanine residues [6] , [34] ( Figure 2A ) . Therefore , this mutant , while not activated , can presumably bind ATP and substrates . Protein kinases are commonly bi-lobed in structure thereby generating a pocket for ATP binding and catalysis . Within the ATP binding pocket are conserved “gatekeeper” residues that restrict binding of other intracellular molecules [35] , [36] . Mutation of a gatekeeper residue to an alanine enlarges the pocket , but generally does not perturb the function of most protein kinases . However , approximately 30% of kinases do not tolerate a mutation of gatekeeper residues [31] , [35] , [36] . Mutation of the gatekeeper residue ( L159 ) in murine AURKB renders the kinase inactive , and it is unable to phosphorylate histone H3 in mitotic cell extracts [31] . Based on protein sequence alignment , we determined that the gatekeeper residue in AURKC is L93 ( Figure 3A ) . Because mutation at this residue likely affects ATP binding instead of activation , we postulated that mutating L93 to A ( hereafter referred to as AURKC-LA ) might behave differently than the dominant negative and could selectively disrupt AURKC . To first confirm that AURKC-LA is not active , we microinjected WT oocytes with Aurkc-LA-Gfp cRNA . We assessed activity by immunostaining the injected oocytes with phospho-specific antibodies that recognize AURKB/C substrates . We found loss of auto-phosphorylated AURKC activation signal ( pAURKC; pT171 ) and significantly decreased pINCENP ( pS893/S894 ) compared to control injected oocytes ( Figure 3B–E ) . Compared to controls , phosphorylated histone H3 ( pH3S10 ) was reduced by ∼50% in LA-injected oocytes ( Figure 3F–G ) . The levels of pAURKC and pINCENP were reduced almost to the same levels as they were in Aurkc-DN injected oocytes ( Figure 3B , D ) . On the other hand , pH3S10 signals were completely inhibited only in the AURKC-DN oocytes , suggesting that H3S10 is a target of both AURKB and AURKC ( Figure 3F–G ) . We note that phosphorylation of H3S10 in mitotic cells is less sensitive to localized AURKB activity compared to other substrates [37] , and our data is consistent with this observation . These data suggest that AURKC-LA is catalytically inactive , and that it inhibits endogenous AURK/CPC activity . To test our hypothesis that the gatekeeper mutant specifically inhibits AURKC , we first microinjected Aurkc-LA in Aurkb−/− oocytes ( which express only AURKC; Figure 4A–C ) and in Aurkc−/− oocytes ( which express only AURKB , and that compensates for AURKC [23]; Figure 4D–F ) . Control mice were WT littermates from each genetic background . AURKC-LA significantly reduced INCENP phosphorylation and PB emission in Aurkb−/− oocytes suggesting that the catalytically inactive AURKC-LA efficiently disrupts endogenous AURKC function ( Figure 4A–C ) . Importantly , Aurkc−/− oocytes expressing AURKC-LA extruded PBs and had normal levels of phosphorylated INCENP , similar to WT and KO injected controls ( Figure 4D–F ) . These data confirm that AURKC-LA selectively disrupts AURKC function without disrupting endogenous AURKB function . To further test the specificity of AURKC-LA , we conducted a rescue experiment in WT oocytes . Co-expression of WT AURKC rescued the AURKC-LA phenotypes . INCENP was phosphorylated to near control levels and PBs were extruded . Co-expression of WT AURKB did not rescue the phenotypes ( Figure 4G–I ) . These data further confirm that AURKC-LA does not perturb AURKB function and suggests that the defect in INCENP phosphorylation and block in meiotic maturation are specific for loss of AURKC function . In budding yeast , aurora kinase activity is required for proper CPC localization and prevents premature localization of the CPC to the spindle [38] . In mitotically dividing tissue culture cell lines , inactive AURKB mutants fail to localize normally at centromeres [31] , [39] . To investigate if AURKC-LA behaves similar to WT AURKC , we analyzed its subcellular localization in oocytes at Met I . WT AURKC localized to kinetochores and inter-chromatid axes of Met I oocytes ( Figure 5A ) as previously reported [23] , [24] . On the other hand , AURKC-LA and AURKC-DN failed to localize normally . Both mutants localized predominantly with the spindle . Therefore , AURKC activity may be required to regulate CPC localization . To examine the changes in CPC localization , we first assessed the localization of endogenous AURKC . When oocytes expressed either AURKC-LA or AURKC-DN we could not detect endogenous AURKC on the chromosomes ( Figure 5B ) . For reasons not determined , we note that the antibody used to detect AURKC on chromosomes is not compatible with detecting de-localized AURKC-LA . Survivin is also a member of the CPC , and is expressed during mouse oocyte meiosis [40] , [41] . Similar to AURKC-DN , oocytes expressing AURKC-LA resulted in displacement of the CPC at Met I as evidenced by the loss of kinetochore and inter-chromatid axis localization of endogenous Survivin ( Figure 5C–D ) . Importantly , AURKC-LA did not alter the spindle localization of AURKB as compared to AURKC-DN , further supporting our evidence that AURKC-LA selectively perturbs AURKC ( Figure 5E ) . These findings are consistent with previous observations that loss of AURKB/C kinase activity by using small molecule inhibitors results in displacement and atypical localization of the CPC in mitosis [31] , [42] and oocyte meiosis ( our unpublished observations ) . Similar to AURKC-DN expressing oocytes , oocytes expressing AURKC-LA were defective in meiotic progression . The majority of oocytes expressing AURKC-LA ( ∼60% ) failed to extrude PBs . The kinetics with which those that did extrude PBs were ∼1 h delayed compared to controls . For AURKC-DN expressing oocytes , this failure was more pronounced ( ∼95% ) ; these oocytes initially extruded PBs , but then retracted them , as previously reported ( Figure 6A ) [6] . The discrepancy suggests that AURKB carries out meiotic functions during MI that do not require AURKC activity . To understand the biological significance of this different phenotype , we first focused on Met I . Both AURKC-LA and AURKC-DN expressing oocytes have nearly the same chromosome misalignment phenotype at Met I ( Figure 6B–C ) , suggesting that AURKC-CPC is the main CPC complex from prophase of MI through Met I , and that AURKC is essential for chromosome alignment . To examine the chromosome alignment phenotype in more detail , we imaged control-injected and AURKC-LA-injected oocytes live . Both groups expressed H2B-GFP to mark chromosomes . Unlike in controls , chromosomes in AURKC-LA oocytes oscillated between nearly aligned and misaligned for the duration of the imaging ( Figure 6D , Movies S1 , S2 ) . The presence of misaligned chromosomes in oocytes expressing AURKC-LA could be due to a chromosome alignment problem , or may reflect a cell-cycle delay in oocyte progression to Met I . To discriminate between these possibilities , we blocked Met I exit by injecting oocytes with non-degradable cyclin B1 ( Ccnb1-Δ90 ) [23] , [43] to allow oocytes more time to align their chromosomes . Strikingly , unlike control oocytes , the majority of the oocytes expressing AURKC-LA still had misaligned chromosomes even after spending 8 hours at Met I ( Figure 6E ) . These data indicate that AURKC activity is indispensable for chromosome alignment in mouse oocyte meiosis . The majority of oocytes expressing AURKC-LA arrested at Met I with bivalent chromosomes ( Figure S1 ) . Given the severe chromosome misalignment at Met I , it was expected that AURK-DN expressing oocytes would also arrest at Met I [44] . As previously reported , all oocytes expressing AURKC-DN contain univalent chromosomes ( Figure S1 ) [6] . The presence of univalents suggests an active Anaphase Promoting Complex/Cyclosome ( APC/C ) and separation of homologous chromosomes . The regulatory mechanism responsible for controlling APC/C is called the Spindle Assembly Checkpoint ( SAC ) . The SAC signals the delay of anaphase onset until all chromosomes acquire the correct kinetochore-microtubule attachment either in mitosis [45] , [46] or oocyte meiosis [9] , [10] , [47] , [48] . We investigated the ability of AURKC-LA and AURKC-DN to maintain the SAC . To conduct these studies , we incubated control , Aurkc-LA or Aurkc-DN injected oocytes in nocodazole , a microtubule-depolymerizing drug that keeps the SAC active in WT cells because of an absence of K-MT attachments . As expected only oocytes expressing AURKC-DN extruded PBs in the presence of nocodazole ( Figure 7A , B ) . We obtained similar results when nocodazole was used at a lower dose that does not completely depolymerize the spindle ( Figure S3 ) . These results indicate that AURKB has a role in maintaining an active SAC signal . ZM447439 is a pan-Aurora kinase inhibitor with higher specificity for AURKB than AURKC and AURKA [49] . Oocytes incubated in a high concentration of ZM447439 ( 10 µM ) bypass the SAC [30] . This dose likely inhibits both AURKB and AURKC . Our data indicate that AURKC-CPC is not the sole CPC involved in SAC signaling , but it possible that its function overlaps with AURKB . To investigate this possibility , we incubated oocytes expressing AURKC-LA with a low dose of ZM447439 ( 2 µM ) that does not normally bypass the SAC ( Figure 7A–B ) , in the presence of nocodazole . This is a dose that likely only inhibits AURKB . When AURKB was inhibited in oocytes expressing AURKC-LA , they bypassed the SAC and extruded PBs ( Figure 7A , B ) . These data suggest that the SAC is controlled by both AURKB and AURKC . In somatic cells , the CPC kinase ( AURKB ) promotes the kinetochore recruitment of key SAC components including BUB1 ( Budding uninhibited by benzimidazoles 1 ) [50] . To further validate our findings , we microinjected Bub1-Gfp cRNA [48] along with Aurkc-LA or Aurkc-DN cRNAs into oocytes . Again , loss of AURKC function alone did not perturb BUB1 kinetochore localization ( Figure 7C–D ) . But when AURKB was also inhibited , BUB1 failed to localize to the kinetochores ( Figure 7C–D ) . These data confirm that AURKC is not the sole CPC kinase involved in SAC signaling . Arrest at Met I is the predominant phenotype observed in oocytes expressing AURKC-LA , but there is small percentage of oocytes which do extrude PBs ( Figure 6A ) . Consistent with Yang et al . , AURKC-DN expressing oocytes began to extrude PBs , but failed to complete cytokinesis and subsequently retracted the PBs [6] ( Figure 8A , Movie S4 ) . This phenotype is reminiscent of oocytes cultured in pan Aurora kinase inhibitors ZM447439 and AZD1152 [17] , [25] , [32] . Unlike AURKC-DN , AURKC-LA expressing oocytes that progressed through Met I extruded PBs normally without any evidence of cytokinesis failure suggesting that AURK-CPC is not the sole CPC controlling cytokinesis , and that AURKB may be important for this function ( Figure 8A–C; Movie S3 ) . Progression to Met II did not depend on expression level of the mutant protein . In a zoomed out image of supplemental movie 3 ( Movie S5 ) , the oocyte expressing less AURKC-LA arrested at Met I , and the oocyte expressing more AURKC-LA extruded a polar body . To further confirm our hypothesis , we investigated pINCENP as a marker of CPC activity at telophase I ( Telo I ) . Similar to controls , oocytes expressing AURKC-LA contained phosphorylated INCENP at the mid-body ( Figure 8D ) . These data further support our observations that AURKC activity is dispensable for cytokinesis in oocytes . Oocytes lacking AURKB contain pINCENP at the midbody , but when we microinjected Aurkc-LA cRNA in Aurkb−/− oocytes ( contain only AURKC ) we did not detect phosphorylated INCENP . These data suggest overlapping AURKB-CPC and AURKC-CPC activities control cytokinesis ( Figure 8D ) . To investigate the biological significance of selectively perturbing AURKC during MI , AURKC-LA expressing oocytes were examined for aneuploidy using an in situ chromosome spread method [7] , [51] . The percentage of aneuploid eggs was significantly higher in Aurkc-LA-injected oocytes ( that did not arrest at Met I ) compared to controls ( Figure 8E–F ) . We did not assess ploidy when both AURKB and AURKC kinases were perturbed because no PBs were extruded , and therefore resulted in 100% polyploidy , as previously described [6] . Thus , AURKC function is critical for faithful chromosome segregation in oocyte meiosis . In mouse oocytes lateral interactions between microtubules and chromosomes drive the early stages of pro-Met I , but the final and sharp alignment of chromosomes at the Met I plate requires end-on K-MT attachments [52] . Brunet and colleagues performed nocodazole washout experiments to examine spindle recovery in mouse oocytes . They found that some chromosomes moved towards the spindle poles ( where K-MT end-on attachment is established ) before congressing to the metaphase plate . In agreement with this observation , in tissue culture cell lines the mal-oriented , but not bi-oriented , chromosomes move to the mitotic spindle pole until correct attachments are made , and then alignment at the metaphase plate is achieved [53] . We therefore hypothesized that failure to correct erroneous K-MT attachments leads to the misaligned chromosomes that are adjacent to the spindle poles ( Figure 6B ) , in Aurkc-LA-injected oocytes . When K-MT attachments are correct at Met I , the bivalent chromosomes are bi-oriented with monotelic attachment of each sister pair to opposite poles . This type of attachment generates tension leading to greater separation between the two sister-kinetochore pairs of each homologous chromosome . Incorrect attachment ( merotelic and syntelic ) leads to decreased tension and reduced separation between the two sister-kinetochore pairs of each homologous chromosome ( Figure 9A–B ) [10] , [54] . Similar to AURKC-DN-expressing oocytes , oocytes expressing AURKC-LA showed significantly shorter inter-kinetochore distance ( detected by CREST anti-serum ) compared to control oocytes . These data imply that the error correction mechanism is impaired in these oocytes . Moreover , the majority of the misaligned bivalents had incorrect attachments as evidenced by the decrease of the inter-kinetochore distance ( Figure 9C ) . These data suggest that the chromosome misalignment phenotype after disruption of AURKC function might be , at least in part , due to a defect in correcting improper K-MT attachments . This result is consistent with the conclusion that mitotic cells lacking AURKB activity fail to align chromosomes due to inability to correct abnormal attachment [54] . To further confirm that correcting erroneous K-MT attachments depends upon AURKC , we conducted an assay to determine the presence of stable end-on attachments of K-MTs to kinetochores . Microtubules that form stable kinetochore attachment are cold stable , whereas microtubules that do not form stable attachments with kinetochores are cold labile [55] . We exposed Met I oocytes to a pulse of cold medium , prior to fixation and immunocytochemistry to detect kinetochores and microtubules . Aurkc-LA-injected oocytes had a significantly greater percentage of abnormal ( merotelic and syntelic ) attachments than mCherry-injected controls ( Figure 9D–E ) . The percentage of abnormal K-MT attachments in Aurkc-LA-injected oocytes was similar to that of Aurkc-DN-injected oocytes ( Figure 9E and [6] ) . We suggest that AURKC-CPC is the predominant form of the CPC that corrects erroneous K-MT attachments and for chromosome alignment in mouse oocyte meiosis .
Distinguishing the roles of AURKB and C has been complicated by many factors . The two kinases are highly similar in sequence and appear to compensate for one another . One logical interpretation is that AURKB is the predominant CPC kinase in mitosis while AURKC is the predominant CPC kinase in meiosis . This model is supported by a report that detected no AURKB protein in mouse oocytes by immunoblotting [6] . But a collection of observations suggests that AURKB is found in oocytes . For example , oocytes express Aurkb mRNA [24] , [25] and overexpression of AURKB , but not AURKC , rescues defects induced by a low dose of ZM447439 , a pan Aurora kinase inhibitor with highest affinity for AURKB [24] , [33] , [49] . In this report , using a different antibody and mouse strains than in the previous report [6] we detected AURKB protein in mouse oocytes by immunoblot , and showed that it localized to centromeres in Aurkc−/− oocytes ( Figure 1 ) . These data provide evidence that AURKB is expressed in mouse oocytes and support our previous report [23] . Dominant negative alleles of AURKB and AURKC perturb themselves and one another when expressed in mitosis [22] . It is therefore not surprising that when we expressed AURKC-DN in Aurkc−/− oocytes , endogenous AURKB was also perturbed ( Figure 2B ) . A second model to consider is the notion that the mouse genome may contain multiple copies of Aurkc [56] and that the knockout is not completely void of AURKC protein . Another group has revisited the updated mouse genome sequence and found that coding regions of Aurkc are not duplicated [57] . Moreover , when we probed oocytes from Aurkc−/− mice , we did not detect any Aurkc transcript or protein [23] . Importantly , until this study , whether AURKB and AURKC have any non-overlapping functions was not known . By selectively disrupting AURKC function in oocytes , we have shown for the first time that AURKC has distinct functions from AURKB in mouse oocytes . We find that AURKC corrects erroneous K-MT attachments , a likely cause of chromosome misalignment at Met I ( Figures 6 , 9 ) . This failure to align chromosomes caused a Met I arrest , as one would expect given an intact SAC ( Figure 7 ) . But the small percentage of oocytes that presumably had mild chromosome misalignment , likely below the threshold of maintaining SAC activation , extruded PBs without any evidence of cytokinesis failure ( Figure 8 ) . We found that these oocytes were aneuploid ( Figure 8 ) . In our experiments , AURKC-LA is expressed from the GV stage though out meiosis . We note that , this prolonged duration of expression could make analysis of the Met II phenotype more challenging . These later roles of AURKB and AURKC will be important to address in future studies . Thus , AURKC-CPC appears to be the predominant CPC that corrects improper K-MT attachments , a function essential for preventing aneuploidy . We are interested in understanding why meiosis might require two CPC kinases whereas most mitotic cells have only one . In mitosis , AURKB directly maintains SAC activation by recruiting components such as BUB1 to kinetochores [39] , [50] , [58] , [59] and indirectly participates in the SAC by destabilizing K-MTs . Given the presence of two forms of the CPC in oocyte meiosis , we propose a separation of function model: AURKB-CPC recruits BUB1 to kinetochores , while AURKC-CPC destabilizes improper K-MT attachments . In agreement with this hypothesis , we observed bypass of SAC-inducing conditions only when we inhibited both AURKB/C ( Figure 6 ) and arrest at Met I when we inhibited only AURKC ( Figure 6 ) . This strategy to use complementary AURKB and AURKC functions to control the SAC may be critical to provide an insurance mechanism to prevent aneuploidy in a transcriptionally quiescent cell type where AURKB protein is not stable [23] . Loss of AURKC function did not affect pINCENP at the mid-body or induce cytokinesis failure ( Figure 8 ) . These data suggest that the CPC containing AURKC as the catalytic subunit is not the predominant form of the CPC that regulates cytokinesis . AURKB-CPC plays an important role in mitotic cytokinesis by phosphorylating many substrates , including INCENP , at the midbody [15] , [16] , [60] , [61] . However , INCENP is phosphorylated in oocytes that lack AURKB ( Figure 8 ) . It is possible that AURKC compensates for loss of AURKB in the knockout oocytes . Interestingly , mitotic cytokinesis requires an increased amount of AURKB activity , as compared to its metaphase functions [62] . AURKB and AURKC both localize to the midbody in oocytes [24] , [33] . Therefore , it is possible that oocytes satisfy the need for elevated AURK activity at the midbody by having overlapping functions of 2 forms of the CPC available , differing only in the catalytic subunit , and further examination is needed . We have not yet determined why the gatekeeper mutant displays specificity for affecting only AURKC . We have eliminated that possibility that these mutants were expressed at different levels in our system or in the different genetic backgrounds ( Figure S4 ) . We have also ruled out possible differences in catalytic activity because oocytes expressing AURKC-LA also showed complete loss of AURKC and INCENP phosphorylation ( Figure 3 ) . In both mutants the activation loop is not phosphorylated but the proteins are different . In the DN protein , the threonines are mutated to alanines , whereas in the LA protein the threonines are present but do not contain phosphate . The activation loop of protein kinases is important not only for catalytic activity but also for conformation stabilization , the ability to bind substrates , and for substrate specificity [63] . The conformation of the ATP binding pocket is also critical for protein structure [22] . Similar to our observations with AURKC-DN , mutation of the activation loop threonines to alanines in protein kinase C ( PKC ) alpha loosens its specificity and the mutant inhibits the other PKC isoforms [64] . Although we are not certain as to the mechanism of inhibition of the LA protein , one model to investigate is that AURKC functions as a dimer within the CPC , and that the gatekeeper mutant functions as a dominant negative only in the context of an AURKC dimer . To our knowledge there is no evidence that AURKB dimerizes , and it would be interesting if this mechanism were AURKC-specific . Alternatively , AURKC-LA may function as a pseudokinase . Pseudokinases have high sequence homology to kinases but do not have detectable catalytic activity [65] , [66] . Some of these proteins contain amino acid substitutions in gatekeeper residues of their ATP binding pockets that would ablate ATP binding or efficient catalysis [65] . If AURKC-LA were acting as a pseudokinase it could be preventing WT AURKC from binding the CPC . Most significantly , in Aurkc−/− oocytes , where AURKB is the only CPC kinase , expression of AURKC-LA but not AURKC-DN resulted in normal meiotic progression and CPC kinase activity . Therefore it is clear that the main difference between these mutants is the inability of AURKC-LA to compete with endogenous AURKB function . To our knowledge , this is the first report to separate AURKB and AURKC meiotic functions , and is consistent with some of the proposed models [17] , [23] . AURKC is expressed in other cell types , including testes , neuronal tissue and some cancer cells [21] , [29] , [67] , [68] . The biological significance of AURKC expression in cancer cells is also of clinical interest , but not well understood . Because these cells also express AURKB , studying the functions of AURKC in cancer cell division poses the same specficitiy difficulties as in oocytes . With our validation of AURKC-LA being specific for disrupting AURKC function , we propose that this gatekeeper mutant will be helpful tool for answering questions relevant to the reproductive and cancer fields
Details for generating and genotyping Aurkc−/− mice were described previously [23] , [67] . The Aurkbfl/fl mice were a generous gift from M . Malumbres ( CNIO , Spain ) [29] . For generating Aurkbfl/fl ZP3-Cre mice , female mice carrying the Aurkb floxed alleles were crossed with ZP3-Cre males ( Jackson laboratories ) [69] , and genotyping for the LoxP sites was carried out as previously described [29] . Cre genotyping was carried out as described by Jackson Laboratories . A detailed phenotypic description will be described elsewhere . All animals were in a mixed background of C57BL/6J , 129/Sv , and CD1 and maintained following Institutional Animal Use and Care Committee and National Institutes of Health ( NIH ) guidelines . Generation of non-degradable cyclin B , Aurka , Aurkb , and Aurkc-Gfp were described previously [24] , [43] . To generate Bub1-Gfp , murine Bub , sequence was amplified via PCR from a cDNA clone , ( Open Biosystems , #3671932 ) and ligated into pIVT-GFP [70] . Aurkc-LA and Aurkc-DN mutants were generated by site-directed mutagenesis using the QuikChange Multi-site Mutagenesis kit ( Agilent Technologies ) following manufacturer's instructions . To generate Aurkc-DN T171 and 175 were changed to an A ( ACA and ACT to GCC; Figure 2A ) . To generate Aurkc-L93 was changed to an A ( CTG to GCC; Figure 3A ) . DNA linearization of all Gfp- and mCherry- containing constructs was carried out using Nde I ( New England BioLabs ) . After DNA linearization , the digests were purified ( Qiagen , QIAquick PCR Purification ) and in vitro transcription was carried out using an mMessage mMachine T7 kit ( Ambion ) according to the manufacturer's instructions . Finally , the cRNA was purified using an RNAEasy kit ( Qiagen ) . Full-grown , GV-intact oocytes were obtained from pregnant mare serum gonadotropin- ( PMSG ) ( Calbiochem #367222 ) primed ( 44–48 h before collection ) , 6-wk-old female mice as previously described [71] . The collection and injection medium for oocytes was bicarbonate-free minimal essential medium ( MEM ) containing , 25 mM Hepes , pH 7 . 3 , 3 mg/ml polyvinylpyrollidone ( MEM/PVP ) and 2 . 5 µM milrinone ( Sigma #M4659 ) to prevent meiotic resumption [72] . Denuded GV oocytes were microinjected with ∼10 pl of 0 . 8–1 µg/µl of the indicated cRNA , unless otherwise noted . Following microinjection , the oocytes were cultured in Chatot , Ziomek , and Bavister ( CZB ) medium containing 2 . 5 µM milrinone . All culture and in vitro meiotic maturation occurred in a humidified incubator with 5% CO2 in air at 37°C . For the oocytes that were examined at Met II , we incubated the injected oocytes for 1–3 h prior to meiotic maturation , and for the oocytes that were examined at Met I , we incubated the injected oocytes overnight ( 14 h ) prior to meiotic maturation . In vitro meiotic maturation was conducted in milrinone-free CZB medium for periods of 6–7 h ( Met I ) , 9 h ( Telo I ) or 16 h ( Met II ) . Nocodazole ( Sigma #M1404 ) and ZM447439 ( Tocris #2458 ) were dissolved in dimethyl sulfoxide ( DMSO ) . Nocodazole and ZM447439 were added to CZB culture medium to a final concentration of 5 µM and 2 µM , respectively , and in vitro maturation was performed in a humidified chamber ( Becton Dickinson #353037 ) . For analysis of cold-stable microtubules , oocytes were incubated for 5 minutes on ice in MEM/PVP , and then fixed for 25 minutes at 37°C in 3 . 7% formaldehyde in 100 mM Pipes , pH 6 . 8 , containing 10 mM EGTA , 1 mM MgCl2 and 0 . 2% Triton X-100 [73] . AURKC-GFP was detected after fixation in 3 . 7% paraformaldehyde in phosphate-buffered saline ( PBS ) for 1 hour; survivin was detected by similar fixation conditions plus 0 . 1% Triton X-100 . In all other experiments , oocytes were fixed in 2–2 . 5% paraformaldehyde in PBS for 20 minutes at room temperature . After fixation , the cells were permeabilized with 0 . 1% Triton X-100 in PBS for 15 minutes and transferred to blocking buffer ( PBS+0 . 3% BSA+0 . 01% Tween-20 ) for 15 minutes . Immunostaining was performed by incubating the fixed oocytes with the primary antibody for 1 hour . After washing in blocking solution , the oocytes were incubated in secondary antibodies for 1 hour; omission of the primary antibody served as negative control . DNA was stained and mounted with 4′ , 6-Diamidino-2-Phenylindole , Dihydrochloride ( DAPI; Life Technologies #D1306; 1∶170 ) diluted in VectaShield ( Vector Laboratories ) under a coverslip with gentle compression . Fluorescence was detected on Zeiss 510 Meta laser-scanning confocal microscope under a 63× objective . All oocytes in the same experiment were processed at the same time . The laser power was adjusted to a level where signal intensity was just below saturation for the group that displayed the highest intensity and all images were then scanned at that pre-determined laser power . The intensity of fluorescence was quantified with NIH image J software keeping the processing parameters identical when experimental analysis required intensity measurements . Oocytes microinjected with the indicated cRNAs and histone H2B-mCherry cRNA were transferred into separate drops of CZB medium covered with mineral oil in a 96 well dish ( Greiner Bio One , #655892 ) . Bright field , GFP and mCherry image acquisition was started at the GV stage using an EVOS FL Auto Imaging System ( Life Technologies ) with a 20× objective . The microscope stage was heated to 37°C and 5% CO2 was maintained using the EVOS Onstage Incubator . Images of individual cells were acquired every 20 min and processed using NIH image J software . The following primary antibodies were used in immunofluorescence: CREST autoimmune serum ( Antibodies Incorporated; #15-234; 1∶30 ) , AURKB ( Abcam #AB2254; 1∶50 ) , AURKC ( Bethyl #A400-023A- BL1217; 1∶30 ) , pAURKC ( kind gift of T . Tang , Institute of Biomedical Science , Taiwan [6]; 1∶500 ) , phospho-specific Ser893/Ser894 INCENP ( kind gift of M . Lampson , UPenn [74]; 1∶1 , 000 ) , survivin ( Cell Signaling Technology #2808S; 1∶500 ) , α-tubulin-Alexa Fluor 488 conjugate ( Life Technologies #322588; 1∶100 ) , phospho-specific H3S10 ( Millipore; #05-806;1∶100 ) . Monastrol treatment , immunocytochemical detection of kinetochores and chromosome counting were performed as previously described [75] . Briefly , eggs were cultured for 2 hours in CZB containing 100 µM monastrol ( Sigma ) to disperse the chromosomes by collapsing the bipolar spindle to a monopolar spindle . Eggs were fixed in freshly prepared 2% paraformaldehyde and stained with CREST anti-serum to detect kinetochores and DAPI to detect DNA . Images were collected at 0 . 6-µm Z-intervals to capture the entire region of the MII spindle ( 16–20 µm total ) . To obtain a chromosome count for each egg , serial confocal sections were analyzed to determine the total number of kinetochores and calculated using NIH image J software . Oocytes were lysed in 1% SDS , 1% β-mercaptoethanol , 20% glycerol , and 50 mM Tris–HCl ( pH 6 . 8 ) , and denatured at 95°C for 5 min . Proteins separated by electrophoresis in 10% SDS polyacrylamide precast gel . Stained proteins of known molecular mass ( range: 14–200 kDa ) were run simultaneously as standards . The electrophoretically separated polypeptides were transferred to nitrocellulose membranes using a Trans-Blot Turbo Transfer System ( Bio-Rad ) then blocked by incubation in 2% blocking ( ECL blocking; Amersham ) solution in TBS-T ( Tris-buffered saline with 0 . 1% Tween 20 ) for 1 h . The membranes were then incubated with primary antibodies at 4°C overnight ( GFP ( Sigma #G6539; 1∶1 , 000 ) , β actin ( Abcam #ab20272; 1∶10 , 000 ) , AURKB ( Abcam #ab2254; 1∶500 ) , α-tubulin ( Sigma #T-6074; 1∶10 , 000 ) . After washing with TBS-T five times , the membranes were incubated with a secondary antibody labeled with horseradish peroxidase for 1 h followed with washing with TBS-T five times . The signals were detected using the ECL Select Western blotting detection reagents ( Amersham ) following the manufacturer's protocol . One-way ANOVA and Student's t-test , as indicated in figure legends , were used to evaluate the differences between groups using GraphPad Prism . The differences of p<0 . 05 were considered significant .
|
Precise control of chromosome segregation is essential for generating cells with the proper number of chromosomes . In germ cells , sperm and egg , an abnormal chromosome number leads to infertility , miscarriage , or , in the case of a live birth , developmental disorders such as Down Syndrome . For reasons not entirely clear , eggs are more prone to chromosome segregation mistakes than sperm . In this study , we study the role of a regulator of chromosome segregation , Aurora C kinase , in mouse oocytes . This is the first study to separate its function from Aurora B kinase that is highly similar in sequence . We find Aurora C is uniquely required to produce eggs with the proper number of chromosomes .
|
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2014
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Selective Disruption of Aurora C Kinase Reveals Distinct Functions from Aurora B Kinase during Meiosis in Mouse Oocytes
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A remarkable characteristic of the human major histocompatibility complex ( MHC ) is its extreme genetic diversity , which is maintained by balancing selection . In fact , the MHC complex remains one of the best-known examples of natural selection in humans , with well-established genetic signatures and biological mechanisms for the action of selection . Here , we present genetic and functional evidence that another gene with a fundamental role in MHC class I presentation , endoplasmic reticulum aminopeptidase 2 ( ERAP2 ) , has also evolved under balancing selection and contains a variant that affects antigen presentation . Specifically , genetic analyses of six human populations revealed strong and consistent signatures of balancing selection affecting ERAP2 . This selection maintains two highly differentiated haplotypes ( Haplotype A and Haplotype B ) , with frequencies 0 . 44 and 0 . 56 , respectively . We found that ERAP2 expressed from Haplotype B undergoes differential splicing and encodes a truncated protein , leading to nonsense-mediated decay of the mRNA . To investigate the consequences of ERAP2 deficiency on MHC presentation , we correlated surface MHC class I expression with ERAP2 genotypes in primary lymphocytes . Haplotype B homozygotes had lower levels of MHC class I expressed on the surface of B cells , suggesting that naturally occurring ERAP2 deficiency affects MHC presentation and immune response . Interestingly , an ERAP2 paralog , endoplasmic reticulum aminopeptidase 1 ( ERAP1 ) , also shows genetic signatures of balancing selection . Together , our findings link the genetic signatures of selection with an effect on splicing and a cellular phenotype . Although the precise selective pressure that maintains polymorphism is unknown , the demonstrated differences between the ERAP2 splice forms provide important insights into the potential mechanism for the action of selection .
Balancing selection maintains advantageous genetic diversity in populations . Unlike positive and purifying selection , which favor fixation of the fittest allele , balancing selection results in enhanced genetic and phenotypic variability in populations . Diversity can be maintained by overdominance ( the higher fitness of heterozygotes ) , frequency-dependent selection ( when an allele's effect on fitness varies with its frequency ) , fluctuating selection ( selection that changes in time or space ) , or pleiotropy ( selection on a variant that affects multiple traits ) . Over time , all of these processes leave the characteristic genetic footprint of balancing selection: an excess of polymorphism due to the long-term maintenance of selected alleles , and an enrichment of variants with a frequency close to the frequency equilibrium ( for example , an enrichment in variants at intermediate frequency if the optimal frequency of the selected variant is 0 . 5 ) . These , and related signatures allow the identification of candidate targets of balancing selection [1]–[3] . However , discerning the biological processes underlying balancing selection remains a challenge , even for loci with striking genetic signatures . As a result , there are few well-characterized examples of balancing selection in humans , with both clear genetic signatures and a known biological mechanism for the action of selection . One prominent exception is the major histocompatibility complex ( MHC ) class I locus , arguably the best-established target of natural selection in vertebrates [4]–[8] . The MHC class I locus is extremely polymorphic ( over 3000 alleles have been described in humans; see ebi . ac . uk/imgt/hla/stats . html ) and some of its ancestral polymorphism has been maintained for millions of years in several extant species ( i . e . , trans-species polymorphism ) [9] . Such extreme variability ensures MHC presentation of highly diverse antigenic peptides and , in turn , allows the detection of many different pathogens , improving the effectiveness of the immune system . Interestingly , another component involved in MHC function , the natural killer-cell proteins that recognize MHC-peptide complexes ( killer-cell immunoglobulin-like receptors , KIR ) , show signatures of balancing selection and coevolution with MHC class I [10]–[12] . The crucial role that MHC-mediated antigen presentation plays on individual survival explains the influence that balancing selection has on the evolution of MHC and KIR . In addition , we recently identified another key element of the MHC class I antigen-presentation process as a candidate target of balancing selection: endoplasmic reticulum aminopeptidase 2 ( ERAP2 ) [3] . The MHC class I-dependent antigen presentation pathway starts with the degradation of intracellular proteins by cytoplasmic proteases . Some of the resulting short peptides are translocated into the endoplasmic reticulum for the final trimming of their N-terminal residues by ERAP2 and its paralog , ERAP1 . The two proteins show different peptide specificity , and they act in a concerted fashion to generate peptides of the appropriate length and sequence for MHC class I binding and presentation . Once the MHC molecule and peptide are coupled , the complex is translocated to the cell surface , where presentation takes place . By performing the final trimming steps that ensure the presence of optimal MHC class I ligands , ERAP1 and ERAP2 play a key role in MHC antigen presentation ( reviewed in [13]–[19] ) . In addition to a role in peptide MHC class I presentation , ERAP1 and ERAP2 contribute to a number of other biological processes . Both genes are regulated by interferon γ IFN- γ and are involved in immune activation and inflammation [20] . They may also regulate angiogenesis and blood pressure [21] , [22] through the trimming of angiotensin II and angiotensin III , respectively [23] , [24] . ERAP1 and ERAP2 are down-regulated in some tumors , suggesting a role in the detection of transformed cells by immune surveillance [25] , [26] . ERAP1 genetic variants are associated with ankylosing spondylitis [27]–[30] , and cervical carcinoma [31]–[33] . Meanwhile , ERAP2 variants and expression levels have been associated with pre-eclampsia [34] , [35] , a dangerous hypertensive complication of pregnancy with both immunological and inflammatory components . Haroon and Inman [36] provide a more comprehensive review of the pathogenic potential of ERAP1 and ERAP2 . Of note , ERAP2 has not been studied as extensively as ERAP1 because of its absence in rodent ( e . g . , mouse , rat , and guinea pig ) genomes , although its phylogeny reveals that it was present in the primate-rodent common ancestor ( genome . ucsc . edu ) . Our earlier genomic study revealed increased polymorphism and the genetic signatures of balancing selection in ERAP2 in African-Americans and European-Americans [3] . Based on these data , we hypothesized that advantageous genetic diversity might enhance not only antigen presentation and recognition ( e . g . , MHC and KIR ) , but also earlier steps of the MHC antigen presentation pathway . Here , we present evidence to support this hypothesis . Specifically , we show that ERAP2 has distinct signatures of balancing selection in geographically diverse human groups , and that , interestingly , ERAP1 shows similar signatures of selection . Furthermore , we provide bioinformatic , molecular , cellular , and immunological evidence that identifies an ERAP2 putatively selected variant , establishes its effect on protein function , and demonstrates a downstream impact on MHC class I presentation .
ERAP2 is a 19-exon gene located on human chromosome 5q15 , residing between ERAP1 ( in the opposite orientation and likely sharing regulatory elements ) and leucyl-cystinyl aminopeptidase ( LNPEP ) ; see Figure S1 . We sequenced the complete protein-coding sequence ( cds ) and adjacent non-coding regions of ERAP2 in 180 individuals from 6 human populations: Luhya , Yoruba , Palestinian , Gujarati , Han , and Toscani . From these data , we identified 22 coding single-nucleotide polymorphisms ( SNPs ) and 57 non-coding SNPs . As a proxy for neutrality , we also sequenced 47 neutral genomic segments ( i . e . , control regions , see Materials and Methods for details ) , identifying 287 SNPs within our sample set . Figure 1A and 1B depicts the distribution of allele frequencies ( i . e . , the allele site frequency spectrum , SFS ) for ERAP2 and the control regions , respectively . With the control regions , the SFS shows a distinct skew towards low-frequency variants , as is typically seen in human datasets [37] . In contrast , with ERAP2 , there is a marked enrichment in intermediate-frequency variants . This excess of intermediate-frequency alleles is significant in all populations based on both the MWUhigh test [3] , [37] and Tajima's D analysis [38] ( Table 1 ) . Analyses of only coding SNPs reveal the same trend ( Figure S2 and Table S1 ) . Overall , ERAP2 shows strong and consistent signatures of balancing selection maintaining intermediate-frequency alleles . Our analyses of ERAP2 revealed 22 coding SNPs and 10 coding fixed differences with chimpanzee: 2 . 2 coding SNPs per fixed difference . This represents a 2 . 7-fold enrichment compared with the control regions , which have 0 . 82 SNPs per fixed difference ( 287 SNPs and 352 fixed differences ) . The excess of polymorphism is significant in two populations ( Palestinian and Gujarati ) and marginally non-significant in the Toscani group ( HKA test [39] , Table 1 ) , but fails to reach significance in the other populations ( likely due to the limited power of the short coding regions ) . Consistent with a relatively long-term influence of selection , ERAP2 does not show the characteristic long-range linkage disequilibrium ( LD ) of very recent balancing selection ( Figure S3 and Text S1 ) ; the estimated coalescent time of the locus is 1 . 44 Mya ( standard deviation: 550 , 000 years ) . The haplotype network of ERAP2 is highly structured , with two differentiated clades or haplogroups: ‘Haplotype A’ and ‘Haplotype B’ ( Figure 2 ) . The two haplotypes are differentiated by numerous SNPs , including four coding SNPs and a large number of non-coding SNPs ( not depicted ) . We refer to these SNPs as ‘diagnostic SNPs . ’ Each haplotype has a frequency around 0 . 5 in all populations ( Figure 2 ) , with the ancestral state set between the two haplotypes . The similar distribution of variants in the two haplogroups and their similar patterns of long-range LD ( see above ) , points to a similar age for each . Taken together , the signatures of selection and the maintenance of two haplogroups at similar frequencies suggest a functional difference between Haplotype A and Haplotype B . We identified four coding diagnostic SNPs that differentiate the coding sequence of Haplotype A and Haplotype B . Only one of these reflects a non-synonymous variant , resulting in a conservative change unlikely to influence protein function ( K392N , a basic polar residue to a neutral polar ) . Nevertheless , several studies have previously identified associations between SNPs in this genomic region and changes in ERAP2 expression and splicing [40]–[43] . In addition , a recent study identified an intronic variant that is associated with differential splicing of ERAP2 [44] . These studies suggest that ERAP2 variants can alter splicing , raising the possibility of differences in the splicing of ERAP2 mRNA expressed from Haplotype A versus Haplotype B . To explore this hypothesis , we sequenced the complete ERAP2 cDNA isolated from EBV-transformed lymphoblastoid cell lines ( LCLs ) derived from two HapMap individuals: one homozygous for Haplotype A ( AA-homozygote ) and one homozygous for Haplotype B ( BB-homozygote ) . We used LCLs because ERAP2 is highly expressed in lymphocytes [45] and this cell type is particularly relevant for studies of MHC class I presentation . One identified splicing form , which contains an extended exon 10 with 56 extra nucleotides ( AY028805 . 1 and AB163917 . 1 [20] ) , was observed only in Haplotype-B mRNAs ( Figure 3A ) . To confirm that this splice form is indeed specific to Haplotype B , we used PCR to isolate from cDNA the region across the exon 10 and exon 11 splice junction in 12 HapMap LCLs with varied genotypes ( Figure 3B ) . The exon 10 ‘extension’ was detected in all 4 BB-homozygotes but none of the 4 AA-homozygotes; both splice forms were detected in AB-heterozygotes . Therefore , Haplotype A-expressed ERAP2 is consistently spliced to contain the standard exon 10 , while Haplotype B-expressed ERAP2 is spliced to contain the extended version of exon 10 . These results are consistent with an in silico analysis of all publicly available ERAP2 mRNAs and ESTs ( Text S1 ) . We conclude that the haplotype-specific splicing of ERAP2 must be driven by a diagnostic SNP . Extension of exon 10 occurs when the standard splice site ( position 69 of exon 10 ) is skipped in favor of a downstream cryptic splice site at position 56 of intron 10 . Only one diagnostic SNP resides in the proximity of exon 10: rs2248374 , which lies within the 5′ canonical splice site ( Figure 3A ) . Haplotype A contains the rs2248374-A allele , while Haplotype B contains the rs2248374-G allele . In silico prediction of optimal splicing ( GeneID [46] ) with the rs2248374-A allele yields the Haplotype A splice form , while prediction with the rs2248374-G allele yields the Haplotype B splice form ( Text S1 ) . According to MaxEnt , a maximum entropy computational analysis of splice sites [47] , and as shown by Coulombe-Huntington et al . [44] , this is due to rs2248374 reducing the signal strength of the exon 10 donor splice site from 9 . 33 ( for the A allele ) to 7 . 61 ( for the G allele ) . Coulombe-Huntington et al . [44] studied 78 candidate loci of allele-specific splicing , and experimentally confirmed 6 of them , including rs2248374 and ERAP2 exon 10 . Together , these results show that the difference in ERAP2 splicing between Haplotypes A and B is due to rs2248374 , whose A and G alleles increase and reduce the strength of the splice site , respectively . The ERAP2 mRNA derived from Haplotype A encodes the canonical ( full-length ) ERAP2 protein consisting of 960 amino acids . In contrast , translation of the ERAP2 mRNA derived from Haplotype B would be predicted to produce a truncated protein of 534 amino acids , since the exon 10 extension contains two TAG stop codons ( Figure 3A ) . This second mRNA form was first reported in an early characterization of the gene [24] . We sought to detect the truncated form of ERAP2 by western blot analysis of protein extracted from LCLs using two antibodies that should detect both truncated and full-length forms of the protein . This analysis revealed that AA-homozygote cells produce only full-length ERAP2 ( 120 kDa ) , while BB-homozygote cells produce no detectable ERAP2 protein ( Figure 4 ) . Additionally , AB-heterozygotes only produce full-length ERAP2 , in seemingly smaller quantities compared to AA-homozygotes ( the intensity of the full-length ERAP2 band in AB-heterozygotes is 35% and 50% that of AA-homozygotes for the two antibodies , respectively ) . Therefore , only the full-length ERAP2 protein is detectable in LCLs , and only in AA-homozygotes and AB-heterozygotes . We did detect an extremely faint band in BB-homozygotes , of the size of the full-length ERAP2 protein , when the western was run with mouse 3F5 antibody [48] ( Figure S4 ) . This band could be due to unspecific binding of the mouse mAb 3F5 antibody , since unspecific bands were observed in that experiment ( Figure S4 ) ; however , if it corresponds to ERAP2 it likely derives from the very limited amount of ERAP2 Haplotype B that is spliced to contain the canonical exon 10 ( Figure 3B ) . This small amount of protein likely has no or very little biological relevance , particularly when compared with the high levels observed in AA-homozygotes and AB-heterozygotes . In any case , note that truncated ERAP2 protein ( 60 kDa ) could not be detected in this experiment ( Figure S4 ) . Nonsense-mediated decay ( NMD ) is a cellular process that degrades aberrant mRNAs , such as those with in-frame stop codons that encode truncated proteins . In ERAP2 , NMD has been shown to degrade a rare mRNA form detected in a mantle-cell lymphoma that included an extra exon ( after canonical exon 12 ) with an in-frame STOP codon [49] . The two stop codons present in exon 10 on Haplotype B also fulfill the established requirements for NMD [50] . Thus , the above-described absence of detectable truncated ERAP2 protein may be due to NMD of Haplotype B-derived mRNA . To test this hypothesis , we performed allele-specific quantitative real-time PCR ( qRT-PCR ) analysis of heterozygote LCLs under normal and NMD-inhibited conditions ( by treating the cells with emetine , which blocks translation and NMD ) . We specifically examined the expression of three coding diagnostic SNPs ( Figure 5A ) . All three SNPs showed significantly lower levels of ERAP2 mRNA expressed from Haplotype B versus Haplotype A ( Figure 5B ) for all AB-heterozygote cell lines . Inhibition of NMD resulted in similar levels of ERAP2 mRNA expression from Haplotypes A and B ( Figure 5B ) . These data indicate that NMD acts on Haplotype B-derived ERAP2 mRNA , accounting for both the reduced levels of Haplotype B-derived ERAP2 cDNA and the absence of truncated ERAP2 protein . Transient knock-down of ERAP1 and ERAP2 reduces the levels of MHC class I molecules on the surface of cultured cells [48] . To establish whether endogenous ERAP2 deficiency has a similar effect in BB-homozygotes , we examined the levels of MHC class I molecules on the surface of peripheral blood B cells by flow cytometry . Two experiments were performed to account for experimental variability . MHC class I ( HLA-ABC ) mean fluorescence intensities ( MFIs ) were lower on BB-homozygote cells compared to AA-homozygote cells; such a difference was not seen with CD19 , a marker constitutively expressed by B cells ( Figures S7 and S8 ) . AB-heterozygotes showed a high level of variability ( Figures S7 and S8 ) . To account for the intrinsic variability among human samples , the HLA-ABC MFIs were standardized relative to CD19 ( see Materials and Methods for details ) . Standardized HLA-ABC MFIs were also reduced in BB-homozygotes: a two-factor ANOVA showed that after controlling for differences among experiments ( a significant factor , P = 0 . 0011 ) , genotype significantly affects the level of standardized HLA-ABC MFIs ( P = 0 . 0137 ) . Such an effect is evident in both experiments ( Figure 6 ) , although the significance of the tests is reduced due to the smaller sample size ( T-test: experiment 1 , P-value = 0 . 0782; experiment 2 , P-value = 0 . 0471 ) . These results demonstrate that BB-homozygotes have reduced levels of MHC class I expression on B-cell surfaces . In order to determine whether the signatures of selection seen with ERAP2 are shared with its closely linked paralog ( ERAP1 ) , we analyzed the polymorphism data for ERAP1 generated with our sample of 180 individuals . The SFS for ERAP1 shows a slight enrichment in intermediate-frequency alleles ( Figure 1C ) , which results in a significant departure from neutral expectations in the Yoruba , Palestinian , Han , and Toscani populations as measured by the MWUhigh test ( Table 1 ) . The Yoruba , Han , and Toscani populations also show departures from neutral expectations according to Tajima's D analysis ( Table 1 ) . ERAP1 has 6 . 4 SNPs per fixed difference ( 45 coding SNPs and 7 coding fixed differences ) , a significant departure from neutral expectations ( HKA test , Table 1 ) . The estimated time to the most recent common ancestor of ERAP1 variants is 2 . 84 Mya ( standard deviation: 839 , 000 years ) . The ERAP1 haplotype network ( Figure 2B ) contains a large number of haplotypes , with a complex relationship among them and many reticulations that represent either recombination or recurrent mutation . In short , it does not reflect a highly structured haplotype network , likely due to the long-term effects of recombination . It is worth noting that LD between ERAP1 and ERAP2 is low ( Figure S9 ) , and the two most common ERAP1 haplotypes do not show linkage with the two major ERAP2 haplotypes ( data not shown ) , indicating that the ERAP1 signatures are independent from those of ERAP2 . Additionally , we found no association between the ERAP2 haplotypes and ERAP1 splicing or expression differences ( Text S1 ) .
By generating and analyzing high-quality genome-sequence data , we have demonstrated that ERAP2 has the distinct signatures of balancing selection that maintains intermediate-frequency alleles . These results validate our initial genome-wide findings [3] , and indicate that the selective agent is not population-specific , because the detected signatures are similar among geographically diverse human groups . Selection has maintained ERAP2 variants for an estimated 1 . 4 million years and , accordingly , the putatively selected variant rs2248374 is not polymorphic in chimpanzee ( sequence analysis , n = 19 ) or orangutan ( sequence analysis , n = 4 ) , and no annotated chimpanzee SNP is shared with humans ( dbSNP version 130 ) . Interestingly , the derived allele was observed in a 4 , 000-year-old Paleo-Eskimo [51] , showing that the non-functional ERAP2 form was present in ancient Homo sapiens populations . We are confident that the detected ERAP2 genetic signatures are due to selection on the gene rather than on adjacent loci ( e . g . , ERAP1 and LNPEP ) because ( a ) signatures of balancing selection are tight in humans [3] due to the long-term effects of recombination [52] , [53]; and ( b ) no linkage block shared between African , East Asian , and European HapMap populations links ERAP2 with ERAP1 or LNPEP ( Figure S9 ) . ERAP1 also shows signatures of selection , although the patterns are less dramatic than with ERAP2 . The excess of polymorphism ( over 7-fold compared with control regions ) and subsequent high estimated coalescence time ( 2 . 8 Mya ) , combined with a modest enrichment in intermediate-frequency variants , suggest long-term balancing selection acting on ERAP1 . Still , the gene lacks a striking excess of intermediate-frequency alleles as seen with ERAP2 , and its haplotype network is not highly structured due to the long-term effects of recombination . Taken together , these results suggest that ERAP1 has evolved under long-term balancing selection that either ( 1 ) maintains a large number of low-to-intermediate frequency variants; or ( 2 ) has changed , stopped , or weakened in recent evolutionary history . ERAP2 is particularly interesting due to the combination of its remarkable signatures of balancing selection and the pronounced functional differences between its two major haplotypes . Specifically , we showed that Haplotype A-derived mRNA encodes full-length , canonical ERAP2 , while Haplotype B-derived mRNA undergoes differential splicing and NMD , resulting in undetectable levels of ERAP2 . We studied LCLs , a particularly relevant cell type for MHC class I presentation . It is possible , though unlikely , that other tissues and/or developmental stages utilize alternate mechanisms that lead to the generation of ERAP2 protein from both haplotypes . Nevertheless , our data suggest that 25% of the population are AA homozygotes and generate abundant amounts of ERAP2 protein in lymphocytes , 50% are heterozygotes and generate reduced amounts of ERAP2 protein , and 25% are BB homozygotes and generate no or virtually no ERAP2 protein . Note that these frequencies are fairly consistent among all of the populations that we analyzed , as well as other human groups ( Text S1 ) . Therefore , based on our results , the ERAP2 genotype should be accounted for in interpreting ERAP2 studies , especially those focused on ERAP2 expression and ERAP2 protein function . For instance , it may be interesting to reassess previous studies of ERAP2 that used immortalized or cancer cell lines and reported contradictory results ( Text S1 ) . In light of the differences in ERAP2 expression from the A versus B haplotypes , what are the biological consequences of lower ERAP2 protein levels in AB and BB individuals ? The evidence that ERAP2 has a functional role in humans is both experimental [24] , [48] and evolutionary ( i . e . , the level of constraint of ERAP2 in humans is similar to that in other mammals; Tables S2 and S3 and Text S1 ) . ERAP1 and ERAP2 share 51% sequence identity [18] , and their protein products can form heterodimers [48] , though the functional nature of these dimmers remains elusive . While both ERAP1 and ERAP2 act as aminopeptidases , there are important differences in their peptide specificity [48]; for example , specific residues in the HIV-derived peptides R10L ( from the HIV-gag protein ) and K51I ( from the HIV-env protein ) are preferentially trimmed by ERAP2 [15] , [48] . ERAP1 and ERAP2 likely act in a concerted fashion to provide important protein-trimming activity in the human endoplasmic reticulum , with each differentially contributing to the pool of antigenic peptides [15] . A possible effect of ERAP2 deficiency could be an alteration in the set of peptides available for the MHC . For example , mouse studies have shown that knocking out ERAP1 results in alterations in the set of presented epitopes [54]–[56] and immunodominance hierarchy [57] . These changes ultimately influence T-cell response [58] . Remarkably , HIV evolves to avoid ERAP1 trimming [59] , suggesting that despite high redundancy in MHC class I presentation of proteins , the particular presented epitope ( which is highly dependent on antigen processing [60] ) influences immune response . The absence of ERAP2 in the mouse genome precludes performing similar knock-out studies as with ERAP1 , although one could envision a similar effect of ERAP2 deficiency in antigen presentation . Importantly , this alteration in the set of presented epitopes may have a previously unrecognized influence , for example , on immunological function , auto-immunity , and histocompatibility . In addition to these putative differences , we demonstrated that ERAP2 deficiency results in a quantitative reduction of MHC class I levels . Specifically , we found significantly less MHC class I on the surface of B cells from BB-homozygotes compared to AA-homozygotes . This result is consistent with the reduced MHC class I cell-surface expression observed after transient knock-down of ERAP1 or ERAP2 in cultured cells [48] , the reduced MHC class I cell-surface expression seen in ERAP1-knock-out mice [54]–[56] , [61] , and our observation that ERAP1 is not upregulated to compensate for ERAP2 deficiency in cells from BB-homozygotes ( Text S1 ) . The reduced MHC class I cell-surface expression might be due to reduced stability of the MHC complex when loaded with suboptimal peptides , as has been suggested with ERAP1-deficient mice [55] , [56] , [62] . Because we studied a natural deficiency of ERAP2 , our results suggest that the observed reduction in MHC class I levels is not transient and that BB-homozygotes likely have lower background levels of MHC presentation . The effect of ERAP2 knock-down is not evident when the antigen-processing machinery is activated by IFN-γ [48] , consistent with the results with ERAP1 knock-out mice [55] ( but see [63] ) . This suggests that rather than affecting inflammatory response , ERAP2 deficiency might be relevant to basal MHC class I presentation . Antigen processing is an inefficient process , with an estimated 10 , 000 proteins degraded to form a single MHC-peptide complex [64] . Therefore , reduced MHC class I levels may result in a lower presentation of rare antigens ( particularly , in this case , of those preferentially trimmed by ERAP2 ) , possibly delaying their specific immune response . Further studies that correlate ERAP2 genotype with levels of MHC class I expression in other tissues , and with the presentation and recognition of specific antigens , are needed to more clearly define the influence of ERAP2 deficiency on immune response . An important remaining question is what selective mechanism accounts for the maintenance of a decayed form of ERAP2 . Selection of polymorphic truncating variants is not unusual , with notable examples in domesticated species [65] , [66] and natural populations [67]–[69] . ERAP2 is involved in a variety of biological processes , including immunity , inflammation , and , perhaps , the regulation of blood pressure; it has also been linked to pathologies such as pre-eclampsia ( see Introduction ) . Therefore , a number of mechanisms may explain the balancing selection seen with ERAP2 . Overdominance is probably the most widely considered mechanism for balancing selection . In this case , overdominance could be explained if heterozygotes had the optimal level of ERAP2 protein . This would be unlikely if MHC levels are the selected phenotype , because MHC cell-surface expression is variable in heterozygotes ( Figure 6 ) . Regardless , AB-heterozygotes might have a different epitope hierarchy than AA or BB homozygotes that account for the putative selective advantage . Another possible mechanism is oscillating selection , where alternative genotypes are advantageous at different times . This has been proposed for FLT1 , a gene that , like ERAP2 , is associated with pre-eclampsia [70] . The short alleles of the FLT1 repetitive region are deleterious during malaria season but appear to be beneficial out of malaria season . There is no known link between malaria and ERAP2 genotypes , and the signatures of selection are observed in non-malaria-suffering regions . However , one can imagine other scenarios where seasonal agents could favor the AA or BB genotype at different times , with adequate temporal fluctuation and selective coefficients to maintain both alleles in the population . Another interesting mechanism of balancing selection is pleiotropic selection , where different genotypes are advantageous for different biological processes . This has been suggested as an explanation for the highly polymorphic KIR loci [12] , with KIR A haplotypes protecting against hepatitis C virus infection but being a risk factor for pre-eclampsia . In this model , differential selection between an immunological function and reproduction maintains genetic diversity . Interestingly , a recent study revealed an association between the ERAP2 Haplotype A and pre-eclampsia in an Australian cohort [34] . The presence of functional ERAP2 and the resulting high levels of MHC class I may be beneficial in some situations ( e . g . , in response to tumors or pathogens ) yet detrimental in others ( e . g . , in the case of auto-immunity ) . Immune-related genes are subject to natural selection in humans [71]–[74] , although the relative importance of positive and balancing selection is not fully defined ( reviewed in [75] ) . In the case of MHC class I presentation , the elements responsible for recognition and presentation of antigenic peptides have evolved under balancing selection [4]–[6] , [10]–[12] , as have the two genes that encode the enzymes responsible for the final trimming of antigenic peptides . The ERAP2 genetic diversity identified here has biological implications in terms of influencing the levels of MHC class I on the cell surface and likely downstream antigen presentation . Future studies should help to establish the influence that this genetic variation has on other biological processes , such as immunocompetence , histocompatibility , regulation of blood pressure , and risk to immune-related disorders such as auto-immunity and pre-eclampsia .
Anonymized samples for this study were derived from allogeneic blood donor samples that already existed and would otherwise be discarded . As the samples were provided anonymously , the NIH Office Of Human Subjects Research approved the use of these samples on an exemption basis , per federal code ( 45CFR46 ) , without the need for IRB review or informed consent . The complete ERAP2 coding region and some exon-adjacent intronic regions ( 8794 bp total , 2883 bp of which are protein coding ) were sequenced in 180 individuals from 6 geographically diverse human groups . Specifically , we studied 30 individuals from each of the following HapMap [76] populations: Yoruba ( Nigeria ) , Luhya ( Kenya ) , Gujarati Indians ( living in Houston , TX , USA ) , Han ( China ) , and Toscani ( Italy ) . As a representative Middle Eastern population , we also studied 30 Palestinian ( Israel ) individuals from the National Laboratory for the Genetics of Israeli Populations ( Tel-Aviv University ) . The same 180 individuals were also used for sequencing portions of the ERAP1 gene ( 9753 bp total , 2847 bp of coding sequence ) . The regions sequenced are shown in Figure S1 . Regions of interest were PCR-amplified and sequenced ( bidirectional Sanger-based sequencing ) , and SNPs were detected with Polyphred/Polyphrap . To minimize sequencing errors , variants residing within the first and last 50 bp of each amplified segment were discarded . Additionally , we manually reviewed all variants associated with discordant results between overlapping amplimers , variants with a quality score lower than 99 , singletons , and triallelic SNPs . The ancestry of each SNP was inferred through comparison with the chimpanzee , orangutan , and macaque genome sequences [77] , [78 , genome . ucsc . edu] . Fixed differences with chimpanzee were identified by comparison with the chimpanzee genome sequence [77] . As a proxy for neutrality , we sequenced 47 control regions . Such regions consisted of unlinked , ancient processed pseudogenes that do not encode a functional protein and are thus expected to evolve in a neutral fashion . The control regions are not part of gene families , are far from genes , do not overlap putative functional elements , are conserved as pseudogenes in chimpanzees , orangutans , and macaques , and have recombination rates and GC contents similar to coding genes . Details about these control regions can be found in the Text S1 . The generated sequence data were analyzed using three neutrality tests: MWUhigh , Tajima's D , and HKA . MWUhigh [37] compares the SFS of a region of interest with the SFS of a neutral region ( s ) ( e . g . , control regions ) to determine whether the former is consistent with neutral expectations [37] . Specifically , we applied MWUhigh to the folded SFS , which becomes significant only in the case of an excess of intermediate-frequency alleles [3] . Tajima's D [38] compares two estimates of θ ( the scaled mutation rate ) and , when significantly positive , identifies genealogies with long internal branches consistent with long-term balancing selection . Finally , HKA [39] identifies regions with an unusual density of polymorphisms when compared with divergence and with the patterns of neutral loci . For the HKA test , we focused only on coding regions and used the chimpanzee as an outgroup . MWUhigh was calculated using an in-house C script , while Tajima's D and HKA were calculated using libsequence [79] . The significance of all neutrality tests was assessed by 10 , 000 coalescent simulations with ms [80] . Selecting an appropriate demographic model for the simulations is crucial to avoid spurious detection of signatures of selection . Our null model followed a recently published demographic scenario that included African , Asian , and European populations [81] and that was a better fit to our control data than previously proposed demographic models . The divergence to chimpanzee was adjusted in the simulations to fit the ratio of SNPs to fixed differences of the control regions . Simulations were conditioned on the total number of informative sites , and the recombination rate was set to 10−6 per base pair , the estimated recombination rate of this genomic region ( genome . ucsc . edu ) . All analyses were performed with an in-house PERL program ( Neutrality Test Pipeline ) . Haplotypes of the coding SNPs were inferred using PHASE [82] , and the haplotype network was created with Network [83] . The estimated age of the haplotypes was calculated using Network and calibrated with chimpanzee , considering a divergence time of 6 Mya . We analyzed the ERAP2 cDNA from LCLs of HapMap Yoruba individuals with different genotypes: AA-homozygotes ( GM18504 , GM18505 , GM11832 , and GM07000 ) , BB-homozygotes ( GM18507 , GM19240 , GM12891 , and GM12892 ) , or AB-heterozygotes ( GM18861 , GM18870 , GM19137 , and GM19201 ) . The cell lines were obtained from the Corriell Cell Repositories ( ccr . coriell . org ) . Total RNA was isolated from each cell line using Trizol reagent ( Invitrogen ) and the RNeasy miniprep kit ( Qiagen ) . cDNA was synthesized from 1 µg of total RNA using the Superscript III First Strand Reverse Transcriptase Kit and random hexamers ( Invitrogen ) . The ERAP2 full-length transcript ( exons 1 to 19 ) was amplified using Expand High Fidelity PCR System ( Roche ) from cDNA prepared from LCLs that were AA-homozygote ( GM18504 ) or BB-homozygote ( GM18508 ) . These PCR products were cloned into the pCR4-TOPO vector ( Invitrogen ) and at least six clones for each haplotype were sequenced ( 3100 Genetic Analyzer , Applied Biosystems ) . Primer sequences for this experiment and for the exon 10 splice-variant screening can be found in Table S4 . The effect of rs2248374 on ERAP2 mRNA splicing was assessed using two in silico methods . First , we used GeneID [46] to predict the splicing of mRNA derived from the two haplotypes ( Text S1 ) . Second , we used MaxEnt [47] to predict the splicing potential of the constitutive splice site with: ( 1 ) the A allele: ATGGTAAGG; and ( 2 ) the G allele: ATGGTGAGG . Western blot analysis was performed as previously described [84] . Briefly , protein extracts from approximately 3×103 cells were separated on a 4–12% NuPage Bis-Tris gel ( Invitrogen ) at 125 V for 100 minutes in 1× NuPage MES SDS Running Buffer ( Invitrogen ) . After transfer to a nitrocellulose membrane , proteins were detected using a 1∶5 , 000 dilution of primary antibody [goat anti-ERAP2 polyclonal antibody ( AF3830 , R&D Systems ) and mouse anti-ERAP2 polyclonal ( ab69037 , Abcam ) ; anti-ß-actin monoclonal prepared in mouse ( A5316 , Sigma ) ] and a 1∶10 , 000 dilution of secondary antibody conjugated with horseradish peroxidase ( HRP ) [goat anti-mouse IgG ( sc-2005; Santa Cruz Biotechnology ) and donkey anti-goat IgG ( sc-2020; Santa Cruz Biotechnology ) ] . Proteins were then visualized by autoradiography after treatment with substrate to HRP ( Thermo Scientific ) for 5 minutes . The ratio of the intensity of the full-length ERAP2 band of AA-homozygotes to AB-heterozygotes was calculated using ImageJ ( rsbweb . nih . gov/ij/index . html ) . AB-heterozygote LCLs were treated with 100 µg/ml of emetine ( Sigma ) for 7 hours to inhibit NMD [50] . Parallel cultures were left untreated and grown at standard conditions . Total RNA was prepared from each cell line and used to generate cDNA as described earlier . We quantified haplotype-specific ERAP2 cDNA in triplicate using an allele-discriminating TaqMan genotyping assay for three coding diagnostic SNPs ( C_3282749_20 for rs2549782 , C_25649530_10 for rs2548538 , and C_25649516_10 for rs2287988; Applied Biosystems ) as previously described [85] . Briefly , for each allele-specific assay , we generated a standard curve consisting of serial dilutions of two HapMap genomic DNA samples homozygous for either the Haplotype A ( GM18504 ) or Haplotype B ( GM18508 ) allele . We used a heterozygous genomic DNA sample ( GM18861 ) to validate the regression equation , in which we expect to see a mean allelic ratio of 1 . 0 since both the Haplotype-A and Haplotype-B alleles are present in an equal proportion . Two experiments ( labeled 1 and 2 in Figure 6 ) were performed with 16 samples each . Human peripheral blood mononuclear cells ( PBMCs ) were isolated from buffy coats using a Ficoll/Histopaque gradient ( Lonza ) . PBMCs were washed and cultured using RPMI 1640 supplemented with 10% fetal calf serum , 1% penicillin and streptomycin , 0 . 2 M L-glutamine , and 20 mM Hepes . Surface staining was measured by flow cytometry using fluorescence-labeled antibodies specific to CD19 ( labeled with APC; clone HIB19; eBioscience ) and HLA-ABC ( labeled with FITC; clone W6/32; eBioscience ) which reacts to HLA-A , B , and C . Flow-cytometry data analysis was performed with Flojo software ( Treestar ) . Specifically , we measured HLA-ABC MFIs from a population of B cells gated by CD19 ( a constitutive B-cell marker ) intensity . Gating and analysis were carried out blindly with respect to genotypes . In order to standardize HLA-ABC MFI in light of the intrinsic variability among human samples , a standardized HLA-ABC measure was calculated for each sample by dividing the HLA-ABC MFI by the CD19 MFI for each sample . The values were partitioned by experiment and sub-partitioned by genotype; within each of these groups , outliers were removed ( defined as samples with values under or over 1 . 5-times the inter-quartile range ) . It is worth noting that the inclusion of outliers did not affect the results . Two sets of analyses were performed for each of these three measures ( HLA-ABC , CD19 , and standardized HLA-ABC ) as the dependent variable . First , a T-test was used to detect differences between cells with AA and BB genotypes for each experiment . Second , a two-factor ANOVA was performed for each measure using the data generated with all AA or BB samples , where the two factors of the ANOVA were genotype and experiment . Genotyping was performed by PCR amplification and sequencing of DNA prepared from the PBMCs ( DNeasy Blood and Tissue kit , Qiagen ) using primers flanking rs2248374 ( see Table S4 for primer sequences ) .
|
It has long been known that the extremely high levels of genetic diversity present in the major histocompatibility locus ( MHC ) are due to balancing selection , a type of natural selection that maintains advantageous genetic diversity in populations . The MHC encodes for molecules required for a type of antigen presentation that mediates detection of infected and cancerous cells by the immune system; the genetic diversity of the MHC thus ensures an adequate response to the wide variety of pathogens that humans encounter . Here , we show that other genes involved in the same antigen-presentation pathway are also subject to balancing selection in humans . Specifically , we show that balancing selection acts to maintain two forms of the endoplasmic reticulum aminopeptidase 2 gene ( ERAP2 ) , which encodes a protein also involved in antigen presentation . Although the two ERAP2 forms are present in a similar frequency ( close to 0 . 5 ) , they are associated with differences with respect to the levels of MHC molecules on the cell surface of immune cells . In summary , our findings show that natural selection maintains variants of ERAP2 that affect immune surveillance; they also establish ERAP2 as one of the few examples of balancing selection in humans where the selected variant , its functional consequences , and its influence in interpersonal diversity are known .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
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"immunology/antigen",
"processing",
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"recognition",
"genetics",
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2010
|
Balancing Selection Maintains a Form of ERAP2 that Undergoes Nonsense-Mediated Decay and Affects Antigen Presentation
|
The onset of protective immunity against pathogenic SIV challenge in SIVΔnef-vaccinated macaques is delayed for 15-20 weeks , a process that is related to qualitative changes in CD8+ T cell responses induced by SIVΔnef . As a novel approach to characterize cell differentiation following vaccination , we used multi-target qPCR to measure transcription factor expression in naïve and memory subsets of CD8++ T cells , and in SIV-specific CD8+ T cells obtained from SIVΔnef-vaccinated or wild type SIVmac239-infected macaques . Unsupervised clustering of expression profiles organized naïve and memory CD8+ T cells into groups concordant with cell surface phenotype . Transcription factor expression patterns in SIV-specific CD8+ T cells in SIVΔnef-vaccinated animals were distinct from those observed in purified CD8+ T cell subsets obtained from naïve animals , and were intermediate to expression profiles of purified central memory and effector memory T cells . Expression of transcription factors elicited by SIVΔnef vaccination also varied over time: cells obtained at later time points , temporally associated with greater protection , appeared more central-memory like than cells obtained at earlier time points , which appeared more effector memory-like . Expression of transcription factors associated with effector differentiation , such as ID2 and RUNX3 , were decreased over time , while expression of transcription factors associated with quiescence or memory differentiation , such as TCF7 , BCOR and EOMES , increased . CD8+ T cells specific for a more conserved epitope expressed higher levels of TBX21 and BATF , and appeared more effector-like than cells specific for an escaped epitope , consistent with continued activation by replicating vaccine virus . These data suggest transcription factor expression profiling is a novel method that can provide additional data complementary to the analysis of memory cell differentiation based on classical phenotypic markers . Additionally , these data support the hypothesis that ongoing stimulation by SIVΔnef promotes a distinct protective balance of CD8+ T cell differentiation and activation states .
Vaccination of rhesus macaques with SIVΔnef can induce robust immune responses and can protect the majority of vaccinated animals from challenge with wild-type SIV virus strains [1–3] . To date , SIVΔnef is the most efficacious of all vaccine strategies analyzed in the macaque model . Although safety concerns preclude the use of attenuated HIV as a human vaccine [4 , 5] , understanding the biological basis for immune protection conferred by SIVΔnef may provide information important for the design of safe and efficacious HIV vaccines . Therefore , substantial efforts have been made to identify correlates of immune protection induced by SIVΔnef over the last two decades . Correlates of immune protection induced by SIVΔnef and related attenuated SIV vaccines identified to date include both cellular and humoral adaptive immune responses [3 , 6–10] . CD8+ T cell responses in particular appear to be critical for SIVΔnef-mediated protection . SIVΔnef can induce robust CD8+ CTL responses and protection can occur in the absence of neutralizing antibody responses [3 , 7] . Additionally , CD8+ cell depletion following vaccination with attenuated SIV vaccines results in impaired control of challenge virus [6 , 11] . Although the vaccine virus is rapidly cleared to levels in plasma that are at or below detection following vaccination in the majority of animals [1] , virus continues to replicate at low levels [12 , 13] . Substantial evidence suggests that the replicative capacity of SIVΔnef , and the ability to provide persistent low-level antigenic stimulation , may mediate the high efficacy of protection [3 , 12–14] . A comparison of SIVΔnef with more-attenuated SIV virus strains found a positive correlation of the magnitude of lymph-node resident SIV-specific T cells to protection from intravenous challenge [13] . However , SIVΔnef does not induce greater numbers of SIV-specific CD8+ T cells than other vaccine approaches that do not induce protection [15–17] . Additionally , the frequency of SIVΔnef-induced SIV-specific CD8+ T cells located in lymphoid and genital tissues does not correlate with the maturation of protection in this model [12] . The frequency or magnitude , therefore , of virus-specific precursors in blood and tissues may be less important for immune control than the composition of CD8+ T cell memory phenotypes induced by SIVΔnef . Immune protection takes approximately 15–20 weeks to develop following vaccination [18] and during that time , SIV-specific T cells acquire a more central memory-like phenotype but maintain elevated PD-1 expression [14] . Persistent expression of PD-1 on SIV-specific CD8+ T cells requires ongoing low-level viral replication , as PD-1 expression is down regulated on cells specific for an epitope that undergoes escape [14] . These data , in conjunction with evidence of viral evolution following vaccination [1 , 19–21] and evidence that ongoing replication is required for vaccine efficacy , suggest that continued stimulation from viral epitopes present due to ongoing low-level replication of vaccine virus may induce a unique differentiation or activation state of SIV-specific CD8+ T cells . SIVΔnef may also promote a balance or distribution of central memory and effector cells , or a T cell repertoire different than that induced by less protective vaccines . A complete understanding of how SIVΔnef-induced CD8+ T cells mediate protection , and the relationship between CD8+ T cell differentiation stage and protective immunity remains unclear . Novel experimental methods that can provide additional information to what can be acquired with traditional approaches such as polychromatic flow cytometry may facilitate a more complete characterization of immune protection . In the past decade , substantial progress has been made in characterizing the differentiation of CD8+ effector and memory cell subsets following antigenic stimulation [22] . Genetic approaches have demonstrated the importance of a number of individual transcription factors in regulating differentiation . However , the combinatorial expression of lineage-specific and general transcription factors and their aggregation at cis-regulatory elements dictate the expression of any specific gene and ultimately the phenotype of the cell [23–25] . Recognition of the combinatorial nature of transcription factor function has motivated a more holistic approach to understanding transcription factor function and prompted a number of comprehensive systems based descriptions of differential transcription factor usage and networks of transcriptional control in different tissues and cell lineages including the hematopoietic system [23 , 24 , 26 , 27] . To expand on the current capacity for cell phenotypic and functional analyses provided by methods such as polychromatic flow cytometry , we developed a novel approach that exploits the fundamental regulation of cell phenotype and function by combinatorial transcription factor activity . We reasoned that simultaneous expression profiling of multiple transcription factors known to regulate cell differentiation could facilitate discrimination of cell lineage and provide novel information complementary to other methods . To assess the utility of transcription factor expression profiling for the characterization of CD8+ T cell differentiation , we measured the expression of a panel of transcription factors known to regulate T cell differentiation in sorted bulk populations of naïve and memory CD8+ T cells and in SIV-specific cells induced by SIVΔnef vaccination . Subsequent organization of samples by unsupervised clustering of expression data indicates that transcription factor expression profiling is a sensitive method that can clearly identify cells at different stages of CD8+ T cell differentiation . We subsequently applied this method to further characterize the differentiation of CD8+ T cells induced by SIVΔnef , and to characterize the phenotype of CD8+ T cells temporally associated with either protective , or non-protective immune responses . Our data demonstrate the utility of transcription factor expression profiling to characterize the differentiation of CD8+ T cells following SIVΔnef vaccination , and indicate that SIV-specific CD8+ T cells appear to be transcriptionally intermediate to , yet clearly distinct from , purified effector memory and central memory T cells isolated from vaccine-naïve animals . Taken together , our results support the conclusion that ongoing activation of CD8+ T cells by replicating vaccine virus may induce populations of CD8+ T cells possessing phenotypic characteristics distinct from , but with similarities to , classically defined effector memory and central memory cells .
To examine whether expression profiling of multiple transcription factors would facilitate discrimination of different CD8+ T cell differentiation states , we initially selected a panel of 18 transcription factors to analyze ( Table 1 ) based on published data indicating their involvement in the regulation of CD8+ T cell differentiation or function . A subset of these transcription factors , such as T-bet , Eomes , Blimp-1 and Id2 are well-characterized primary regulators of CD8+ memory or effector cell differentiation [28–36] . Another set of transcription factors , including BATF , Runx3 and BCL11b , regulate expression of the transcription factors noted above that serve as regulators of T cell function [37–43] . A third group , the Wnt signaling pathway effector transcription factors Lef-1 and Tcf-7 ( also known as Tcf-1 ) , are positive regulators of quiescence [44 , 45] . A fourth set of transcription factors , Rorα , Rorγt and GATA3 , regulate differentiation of additional T cell lineages and CD8+ T cell activation and effector function [46–49] . To determine if transcription factor expression profiling can be used to identify distinct stages of CD8+ T cell differentiation , we initially sorted highly purified populations of naïve , central memory , transitional memory , and effector memory CD8+ T cells from five healthy unvaccinated uninfected rhesus macaques based on differential surface expression of CD28 , CD95 and CCR7 ( S1 Fig . , Fig . 1 ) . We then measured the expression of the transcription factors included in our panel by multi-target qPCR , and used agglomerative unsupervised hierarchical clustering of the expression data to organize the samples . The sample dendrogram ( Fig . 1 ) demonstrates clear segregation of samples by cell differentiation stage , with the three memory CD8+ T cell subsets segregating from naïve cells . Distinct sets of transcription factors displayed unique expression profiles among cell subsets ( Fig . 2 ) . The Wnt pathway effectors LEF1 and TCF were expressed at the highest levels in naïve and central memory cells , and lower levels in transitional and effector memory cells . The transcription factors TBX21 , PRDM1 and NFIL3 , were expressed at the highest levels in effector memory cells . In contrast , EOMES , AHR and RORC , were expressed at the highest level in transitional memory cells . All of the transcription factors except IRF4 and BCL11B were expressed differentially among the CD8+ T cell subsets ( p≤0 . 001 ) . The differences in expression levels varied widely among transcription factors with some transcription factors demonstrating up to 1000-fold differences in mean expression level between sorted cell populations . Unsupervised clustering of samples by differentiation stage demonstrates that expression profiling of transcription factors is a sensitive method that can be used to clearly resolve distinct stages of memory CD8+ T cell differentiation . Longitudinal studies suggest that vaccine-induced protection to pathogenic virus challenge matures during the weeks following vaccination [2 , 11 , 18 , 50] . Animals challenged at 15 to 20 weeks following vaccination are better protected than animals challenged at five weeks following vaccination . As transcription factor expression profiling was able to differentiate between sorted naïve and memory T cell subsets , we sought to use this approach to identify differences in transcription factor usage in SIV-specific CD8+ T cells isolated at time points following SIVΔnef vaccination associated with either lesser or greater protection , and to further characterize the phenotype of these cells by comparing their transcription factor expression profiles with the profiles of sorted naïve and memory CD8+ T cell subsets . We analyzed CD8+ T cells specific for either of two Mamu-A*01-restricted immunodominant SIV epitopes differing in their propensity for immune escape . The Gag CM9 epitope is typically conserved over time [51] , whereas the Tat SL8 epitope mutates rapidly following infection in response to immune pressure , beginning to accumulate sequence heterogeneity at two weeks post infection [52 , 53] . We hypothesized that the distinct escape kinetics and resulting sensitivities to ongoing antigenic stimulation would induce differences in differentiation stage resolvable by transcription factor expression profiling . We sorted Gag CM9- and Tat SL8- specific CD8+ T cells obtained from four rhesus macaques at either 5 weeks or 20 weeks following SIVΔnef vaccination , and measured the expression levels of the transcription factors in our target panel by multi-target qPCR . To integrate the expression profiles of the SIV-specific cells with the sorted CD8+ subsets , we applied principal component analysis ( PCA ) to the combined data sets . Plotting principal components 1 vs , 2 , and principal components 2 vs . 3 , ( PC1 , PC2 , PC3; Fig . 3A , S1 Video ) segregated the data into distinct clusters . The data points representing the sorted CD8+ T cells occupy the periphery of the PC1 vs . PC2 plot , and segregate into separate clusters based upon cell differentiation stage . The naïve cells segregate from the memory cells along the PC1 axis , whereas the memory cells segregate along the PC2 axis , with the transitional memory cells positioned intermediately between the central and effector cells . The PC1 and PC2 loading factors ( Fig . 3B ) indicate that in this analysis , differential expression of LEF1 , TCF7 , PRDM1 and TBX21 strongly influence segregation of naïve from memory cells , whereas differential expression of ID2 , RUNX3 , AHR and LEF1 strongly influence segregation of memory cell subsets . The SIV-specific CD8+ T cells cluster with the sorted memory cells on the PC1 axis , and are positioned intermediately between central memory and effector memory cells on the PC2 axis . This intermediate position on the PC2 axis in part reflects the combined expression profiles of different memory subsets present in the SIV-specific cell samples . However , the SIV-specific samples significantly segregate from any sorted memory subset , particularly on the PC3 axis ( p<0 . 001 ) , indicating that the transcription factor expression profiles of the SIV-specific cells are distinct from the sorted subsets and are not solely comprised of proportions of memory subsets . The PC3 loading factors ( Fig . 3B ) indicate that in this analysis , the differential expression of NFIL3 , IRF4 , LEF1 and EOMES influence segregation of SIV-specific cells from the sorted naïve and memory subsets . The SIV-specific cells form two clusters , generally organized by week post-infection . The week 5 and week 20 post-vaccination samples occupy significantly different positions in PCA space ( p<0 . 01 ) . The week 5 post-vaccination cells , temporally associated with less protection to challenge , have greater PC2 values , indicating a more effector-like profile , whereas the week 20 post-vaccination cells have lesser PC2 values indicating a more central-memory like profile . The samples also significantly segregate based on epitope specificity ( p<0 . 01 ) . The Gag CM9-specific cells have overall higher PC2 values indicating a more effector-like phenotype , whereas the Tat SL8-specific cells have lower PC2 values indicating a more central memory-like phenotype . The changes in expression profiles from week 5 to week 20 are consistent with SIV-specific CD8+ T cells becoming overall more central memory-like and less effector-like over time following vaccination . Similarly , the differences seen between Gag CM9- and Tat SL8-specific cells are consistent with the kinetics of epitope escape and likely reflect the loss of antigen stimulation of the Tat-specific cells versus the ongoing stimulation of the Gag-specific cells . To further validate the approach of using transcription factor expression profiling to characterize cell differentiation , we compared this method to conventional flow-cytometric assessment of CD8+ T cell memory subsets present in SIV-specific populations at different time points following vaccination . In agreement with our expression profiling methods , flow cytometric methods based on differential expression of CCR7 and CD28 showed an overall increase in CM cells and a decrease in EM cells from week 5 to week 20 post-vaccination . Furthermore , Tat SL8-specific cells had greater frequencies of CM cells and lesser frequencies of EM cells than Gag CM9-specific cells ( S2A Fig . ) . Additionally , the ratio of EM to CM cells found in a sample of SIV-specific cells positively correlates with the PC2 value of the combined PCA analysis ( S2B Fig . ) . To provide additional context for interpreting SIV-specific CD8+ T cell expression profiles , and to further examine the effect of ongoing viral replication on transcription factor expression profiles , we used PCA to compare vaccine-specific and sorted naïve and memory subsets to SIV-specific CD8+ T cells collected at week 20 following infection with pathogenic wild-type SIV . A plot of principal components 1 and 2 ( Fig . 4 ) positioned Gag CM9-specific cells obtained from wild-type SIV-infected animals near the sorted effector memory cells but with higher PC2 values . Tat SL8-specific cells from wild-type SIV-infected animals were more heterogeneous but generally occupied positions more positive along the PC2 axis than SIVΔnef-induced cells , with PC2 values similar to sorted effector memory cells . These results are consistent with the idea that ongoing activation by replicating virus may induce a more effector-like phenotype , and that epitope escape facilitates a more central memory-like phenotype . A number of individual transcription factors were significantly differentially expressed between week 5 post-vaccination and week 20 post-vaccination ( Fig . 5 ) . For example BCOR , EOMES and TCF7 were expressed at significantly higher levels at week 20 post-vaccination . Higher expression of these transcription factors is consistent with a more quiescent and central memory-like phenotype [44 , 45 , 54] . Conversely , ID2 , RORA , NFIL3 and RUNX3 were expressed at significantly lower levels at week 20 post-vaccination . Lower expression of ID2 and RUNX3 is also consistent with a more central memory-like phenotype [35 , 40] . Elevated TBX21 expression , which is associated with effector differentiation , however , was maintained at week 20 , consistent with continued effector function [28 , 29] . A number of transcription factors were also significantly differentially expressed between Gag CM9- and Tat SL8- specific cells . Gag-specific CD8+ T cells expressed significantly higher levels of BATF , TBX21 and RORA . These differential expression profiles are consistent with Gag CM9-specific cells maintaining a more effector-like phenotype than Tat SL8-specific cells [28 , 37 , 46] . Interestingly , EOMES was expressed at significantly lower levels in Gag CM9-specific cells than in Tat SL8-specific cells at week 5 post-vaccination , and a trend towards higher expression was observed in Gag CM9-specific cells than in Tat SL8-specific cells at week 20 post-vaccination . Conversely , ID2 is expressed at significantly higher levels at week 5 in Gag-specific cells and more similar to Tat-specific cells at week 20 . These trends are consistent with Tat-specific cells being more central memory-like even at the earlier time point [31 , 35] . Many of the transcription factors were differentially expressed between SIV-specific CD8+ T cells in wild-type SIV- and SIVΔnef-infected animals at week 20 post-vaccination , likely reflecting the higher viral loads and attendant greater stimulation of SIV-specific cells in animals infected with wild-type SIV . EOMES , ID2 and RUNX3 were expressed at significantly higher levels in cells from wild-type SIV-infected animals ( p≤ 0 . 05 ) . In contrast , AHR and LEF1 were expressed at significantly lower levels in wild-type SIV-specific cells . A number of transcription factors were differentially expressed between Gag- and Tat-specific cells in wild-type SIV-infected animals . In particular , the Wnt pathway effectors TCF7 and LEF1 , as well as BCL6 , had significantly higher expression in Tat-specific cells . In contrast , EOMES , RUNX3 and IRF4 demonstrated trends towards higher expression in Gag-specific cells . These differences are consistent with the different kinetics of epitope escape , and consistent with loss of antigen stimulation mediating cell differentiation towards a less activated or more central memory phenotype [11 , 12 , 44 , 55 , 56] . Overall , the differences we observed in transcription factor expression profiles suggest that the process of CD8+ T cell differentiation following SIVΔnef vaccination involves the coordinate regulation of multiple transcription factors . At later time points following vaccination , SIV-specific cells express higher levels of transcription factors associated with memory differentiation , such as EOMES and TCF7 , down-regulate transcription factors associated with effector responses such as ID2 and RUNX3 , yet maintain elevated levels of TBX21 . To facilitate comparison of expression levels of individual transcription factors in the different populations of T cells , we also generated a PCA heatmap ( Fig . 6 ) of each transcription factor by overlaying expression values as heatmap colors on a plot of principal components 1 and 2 . The PCA heatmaps demonstrate that expression of PRDM1 and TBX21 in SIV-specific cells is similar to central memory or transitional memory cells . In contrast , expression of EOMES , TCF7 , GATA3 and BCL6 in SIV-specific cells is more similar to effector memory cells . Expression of AHR , ID2 , RUNX3 and LEF1 in SIV-specific cells is intermediate between central memory and effector memory cells . Interestingly , expression of the transcription factors BATF , BCOR , RORA , NFIL3 , IRF4 and PBX3 is greater in SIV-specific cells than any sorted subset . The higher expression levels of these transcription factors in SIV-specific cells provides additional evidence that the SIV-specific cells are transcriptionally distinct from any purified memory subset , or proportion of subsets . Although statistically significant for only PBX3 ( p<0 . 05 ) these trends of increased expression may , in part , reflect ongoing stimulation from replicating virus .
Memory T cells display substantial heterogeneity in phenotype , function and anatomic distribution [57] . The characterization of memory cell differentiation and the definition of phenotypic memory cell subpopulations has traditionally employed flow cytometric analyses of a subset of cell surface proteins , which regulate cell activation , survival and tissue-homing [58–60] . Over the past decade , the characterization of molecular mechanisms regulating memory differentiation has also identified key transcription factors that modulate the gene expression profiles in differentiating cells and cell subpopulations [22] . As transcription factors fundamentally regulate cell phenotype and function , global analyses of differential transcription factor usage accompanying cell differentiation can conceptually both identify novel subpopulations of cells not resolvable by standard flow cytometric techniques and provide novel insights into the process of cell differentiation and the function of cell subpopulations . We validated this approach by initially characterizing the transcription factor expression profiles of CD8+ T cells isolated at different stages of CD8+ T cell differentiation , and we subsequently used these data to characterize CD8+ T cells induced by SIVΔnef vaccination and wild-type SIV infection . Clustering analyses of transcription factor expression data verified that CD8+ T cells purified by traditional flow cytometric gating strategies display distinct transcription factor expression profiles . This strict segregation by differentiation state , of cells from all animals is striking , given that rhesus macaques are outbred animals and would be expected to display substantial transcriptional heterogeneity between individuals . The three memory subsets had transcription factor expression profiles more similar to each other than to naive cells , and the transitional memory cells appeared more similar to central memory cells than to effector memory cells . The expression profiles across subsets corroborates , for many of the transcription factors , patterns of expression that have been reported for single transcription factors or small subsets of transcription factors , although comprehensive analyses of expression of multiple transcription factors have not previously been undertaken . TBX21 , PRDM1 , RUNX3 and ID2 , for example , were expressed at higher levels in sorted effector memory cells , whereas the Wnt pathway effectors LEF1 and TCF7 were expressed at higher levels in the more quiescent naive and central memory cells . The high ratio of TBX21 to EOMES observed in effector memory cells , and similarly , the high ratio of PRDM1 to BCL6 observed in transitional memory and effector memory cells is consistent with the promotion of an effector phenotype [61] . Given the caveat that differences in transcription do not always directly correlate with differences in protein expression or function , overall , these results validate the approach of using transcription factor expression profiling to define the memory differentiation state of CD8+ T cells . We subsequently used transcription factor expression profiling to characterize changes in SIV-specific CD8+ T cells over time as immune protection to challenge matures following vaccination . Half of the transcription factors assessed had significantly different ( p≤0 . 05 ) expression levels at week 20 than at week 5 post-vaccination , and segregation by PCA suggested that SIV-specific cells had substantially different expression profiles at the two time points . SIV-specific CD8+ T cells in SIVΔnef-vaccinated animals at week 20 displayed a transcriptional signature characteristic of ( but not identical to ) central memory cells , as manifested by elevated levels of TCF7 and EOMES and decreased expression of ID2 and RUNX3 . Importantly , because transcription was assessed in bulk populations of cells , differences between levels of expression , and location in PCA space reflect both differences between expression levels on a per-cell basis , and differences in proportions of subsets present in the sample . Thus , the more central memory-like expression profile displayed at week 20 post-vaccination likely reflects the greater proportion of central memory-like cells present at week 20 post-vaccination , in agreement with our conventional flow cytometric analyses , but may also reflect a more central-memory like profile in SIV-specific cells overall . A number of transcription factors were also differentially expressed between cells with different epitope specificities and immune escape kinetics . For example , TBX21 and BATF were expressed at higher levels in Gag CM9-specific cells than in Tat SL8-specific cells . Since BATF is upregulated downstream of PD-1 signaling [38] , this observation is consistent with the higher levels of PD-1 expressed on Gag CM9-specific cells than in Tat SL8-specific cells [14] . The role of PD-1 in promoting an exhausted T cell phenotype in the setting of chronic viral infection has been widely described [62 , 63] . However , the expression of PD-1 is not an indication in itself of an exhausted phenotype and more accurately reflects cell activation [13 , 14 , 64] . Overall , the Tat SL8-specific cells appeared less activated and more memory-like than the Gag CM9-specific cells . Although differences in expression profiles between Gag CM9- and Tat SL8-specific cells are likely to be influenced by a variety of variables , the differences we observed are consistent with the loss of antigenic stimulation due to the evolution of escape in the Tat SL8 epitope , and on-going stimulation of Gag CM9-specific cells . These results are also consistent with prior work demonstrating a decline in the frequency of activated Tat SL8-specific cells , but not Gag CM9-specific cells in genital tissue from 5 to 20 weeks post-vaccination with SIVΔnef [12] . SIV-specific cells from wild-type SIV-infected animals displayed a pattern of expression of transcription factors more characteristic of effector memory cells than SIV-specific cells from SIVΔnef-vaccinated animals . Gag CM9-specific cells more closely resembled effector memory cells in expression of transcription factors than Tat SL8-specific cells , as predominantly reflected by their differential expression of LEF1 and TCF7 . Further , LEF1 was expressed at overall lower levels in cells from wild-type-infected animals than SIVΔnef-vaccinated animals , mainly due to very low expression in the Gag CM9-specific cells . This difference likely reflects the much higher levels of antigen stimulation in wild-type SIV-infected animals . BATF was also expressed at lower levels in cells from wild-type-infected animals than in cells from SIVΔnef-vaccinated animals . Since BATF has recently been shown not only to promote effector differentiation , but also to restrain the expression of the effector molecules IFNγ and granzyme B [39] , the reduced expression observed in wild-type-infected animals may reflect reduced inhibition of effector molecule expression in the presence of high viral loads . Overall , these results are consistent with previous studies suggesting virus-specific cells continue to be activated by replicating virus , and that CTL escape is associated with reduced cell activation and central memory differentiation [3 , 12–14] . Studies in macaques using the live-attenuated SHIV89 . 6 vaccine suggest that protection against vaginal challenge is associated with the presence of SIV-specific CD8+ T cells in the female reproductive tract that possess both cytolytic function and some proliferative capacity [65 , 66] . Earlier studies showed that protection was associated with a higher ratio of central memory to effector memory CD8+ T cells in blood and lymph nodes [67] . CD8+ T cells from protected animals also showed higher pre-challenge measures of survival and lower apoptotic potential . In contrast to the effector memory cells found in the female reproductive tract , IL-2 secreting SIV-specific CD8+ T cells were found in lymph nodes . Overall this suggests a model whereby in protected animals , SHIV89 . 6 induces central memory CD8+ T cells that continually supply effector cells to the genital mucosa in response to persistent antigenic stimulation by replicating virus . That unprotected animals have a higher ratio of effector memory to central memory cells in blood and lymph nodes suggests that lack of protection may be associated with heightened systemic T cell activation and resultant apoptosis and exhaustion . In the setting of spontaneously controlled HIV infection , overwhelming evidence suggests CD8+ T cell activity is critical for viral suppression . However there has been substantial heterogeneity reported in ex vivo measures of CD8+ T cell function in controllers . Studies have variously shown associations between control of viremia and CD8+ T cell polyfunctionality , HIV-specific CD8+ T cell frequency , virus suppressive capacity , or proliferative capacity [68–72] . A more recent study [73] examined individuals who control viremia to very low levels in the absence of ex vivo CD8+ T cell responses ( weak responders ) , and found that these subjects maintain an HIV-specific population of central memory CD8+ T cells capable of suppressing HIV ex vivo . An additional study [74] showed that spontaneous protection from HIV in controllers correlates with CD8+ T cell memory-type responses , prolonged cytokine secretion , and cell proliferation . In the setting of elite control and very low viremia , HIV-specific T cells may receive less antigenic stimulation facilitating differentiation towards a more central memory phenotype . Our data demonstrate the utility of using transcription factor expression profiling to characterize the differentiation of CD8+ T cells following vaccination with SIVΔnef . Using this approach we demonstrate that SIV-specific cells isolated from vaccinated animals at time points associated with greater immune protection display a distinct pattern of expression of transcription factors that represent the presence of different proportions of CD8+ T cell memory subsets , or different levels of activation or differentiation states . The higher expression levels of a number of transcription factors in SIV-specific cells than in any purified memory subset suggests that continued activation of subsets of virus-specific cells by low-level replicating virus induces transcriptionally distinct populations of CD8+ T cells which may have characteristics of both central memory and effector memory cells .
The 14 female Indian-derived rhesus macaque monkeys ( Macaca mulatta ) described in this study were housed at the New England Primate Research Center ( NEPRC ) in accordance with the regulations of the American Association of Accreditation of Laboratory Animal Care and the standards of the Association for Assessment and Accreditation of Laboratory Animal Care International . All protocols and procedures were approved by the relevant Institutional Animal Care and Use Committee , which was the Harvard Medical Area ( HMA ) Standing Committee on Animals at Harvard Medical School . All animals were housed indoors in an SOP-driven , AAALAC-accredited facility . Husbandry and care met the guidance of the Animal Welfare Regulations , OLAW reporting and the standards set forth in The Guide for the Care and Use of Laboratory Animals . All research animals were enrolled in the NEPRC behavioral management program , including an IACUC-approved plan for Environmental Enrichment for research primates . This program included regular behavioral assessments , and provision of species appropriate manipulanda , and foraging opportunities . This protocol had an IACUC-approved exemption from social housing based on scientific justification . Primary enclosures consisted of stainless steel primate caging provided by a commercial vendor . Animal body weights and cage dimensions were regularly monitored . Overall dimensions of primary enclosures ( floor area and height ) met the specifications of The Guide for the Care and Use of Laboratory Animals , and the Animal Welfare Regulations ( AWR’s ) . Further , all primary enclosures were sanitized every 14 days at a minimum , in compliance with AWRs . Secondary enclosures ( room level ) met specifications of The Guide with respect to temperature , humidity , lighting and noise level . The animals were provided ad lib access to municipal source water , offered commercial monkey chow twice daily , and offered fresh produce a minimum of three times weekly . Light cycle was controlled at 12/12 hours daily . The animals were subject to twice daily documented observations by trained animal care and veterinary staff , and enrolled in the facility’s environmental enrichment , and preventative health care programs . Euthanasia took place at defined experimental endpoints using protocols consistent with the American Veterinary Medical Association ( AVMA ) guidelines . Animals were first sedated with intramuscular ketamine hydrochloride ( 20 mg/kg ) followed by sodium pentobarbital ( ≥100 mg/kg ) intravenously to achieve euthanasia . Peripheral blood samples were collected from unvaccinated healthy rhesus macaques ( n = 5 ) for purification of naïve and memory CD8+ T cell subsets , or Mamu-A*01+ SIVΔnef-vaccinated animals ( n = 4 ) at week 5 and week 20 post-vaccination , or Mamu-A*01+ wild-type SIV-infected animals ( n = 5 ) at week 20 post-infection , for purification of SIV-specific cells . Blood was collected in EDTA vacutainer tubes ( Becton Dickinson Vacutainer systems , Franklin Lakes , NJ ) , and peripheral blood mononuclear cells ( PBMC ) were separated by density gradient centrifugation ( Lymphocyte Separation Medium; MP Biomedicals Inc . , Solon , OH ) at 1500 rpm for 45 minutes . PBMC from vaccinated or infected animals were cryopreserved , and subsequently thawed prior to cell sorting . PBMC from healthy uninfected animals were used immediately after separation . Total RNA copy number equivalents were determined in EDTA-treated plasma using a standardized quantitative real-time RT-PCR assay based on amplification of conserved gag sequences as described previously [75] . In wild-type SIV-infected animals , viral loads were between 22 , 000 and 1 , 900 , 000 copy equivalents/ml plasma at 20 weeks following infection . Naïve ( CD95−CCR7+ CD28+ ) , central memory ( CD95+ CCR7+ CD28+ ) , transitional memory ( CD95+ CCR7−CD28+ ) and effector memory ( CD95+ CCR7− CD28− ) CD8+ T cell subsets were sorted from PBMC from uninfected animals . SIV-specific CD8+ T cells were sorted from previously cryopreserved PBMC from SIVmac239Δnef-vaccinated or SIVmac239-infected animals . SIV-specific CD8+ T cells from Mamu-A*01+ or Mamu-A*02+ animals were identified using APC- or PE- conjugated Mamu-A*01 or A*02 MHC class I tetramers or pentamers ( Proimmune ) complexed with the cognate CTL epitope . A*01 Gag181–189CM9 [76] and A*01 Tat28–35SL8 [77 , 78] tetramers were kindly provided by Nancy Wilson and David Watkins ( Wisconsin National Primate Research Center , Madison , WI ) . To identify naïve and memory phenotypes , PBMC were stained with CD3 ( SP34 ) FITC or Pacific Blue; CD4 ( L200 ) PerCP-Cy5 . 5; CD8 ( RPA-T8 ) Alexa 700; CD28 ( 28 . 2 ) ECD ( Beckman Coulter ) , CD95 ( DX2 ) APC , CCR7 ( 150503 ) PE ( R&D Systems ) . Antibodies were obtained from BD Pharmingen unless specified . PBMCs ( 1–2 × 106 ) were initially labeled with LIVE/DEAD viability stain ( Life Technologies ) and washed; incubated with CCR7 antibody for 15 min at 37C; incubated with tetramers or pentamers for 10 min at RT and washed; then incubated with all other antibodies for 20 min at RT and washed prior to sorting . Cell sorting was performed on a FACS Aria II cell sorter ( BD Biosciences ) . Sorts were > 99% pure for all populations , and cell yields generally ranged between 103 and 105 cells . Total RNA was isolated using RNeasy Plus Micro Kit ( 74034 , Qiagen ) and quantified from CD8+ T cells that were FACS purified from PBMC . cDNA was synthesized using the High-Capacity Reverse Transcription Kit with RNase Inhibitor ( 4374966 , Life Technologies ) . The resulting cDNA ( 1ng equivalent input ) per reaction was subjected to 18 cycles of preamplification using the ABI Preamp Master Mix kit and pooled TaqMan assays ( S1 Table ) ( Applied Biosystems , Life Technologies ) . Preamplified cDNAs were diluted 5-fold with 1×TE and loaded on 96x96 Fluidigm BioMark dynamic arrays ( Fluidigm ) along with the selected real-time PCR assays . All possible combinations of samples and assays on the BioMark dynamic array chip were mixed using the Fluidigm ( IFC ) integrated fluidic circuit controller . The Fluidigm Biomark System was used for real time PCR amplification and data collection , using 40 cycles of amplification with real-time monitoring of FAM fluorescence in each well . Initial calculations of cycle thresholds ( Ct ) were performed using the Fluidigm BioMark software and further analysis was carried out using GenEx software ( MultiD Analyses , URL: http://www . multid . se ) . Offscale-low expression values were set to maximum onscale Ct+1 for each target transcript . Five endogenous control genes were included in each Fluidigm run and the stability of endogenous control genes across all experimental samples was analyzed using the NormFinder algorithm in GenEx . The mean expression of the two most stable endogenous control genes ( PGK1 and TBP ) was used for normalization . Relative expression ( 2-Δct ) values were log2 transformed for subsequent analyses ( S1 Dataset ) . Unsupervised agglomerative hierarchical clustering was performed on transcript mean-centered expression values using the Euclidean distance metric and complete linkage clustering method . Principal component analysis was performed using un-scaled expression values . PCA heatmap expression values were normalized by scaling expression values to the range of each transcript . RORC was excluded from PCA analyses because RORC was expressed at or below the limit of detection in the majority of SIV-specific cells , and chip-to-chip differences in the values assigned to offscale-low reactions introduced artifacts of apparent differential expression that confounded PCA plot structure . Clustering analyses were performed using R [79] , and the functions prcomp {stats} , and hclust {stats} . In addition to base R the following R packages were used: RColorBrewer [80] , Plotrix [81] , gplots [82] , lme4 [83] . Statistical analyses were performed using both Stata software ( StataCorp . 2013 . Stata Statistical Software: Release 13 . College Station , TX: StataCorp LP ) and R [79] . Differences in transcription factor expression between sorted naïve and memory subsets , and differences between sorted subsets and SIV-specific cells were assessed by one-way ANOVA . Differences in principal component plot positions of SIV-specific cells and sorted naïve and memory cell subsets were assessed by unpaired Student’s t-test of PC3 values . Differences in principal component plot positions of week 5 and week 20 post-vaccination samples and of Gag CM9- and Tat SL8-specific samples were assessed by mixed effects linear regression modeling of PC1 values of a principal component analysis of SIV-specific cells . Differences between individual transcription factor expression values at week 5 and week 20 post-vaccination or infection , or between Gag CM9- and Tat SL8-specific cells , were assessed using mixed effects linear regression models . Differences between individual transcription factor expression values in cells isolated at 20 weeks post SIVΔnef vaccination and wild-type SIV infection were assessed by unpaired Student’s t-test . Differences in frequencies of CD8+ T cell memory subsets in Gag CM9- and Tat SL8-specific cells were assessed by unpaired Student’s t-test ( week 5 vs . week 20 ) or paired Student’s t-test ( Gag vs . Tat . ) AHR 714254 BATF 702646 BCL6 708736 BCL11B 705238 BCOR 698644 PRDM1 696757 EOMES 704711 ID2 693394 RORC 717052 RORA 704014 PBX3 711691 NFIL3 704757 IRF4 722883 RUNX3 719447 TBX21 694044 TCF7 710234 LEF1 695776 GATA3 713840
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The live attenuated vaccine SIVΔnef can induce robust CD8+ T cell- mediated protection against infection with pathogenic SIV in macaques . Thus , there is substantial interest in characterizing these immune responses to inform HIV vaccine design . Animals challenged at 15–20 weeks post vaccination exhibit robust protection , whereas animals challenged at 5 weeks post-vaccination manifest little protection . Since the frequency of SIV-specific T cells decreases from week 5 to week 20 , it is likely that the quality of the response to challenge changes as virus-specific cells differentiate . We applied a novel approach of transcription factor expression profiling to characterize the differences in SIV-specific cell function and phenotype at more protected and less protected time points . Using unsupervised clustering methods informed by expression profiles assessed in purified CD8+ T cell subsets , we show that SIV-specific cells display expression profiles different than any purified CD8+ T cell subset , and intermediate to sorted effector memory and central memory subsets . SIV-specific cells overall appear more effector memory-like at week 5 post-vaccination , and more central memory-like at week 20 post-vaccination . Distinct profiles of CD8+ T cells specific for different SIV epitopes having different immune escape kinetics suggests maturation is regulated by ongoing low-level replication of vaccine virus .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
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Characterization of CD8+ T Cell Differentiation following SIVΔnef Vaccination by Transcription Factor Expression Profiling
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A comprehensive systems-level understanding of developmental programs requires the mapping of the underlying gene regulatory networks . While significant progress has been made in mapping a few such networks , almost all gene regulatory networks underlying cell-fate specification remain unknown and their discovery is significantly hampered by the paucity of generalized , in vivo validated tools of target gene and functional enhancer discovery . We combined genetic transcriptome perturbations and comprehensive computational analyses to identify a large cohort of target genes of the proneural and tumor suppressor factor Atonal , which specifies the switch from undifferentiated pluripotent cells to R8 photoreceptor neurons during larval development . Extensive in vivo validations of the predicted targets for the proneural factor Atonal demonstrate a 50% success rate of bona fide targets . Furthermore we show that these enhancers are functionally conserved by cloning orthologous enhancers from Drosophila ananassae and D . virilis in D . melanogaster . Finally , to investigate cis-regulatory cross-talk between Ato and other retinal differentiation transcription factors ( TFs ) , we performed motif analyses and independent target predictions for Eyeless , Senseless , Suppressor of Hairless , Rough , and Glass . Our analyses show that cisTargetX identifies the correct motif from a set of coexpressed genes and accurately predicts target genes of individual TFs . The validated set of novel Ato targets exhibit functional enrichment of signaling molecules and a subset is predicted to be coregulated by other TFs within the retinal gene regulatory network .
The development of the structural and functional properties of cells is largely determined via differential extraction of information from the genome by transcription factors ( TFs ) . The first detailed analyses of TF-controlled genetic programs have recently been performed in yeast [1] , [2] and in early embryonic development of sea squirt [3] , sea urchin [4] , and fruitfly [5] , [6] . These initial studies revealed an astonishing complexity of regulatory interactions , between TFs and their target genes in the genome . The expression of most genes is regulated by combinations rather than single TFs , and extensive cross-regulations exist amongst TFs , often through feed-forward and feedback loops . These characteristics make it necessary to represent the regulatory blueprint of a cell as a network , for which the emerging properties explain the complement of active genes in that cell . The mapping and characterization of these networks represents a major goal in developmental biology , as they will yield profound mechanistic insights into embryonic and postembryonic developmental programs . However , the elucidation of such networks remains a formidable task for the vast majority of biological processes in most organisms . Two main approaches are being used for gene regulatory network ( GRN ) mapping . The first approach relies on chromatin immunoprecipitation ( ChIP ) with an antibody against a particular TF , followed by hybridization on a chip ( ChIP-chip ) or next-generation sequencing ( ChIP-Seq ) to identify the regions bound by the TF . In yeast , a first draft of the entire regulatory network has been described [2] by ChIP-chip for every TF . Importantly , ChIP-chip data alone were not specific enough and required additional computational predictions of conserved TF binding sites in the bound regions . In Drosophila , ChIP-chip has been successful in identifying target genes for a few TFs , such as dorsal , Mef2 , twist , and biniou [6] , [7] . Here also ChIP-chip data alone were not specific enough , but combinations with computational binding-site predictions and with gene expression data under normal and TF perturbation conditions identified a significant number of bona fide regulatory interactions . The limitations of this approach are the large amounts of material required for ChIP ( hence so far only successful for yeast cultures and large embryo collections ) and the need for high quality , “ChIP-grade” antibodies . Therefore , it is not possible today to perform ChIP-Seq for most TFs at most developmental stages in multicellular organisms . The second approach is based on genetic perturbations of a TF , followed by quantitative measurements of expression level changes of downstream genes . Either a selected candidate gene set is measured by quantitative reverse transcription PCR ( qRT-PCR ) , or all genes are measured . In yeast , a complete functional network was uncovered by profiling transcriptional responses of individual deletions of all TFs [1] . In higher eukaryotes , perturbation of multiple TFs ( e . g . , morpholino knock-down ) followed by qRT-PCR or nanostring [8] , have lead to several networks by measuring quantitative changes in gene expression upon TF perturbation . Examples of this approach are the endomesoderm network in sea urchin [4]; the network underlying central nervous system compartmentalization in Ciona intestinalis [9] and the network underlying mouse T-cell specification [10] . These networks are more complete than the ChIP-based networks because they contain interactions ( i . e . , targets ) for many TFs . However , the limitations of this approach are ( 1 ) they are only applied as transient perturbations in early embryo's or in cell culture; and ( 2 ) these networks are based on expression changes and usually do not contain cis-regulatory evidence . In summary , while significant progress has been made in decoding regulatory interactions in cell culture models and early embryonic patterning , in vivo description of GRNs required during development remains a significant challenge for developmental and regulatory biology . In order to begin to tackle this challenge , we exploited the second approach , which does not require special molecular reagents , to predict target genes of TFs involved in a specific postembryonic process , namely specification of D . melanogaster adult sense organs , and then provide direct in vivo cis-regulatory evidence for these interactions . A genetic TF perturbation followed by sample dissection and a microarray experiment , which is in principle feasible for any cell type , yields sets of up- and downregulated genes as candidate target genes for that TF . Bioinformatics methods to discover over-represented motifs across such a set of coexpressed genes , such as Clover , oPOSSUM , PASTAA , or PSCAN , are limited to small sequence search spaces , such as proximal promoters , often ( except oPOSSUM ) work on single genomes , and do not incorporate motif clustering [11]–[14] . On the other hand , motif scanning approaches that incorporate motif clustering , such as Stubb , SWAN , or Cluster-Buster , do not take gene coexpression information and genomic background information ( i . e . , genes not differentially expressed by the TF perturbation ) into account [15]–[17] . Recently , methods that combine both approaches , namely motif over-representation and motif cluster scoring , like PhylCRM/Lever and ModuleMiner have been successfully used on yeast and human [18]–[20] . We developed a method , called cisTargetX , and applied it to Drosophila . cisTargetX produces high-confidence target predictions that result from statistical correlations between coexpressed gene sets and genome-wide target prioritizations on the basis of rankings of conserved motif cluster predictions . Unlike existing methods , cisTargetX allows identifying both the motif and the optimal subset of direct targets of the perturbed TF , and to dissect a set of coexpressed genes into subsets of targets of different TFs . Furthermore , its computational efficiency allows online usage by expert and nonexpert users through a Web-based application . The developmental system we use as a model is retinal differentiation in Drosophila . This system has served as a model for the analysis of postembryonic development and cell-fate specification , and extensive genetic studies have uncovered key TFs and signaling pathways that control this process [21] . During the initial steps of photoreceptor specification , competent neuroepithelial cells specified by eye determination TFs such as Eyeless/Pax6 ( Ey ) express the proneural TF Atonal ( Ato ) , leading to the specification of individual R8 photoreceptor precursor cells with a determined sensory fate . This process initiates a cascade of signaling events that result in the specification of all retinal cells . However , the regulatory interactions underlying this signaling cascade are unknown . Moreover , the fly retina is used as a cancer model [22] . Hence our endeavor to identify the regulatory environs of Ato may yield insight into the regulatory mechanisms underlying tumor suppression [23] . In order to determine the space of Ato downstream genes , we first generate microarray data using gain-of-function ( GOF ) and loss-of-function ( LOF ) genetic perturbation resulting in 451 Ato downstream genes . cisTargetX analysis of this set results in the prediction of 74 direct target genes . We then perform extensive in vivo enhancer-reporter validations for 39 predicted Ato enhancers and confirm 20 enhancers as bona fide Ato targets . Next , we apply cisTargetX to microarray data sets obtained under different conditions and for other TF perturbations , and dissect sets of coexpressed genes into direct targets of the TFs Ey , Senseless ( Sens ) , Suppressor of Hairless ( Su ( H ) ) , Rough ( Ro ) , and Glass ( gl ) . Drawing edges between TFs and their targets results in a transcriptional network underlying early retinal differentiation and defines the gene regulatory environs of Ato-dependent retinal differentiation . These data provide evidence for a generalized approach for the prediction and in vivo validation of postembryonic cell-fate specification GRNs .
We apply a methodology for target gene discovery that combines genome-wide motif cluster predictions with gene set enrichment analysis . The procedure consists of two steps , illustrated in Figure 1 and Text S1 for the Drosophila homologue of the nuclear factor-κB ( NFκB ) TF Dorsal ( dl ) as a positive control . The dl binding motif is available as a position weight matrix ( PWM ) ( Figure 1A ) , and many of its direct target genes are known [24]–[26] . Cluster-Buster [17] is used to predict clusters of dl binding sites across the 12 Drosophila genomes ( Figure 1B ) . 5 kb upstream regions and introns of all D . melanogaster genes are scored , as well as all their respective orthologous regions from the 11 other Drosophila species , as determined using liftover on the UCSC Genome Browser net alignments [27] . Each Dmel reference region k receives 12 Cluster-Buster scores ( Sk , i , for each species i ) and 12 corresponding ranks ( Rk , i , the rank position out of 93 , 330 regions ) . For each region , the 12 independent species ranks are integrated into one final rank ( Rk ) using order statistics [28] , [29] , followed by selecting the highest ranking region for each gene , ultimately producing a final ranking of all Dmel genes ( Figure 1C ) [28] . Next , the genomic ranks of a subset of genes are plotted in a cumulative recovery curve ( Figure 1D ) . For the dl example we use 80 coexpressed genes downstream of dl obtained from GOF and LOF Dorsal perturbations [26] . The observed recovery curve for these 80 genes ( blue curve in Figure 1D ) indicates that they are enriched in the top part of the motif-based gene ranking . This enrichment is higher when predictions are integrated across 12 genomes than for Dmel alone ( cyan curve in Figure 1D ) , and is statistically significant ( z score is 5 . 61 ) , as determined by comparing the area under the curve ( AUC ) to the AUCs under 1 , 980 control curves constructed for an entire motif library ( Table S1 ) . The recovery curve yields 13 predicted targets at the optimal cutoff , of which 12 are true dl targets [7] . An important additional feature of this approach is its use for the detection of enriched motifs in the regulatory sequences of predicted target genes . This use is because AUC calculations are performed for all 1 , 981 motifs , thus allowing motif discovery by selecting the motif ( s ) with the highest AUC . That motif will have the highest enrichment of coexpressed candidate genes among its top-scoring target predictions . For the 80 genes downstream of dl , the dl motif is identified as the best motif with the highest AUC ( Figure 1E; Table S2 ) , together with several variations of the NFκB motif . Other motifs with significant recovery curves are the motif for Tinman , a homeobox NK family TF , and an E-box motif possibly representing binding sites for the basic-Helix-Loop-Helix TFs Twist or Snail . In conclusion , the dl motif together with dl target genes can be identified through homotypic binding-site cluster predictions , even though dl binding sites are usually accompanied in the cis-regulatory module ( CRM ) by binding sites for Twist , Snail , or other TFs [7] . Interestingly , the cisTargetX performance using only the dl PWM is similar to the performance when [dl+twi] or [dl+twi+sna] heterotypic cluster predictions are used ( Figure S1 ) . Although some bona fide enhancers receive better rankings using multiple PWMs , increasing the specificity , other enhancers are filtered out ( namely those where dl works alone or cooperates with other TFs ) , decreasing the sensitivity . This balance of positive and negative effects of heterotypic versus homotypic models results in comparable recovery curves ( Figure S1 ) . Note that the cooperative regulation of target genes can be discovered through first discovering target genes for a single TF and then discovering overrepresented motifs of other TFs within the same target gene space . To further test the performance of our approach , we performed similar computational experiments for other TFs using various types of input gene sets , such as coexpressed gene clusters from microarray gene expression , in situ gene expression , or literature-curated gene expression data; coregulated genes from chromatin-binding experiments; and functionally related genes from Gene Ontology . We find significant recovery curves , and accordingly high-confidence predictions of target genes , for Mef2 , Cf2 , Pointed , Serpent , Biniou , Svb , Bcd , Kr , Cad , Hb , and several other TFs ( Table 1 and the cisTargetX Web site ) . Interestingly , these analyses yield a number of novel target gene predictions for these TFs , which are publicly available as an online resource for the community . Because cisTargetX uses a larger sequence space and a larger motif collection than other motif discovery methods ( e . g . , PASTAA , Clover , or PSCAN ) , and because it employs motif clustering and cross-species comparisons , cisTargetX identifies the correct motif with higher significance , and in more gene sets than other methods ( Text S2 ) . From these validation experiments we conclude that if a candidate input gene set—usually a set of coexpressed genes—contains a critical number of direct targets for a certain TF , then this procedure can identify the optimal motif for this TF together with the optimal subset of predicted direct target genes . The enhancer predictions that underlie the cisTargetX scores are also useful for identifying the actual enhancer regulating each target gene , although this step is more difficult to validate in silico because of limited data availability and because of the possible presence of redundant enhancers [7] . Therefore , validating these predictions requires in vivo testing of the putative enhancers . To unravel the GRN underlying sensory cell-fate specification , we turned to the Drosophila retina as a model system . The acquisition of neural cell fate in the retina is under the control of the proneural tumor suppressor TF Ato . Loss of Ato results in the complete failure of retinal differentiation [35] , and therefore Ato must occupy a key position in the regulatory hierarchy underlying retinal development . However , only four target genes are currently known for Ato , namely sens , dap , Brd , and mir-7 , yielding a poor explanation of the regulatory network underlying the complex process of Ato-dependent neural fate specification [30]–[33] . We therefore first focused on expanding the regulatory interactions directly downstream of Ato . To this end , we overexpressed Ato in the eye imaginal disc using two Gal4 drivers , namely GAL4-7 and AtoGAL4 , verified the downstream effects on known targets by qRT-PCR , and then measured gene expression changes by microarrays ( Methods , Figure S2 ) . This GOF experiment results in a set of 204 Ato downstream genes ( Methods , Table S3 ) , containing the positive controls sens and dap , and is furthermore enriched in relevant biological processes , such as nervous system development ( p = 4 . 1×10−9 ) , and cell-fate commitment ( p = 1 . 6×10−5 ) . Applying cisTargetX on this set of candidate genes identifies two kinds of motifs that produce highly significant recovery curves , namely E-box motifs and Su ( H ) motifs ( Figure 2; Table S4 ) . The best motif among the 1 , 981 motifs tested is the E-box motif RACASCTGY from the Stark et al . conserved motif collection [34] . This motif is slightly different from the previously reported Ato binding-site consensus sequence AWCAKGTGK but preserves the typical CANNTG core [31] . We also constructed our own Ato “phylo-PWM” [28] , on the basis of known Ato binding sites and conserved sites in other species ( Table S5 ) , which also yields a significant recovery curve ( z = 2 . 67 ) . The ROC curve for the RACASCTG motif is shown in Figure 2B . The AUC is significantly higher than expected by chance ( z = 3 . 86 ) and applying the optimal cut-off at position 674 results in 36 direct Ato target-gene predictions ( Tables 2 and S6 ) . At this position on the x-axis , the observed recovery ( y-axis; blue curve ) of Ato upregulated genes , versus the expected recovery ( y-axis; red curve ) , is most significant . We confirmed the specificity of the RACASCTGY motif for Ato by comparing cisTargetX results on Ato GOF data to control gene sets and to coexpressed genes enriched for specific targets of Scute , a related bHLH proneural factor . Scute downstream genes also have significant curves for several Su ( H ) and E-box motifs , but not for RACASCTGY . Using all significant E-boxes in the GOF Ato set ( Table S4 ) yields , in total , 55 direct Ato target gene predictions ( Tables 2 and S6 ) . Next we performed microarray experiments for ato−/− eye discs . Because loss of Atonal results in the complete loss of retinal differentiation [35] , more genes are found that change expression , mostly downregulated genes . The most significant motif in a set of 315 downregulated genes ( >3-fold downregulation ) is Su ( H ) , indicating that this TF is involved in many cell types throughout retinal differentiation . Not surprisingly , E-boxes are ranked lower than in the GOF analysis . RACASCTGY is nevertheless over-represented in the LOF set ( z = 2 . 41 ) , and yields 18 target gene predictions of which seven overlap with the GOF target predictions . Using all significant E-boxes ( Table S7 ) and adding the GOF predictions yields a total of 74 predicted Ato target genes ( Tables 2 , 3 , and S6 ) . Analysis of Gene Ontology over-representation among these 74 genes yields biological processes that are not over-represented among the initial 204 Ato-upregulated genes , such as eye development ( p = 1 . 1×10−6 ) and compound eye photoreceptor cell differentiation ( p = 0 . 0041 ) , indicating that the target predictions yield an enrichment towards the process under study . To determine if any of the predicted genes are direct targets of Atonal , we tested 39 predictions by an in vivo enhancer reporter assay using a vector we designed for this purpose ( Figure S3; Tables 3 , S8 , and S9 ) . Of these , three were already known Ato targets , namely dap , sens , and ato , and four others are previously known Scute targets namely siz , Traf4 , m4 , and E ( spl ) . The enhancers of dap , ato , and Traf4 were recloned in our vector , while for sens , siz , m4 , and E ( spl ) we used the published lines [36] , [37] . For the new enhancers , we selected genomic fragments that encompass high-scoring clusters of Ato binding sites , and we manually extended the fragments on both sides retaining flanking sequence with high phastCons [38] conservation scores across 12 Drosophila genomes , to prevent potentially fragmenting an enhancer . Most genes have multiple motif clusters , though here we selected only one per gene , usually the highest scoring region for our Ato PWM . Fragments ranging in size from 300 bp to 3 , 300 bp were cloned upstream of the Hsp70 minimal promoter driving nuclear green fluorescent protein ( GFP ) , and inserted into predefined genomic positions via ΦC31-mediated transgenesis [39]–[41] . The vector was tested using the previously known ato femoral chordotonal organ auto-regulatory enhancer [42] and the dap eye enhancer ( Figure S3 ) [30] . In total , 20 enhancers produce reporter GFP expression in Ato-dependent photoreceptor precursor cells in the eye imaginal disc , or in the Ato-dependent chordotonal sensory organ precursors ( SOPs ) , in wild-type animals ( Figures 3 and S4; Table 2 ) . These include the three previously known targets sens , dap , and ato; and 17 new Ato targets: Fas2 , CG30492 , CG1626 , Dscam , Pde8 , sca , Rapgap1 , Spn , CG8965 , nmo , spdo , phyl , Traf4 , m4 , E ( spl ) , siz , and neur . Thus , we achieved a 51% target-gene discovery success rate , even though we tested only one candidate region per gene . We note at least two caveats in these enhancer reporter assays . Namely , isolated fragments may lack necessary neighboring coactivating sites . Conversely , relatively short isolated fragments could lack neighboring repressive elements . To test whether this could have biased our findings , we compared the size of the positive and negative enhancers and found no significant difference in size ( Figure S5 ) , arguing against the under-representation of repressive elements in the positive versus negative enhancers . Furthermore , longer fragments ( 2 kb and 5 kb ) flanking the ato autoregulatory enhancer do not cause loss of enhancer activity ( Figure S5 ) . To investigate whether Ato is sufficient to activate the positive enhancers , we ectopically expressed Ato along the anterior-posterior axis of the wing disc using the dppGal4 driver . 16 of the 20 tested enhancers show ectopic GFP expression along this boundary in response to Ato ( Figures 3 and S6 ) . To investigate whether the enhancers are dependent on the predicted Ato binding sites , we mutated predicted Ato binding sites in six positive enhancers from Fas2 , CG30492 , CG1626 , Dscam , Pde8 , and sca . All six enhancers showed altered expression upon the mutation of predicted Ato binding sites . For five of the six enhancers , GFP reporter expression is undetectable ( sca , CG30492 , and Dscam ) or severely reduced ( CG1625 , Pde8 ) in the posterior part of the eye disc ( Figure 3 ) . The Fas2 mutant enhancer does not show strong loss of GFP in the eye disc by immunofluorescence , however GFP mRNA levels produced by the mutated Fas2 enhancers are 3-fold reduced compared to the wild-type enhancer ( Figures 3 and 7 ) . Furthermore , the mutant Fas2 enhancer is no longer ectopically activated by Ato . These data demonstrate that the mutated Ato binding sites were predicted correctly for all target enhancers tested . Examination of the molecular functions of the newly identified target genes , and the biological processes they are involved in , reveals that whereas several Ato target genes are known to be involved in neuronal specification and retinal differentiation ( nmo , Dscam , Fas2 , sca , phyl , spdo , neur , and Traf4 ) , others we associate with these processes for the first time ( Pde8 , Rapgap1 , and Spn ) , and for the unknown genes we provide a novel functional annotation ( CG1625 , CG30492 , and CG8965 ) . Our target gene and enhancer predictions are based on high-scoring motif clusters across the 12 sequenced Drosophila species , hence the majority of the new Ato target enhancers are highly conserved in sequence . To test whether these enhancers are also functionally conserved , we tested the aligned sequences from two other species , namely D . annannassae and D . virilis , for three positive Ato target enhancers ( Dscam , CG1626 , and nmo ) by reporter assays in D . melanogaster . We find that all enhancers that are conserved in sequence are also conserved in function , in terms of their activity downstream of Atonal in the eye disc ( Figure 4 ) . The Dscam enhancer is conserved in sequence between D . melanogaster and D . ananassae , but the D . virilis orthologous sequence lacks the large region where the D . melanogaster Ato binding sites are located ( red box in Figure 4C ) . Expression analysis shows that the D . ananassae enhancer is active in the eye disc , whereas the D . virilis enhancer is not . Therefore , the newly identified regulatory regions are bona fide Ato target enhancers and Ato-dependent enhancer activity is under functional evolutionary constraint . Ato not only specifies the visual sensory receptors , but also the hearing , balance , and stretch sensory organs [35] , [43] . Our ignorance of the proneural code is highlighted by the fact that no known genes explain how a single proneural TF specifies different sense organs . We reasoned that the large set of Ato targets identified here could provide insight into how diverse specification programs are controlled by the same proneural factor . To this end , we examined the GFP expression patterns of the 20 Ato target enhancers across various imaginal discs under wild-type conditions , and in the wing imaginal disc under ectopic Ato expression conditions ( Figures 5 , S4 , S8 , and S9 ) . We find that none of these Ato target enhancers is specific to a single sensory organ subtype . Instead , we observe extensive reuse of targets across multiple organs as depicted in a heatmap plotting enhancer activation per sensory organ subtype ( Figure 5B ) . Particularly , we find that there exist two classes of enhancers . The first class , representing 45% of the targets ( nine out of 20 , all green in Figure 5 ) , is active in all sense organs examined; is easily ectopically activated by Ato; and contains genes such as Spn , Dscam and neur ( Figures S4 , S8 , and S9 ) . These data suggest that these enhancers form the core of a universal postembryonic Ato-dependent sensory program . Although unlikely , it cannot be fully excluded that some of these enhancers may have more restricted Ato-dependent activity patterns , because fragments cloned in this work lack putative repressor information . The second class of enhancers is restricted to a subset of sense organs and most show weak or no response to ectopic stimulation . This class contains genes such as Fas2 and nmo ( Figures S4 , S8 , and S9 ) . Interestingly , each enhancer of this class has a unique activity pattern ( Figure 5B , rows in the heatmap ) . Combined , these two enhancer subtypes yield a unique combination of targets for each sensory organ developmental program ( Figure 5B , columns in the heatmap ) . Because many proneural target genes are signaling molecules representing a diverse set of major developmental pathways such as BMP , Notch , Wnt , EGFR/Ras , JNK , and small GTPases , the differential transcriptional modulation of these signaling molecules between different sense organs could result in different developmental programs downstream of Atonal [44] . The GRN underlying photoreceptor differentiation is expected to comprise many TFs . Using previously published microarray data comparing wild-type eye imaginal discs with wild-type wing imaginal discs [45] , together with our Ato LOF microarray data , we find at least 94 TFs either enriched in the eye disc compared to the wing disc or significantly downregulated in ato−/− eye discs respectively . Determining the regulatory interactions between all these TFs and their target genes , as well as among the TFs themselves , will be a considerable undertaking . To achieve this , either ChIP-grade antibodies are required for all these TFs together with ChIP procedures optimized for small sample sizes ( e . g . , only few thousands cells ) . Alternatively , once high-quality position weight matrices are available for these factors , for example thanks to protein-binding microarrays [46] or other approaches [47] , we will be able to apply similar procedures as we applied for Ato above . Indeed , our validation experiments in Table 1 show that this may be feasible for other TFs . For example , to predict Ey targets we used publicly available microarray data obtained from wild-type and Ey-GOF imaginal discs . In a set of 189 upregulated genes after Ey overexpression ( this was done in a normal and an ato−/− background to obtain Ato-independent Ey-downstream genes ) , cisTargetX identifies the Ey motif [45] as the best motif among the 1 , 981 tested motifs , with 14 predicted direct targets , including known or likely Ey targets like so , Optix , eya , toy , and Tie ( Figure S10; Table 3 ) . Two predicted Ey target genes , namely Fas2 and CG30492 , had also been identified above as Ato targets using independent ( Ato GOF ) data . Remarkably , the predicted genomic binding sites for Ey fall within the Ato target regions ( Figure 6 ) , implying potential combinatorial control of Ey and Ato on shared target CRMs . This result may explain why the mutation of Ato binding sites alone in the Fas2 enhancer weakens but does not abolish its activity . We suggest that these factors cooperatively regulate a number of targets and therefore constitute a feed-forward regulatory loop with at least two shared target genes ( Figure 7 ) . Note that this combinatorial regulation could not be discovered by motif analysis on the validated Ato target enhancers ( using Clover ) , nor by heterotypic cisTargetX analysis , because this code represents only a minority of the Ato targets discovered thus far , yet independent Ey target discovery identified the cooperativity simply by overlapping target sets . As a final example of target discovery using TF perturbations , we performed an additional TF perturbation experiment followed by microarrays on three biological replicates for the Zinc-finger TF Senseless ( eye-antennal imaginal discs from atoGAL4 × UAS-Sens ) . Among a set of 97 significantly ( p<0 . 01 ) downregulated genes , cisTargetX identifies a Sens-related motif , namely a predicted motif using the Sens Zinc-fingers [48] as having a significantly ( z = 2 . 73 ) enriched subset of 24 predicted targets among these 97 genes ( Figure S10; Table S10 ) , including a shared target with Ato , namely Fas2 . Interestingly , cisTargetX also identifies Sens-related motifs in a set of upregulated genes ( p<0 . 05 and at least 2-fold upregulation ) , namely the RCWSWGATTTR consensus and the GFI PWM from TRANSFAC ( M00250 ) . These analyses confirm that TF perturbations allow identifying subsets of direct target genes of the perturbed TF . Although gene expression analyses , unlike ChIP for example , after TF perturbation are feasible for any TF , performing such experiments for the purpose of mapping an entire network would still represent an extensive effort . We therefore investigated whether direct target genes can be predicted from microarray data obtained under wild-type conditions . Ostrin et al . [45] determined gene expression profiles in wild-type eye imaginal discs and in wing imaginal discs , as controls for their Ey-overexpression studies . We used these control hybridizations to identify a set of 211 genes enriched in the eye disc ( >1 . 5-fold ) and used it as input for cisTargetX . Significant motifs found in this set include motifs of TFs with known eye functions , such as Su ( H ) ( best motif , z = 3 . 20 ) , Stat92E ( z = 2 . 85 ) , Atonal ( z = 2 . 74 ) , and glass ( z = 2 . 02 ) ( Table 3 ) . The Atonal predicted targets from this set overlap with the Ato GOF targets identified above ( e . g . , neur , m4 , CG8965 , Traf1 , Pde8 ) but also include new predictions that are likely true targets based on their established role or expression pattern , such as argos . The Su ( H ) motif found in this set was also identified as an important motif in the set of Ato-upregulated genes . Several of the predicted Su ( H ) targets ( see Table S10 ) are known or likely true Su ( H ) targets , such as E ( spl ) , m4 , HLHmgamma , phyl , and neur . We moreover find a large overlap between predicted Su ( H ) targets and validated Ato targets ( Figure 7 ) , and for the majority of the shared targets , although not all , the predicted target region coincides with the Ato target region . This finding corroborates previous findings of cooperative regulation by Su ( H ) and a proneural factor [36] , [37] . Additionally , a motif discovery analysis among the validated Ato target regions using Clover [14] identifies the Su ( H ) motif as significantly over-represented ( p<0 . 001 ) ( Figure 6; Table S11 ) . Nevertheless , some predicted shared Su ( H ) -Ato target genes have no Su ( H ) binding sites within the Ato target regions ( e . g . , Pde8 , neur , CG30492 , and CG8965 ) , and could be coregulated through different enhancers . This experiment , using coexpressed gene sets from wild-type tissues , illustrates how a set of coexpressed genes can be dissected into target genes of different TFs that operate in the same network neighborhood . This finding may be important because similar approaches can be applied in evolutionary studies using organisms for which transgenesis , and hence TF perturbation , is not feasible . Finally , using the significant target gene predictions for Ey , Atonal , Su ( H ) , Sens , and Glass , and adding previously published regulatory interactions , we derive a putative GRN underlying retinal differentiation , containing 250 predicted regulatory interactions between 177 genes ( Figures 7 and S11; Table S10 ) . This predicted network highlights extensive combinatorial regulation downstream of Ey , and suggests that signal transduction molecules may be key targets of the transcriptional program of retinal differentiation as they are highly over-represented in the network ( GO:0007165; p = 10−10 for all 177 genes of the network ) .
In this study we apply an integrated genetics and computational pipeline to identify functional target genes and target enhancers of TFs in the GRN underlying sensory organ development in Drosophila . Identifying target genes for any TF through genome scanning remains a significant challenge because any given consensus sequence has 103–106 instances throughout the genome [49] . For example , there are more than 600 , 000 matches to the canonical E-box motif CANNTG in the genome and ∼10 , 000 to ∼200 , 000 single matches to the more specific Ato motif ( Table S5 ) , depending on the similarity threshold employed [50] . To solve this problem we developed a method called cisTargetX to predict motif clusters across the entire genomes of 12 Drosophila species and determine significant associations between motifs and subsets of coexpressed genes . Validation of cisTargetX on publicly available gene sets identifies the correct motif and targets for nearly all tested TFs , demonstrating the general utility of approach . We therefore developed a cisTargetX Web tool available freely at http://med . kuleuven . be/cme-mg/lng/cisTargetX . cisTargetX is conceptually similar to the PhylCRM/Lever and ModuleMiner methods for vertebrate genomes [18] , [20] and allows determining whether a set of candidate genes , for example a mixture of direct and indirect target genes , is enriched for direct targets of a certain TF or combination of TFs . Compared to other motif discovery methods , such as Clover , PASTAA , PSCAN , and oPOSSUM , cisTargetX integrates motif clustering , cross-species comparisons , and whole-genome backgrounds in the discovery process . Additionally , and unlike the vertebrate methods mentioned above , cisTargetX focuses on homotypic CRMs and therefore allows separating the motif scoring ( performed offline ) from the gene set enrichment analysis ( performed online ) , yielding a computationally efficient method that can be used as an online Web application . A second difference from PhylCRM/Lever is that once a predicted motif is selected , cisTargetX determines the optimal subset of direct TF targets from the input set . cisTargetX was applied to Ato downstream genes identifying novel E-box motifs together with a significant enrichment of predicted direct targets . Although both GOF and LOF analysis yielded significant enrichment of E-boxes in misregulated genes , the significance was higher in the GOF analysis . This higher significance is likely because GOF of Ato results largely in the ectopic gain of one particular cell type , namely the R8 photoreceptor precursor , while the LOF condition results in the loss of all cell types and hence the downregulation of a larger set of genes across numerous cell types . In the third step we tested several predicted Ato target enhancers in vivo . This procedure identified 20 bona fide Atonal target enhancers out of 39 tested predictions , of which 17 are novel . This relatively high success rate almost certainly represents the lower limit of the true enhancer discovery rate because of false negative experimental results such as cases where the isolated enhancer is insufficient or requires its endogenous proximal promoter . Generally , demonstration of in vivo binding of the TF to a target enhancer that has been shown to be functional would be ideal . However , this is often not feasible , either due to lack of reagents or due to spatially and temporally sparse expression patterns of the TF in question . Our data suggest that cisTargetX is a cheap , simple , fast , and high-confidence approach for CRM discovery for any TF . Finally , it is important to note that 11 of the 20 Ato target genes are known to act in sensory organ development or function , indicating that our approach identifies biologically relevant target genes and that the other nine genes are also players in this process . A significant portion of the Ato target genes encodes signaling molecules regulating most of the known key developmental pathways such as Notch , EGFR , Wnt , and JNK . Ato activates targets that modulate signaling pathways; thus far no evidence exists that Ato ( or , to our knowledge , any other proneural TF ) directly activates terminal differentiation genes . Even for molecules like Fas2 , long thought to exclusively mediate adhesion during synaptic targeting , recent evidence reveals a role in regulating the precision of EGFR signaling during early photoreceptor specification [51] . While we cannot exclude that we have missed such target genes in this analysis because no approach can be certain of identifying all possible target genes , it is highly unlikely that a specific set of molecular functions would be selected against in an expression analysis approach . We therefore favor the idea that the terminal differentiation genes are activated by other TFs , or by the TFs downstream of the Ato-regulated signaling pathways . It is noteworthy that the pathways regulated by Ato target genes , as well as many of the target genes themselves or their mammalian homologues , such as sens , dap , Traf4 , and Mmp2 are implicated in cancer . We suggest that Ato's functions in cancer [23] , [52] , [53] is implemented via the regulation of some or all of the targets identified herein . A remarkable finding is that none of the Ato target enhancers is active in a single sensory organ . Instead , Ato activates a unique combination of targets in each sensory organs it specifies . What kind of target genes can , in a combinatorial fashion , lead to differential morphological and functional development ? On the basis of the analysis of the diversity of the beak sizes of Darwin's finches , it has been speculated that evolutionary changes in enhancers of signaling molecules have switch-like effects on a developmental GRN [44] . Our data suggest that variation of the proneural target set driven by changes in the cis-regulatory sequences of target genes shapes a unique regulatory state defined by a particular combination of signaling molecules . Interestingly , the Ato response elements within the regulatory sequences of target genes are evolutionarily conserved and their absence appears to alter the expression of these sequences . This observation leads us to hypothesize that a largely common genetic program induces different sensory organs , and that developmental and evolutionary variation of these organs occurs via subtle variations in the cis-regulatory sequences of signaling regulators . We propose that similar principles underlie diversification of most , if not all , developmental programs . The encouraging results for Atonal lead to the prediction of a large set of target genes for multiple TFs involved in retinal differentiation and they furthermore show that expression studies combined with computational predictions are a powerful tool of regulatory network discovery . The identification of Glass and Su ( H ) targets from wild-type eye versus wing comparisons of gene expression shows that genetic perturbations of TFs are not a prerequisite to find enriched direct targets in a set of candidate genes , at least for tissue specific TFs . Therefore , from wild-type comparative gene expression experiments meaningful results can be obtained . The cisTargetX analyses in this study compare the enrichment of predicted targets for single motifs ( i . e . , homotypic enhancer models ) within sets of coexpressed genes . The most important advantage of homotypic clusters is that no a priori knowledge of cooperative factors is needed . An additional advantage is that theoretically the predictions can be more specific than “free” heterotypic clusters in which binding sites for any combination of TFs is allowed ( the “OR” rule ) , and more sensitive than the “constrained” class of heterotypic clusters in which all input TFs are required to have binding sites ( the “AND” rule ) . Tests with heterotypic enhancer models , consisting of motif combinations , generally showed lower enrichment than homotypic models ( unpublished data ) , corroborating previous findings [54] . Genes that are activated in the same temporal and spatial patterns do not necessarily share the same cis-regulatory code , and the performance of genome-wide predictions may not necessarily benefit from heterotypic enhancer models , mainly because of sensitivity problems , at least in approaches similar to cisTargetX that are based on enrichment of direct targets in a candidate gene set . In other words , if many different combinatorial codes exist , then the presence of cofactor sites in only a few enhancers does not yield statistical over-representation and hence does not emerge from the noise . Moreover , coregulation might also occur through different enhancers of the same target genes and we observe many potential examples of this by predicting targets for multiple TFs independently . The important point is that whether coregulation occurs through shared or distinct enhancers , homotypic cluster predictions using cisTargetX , followed by comparisons of the targets between the TFs can discover these relationships . The putative early retinal differentiation network reconstructed from cisTargetX predictions shows waves of combinatorial regulation orchestrating spatial and temporal gene expression accuracy . We find two feed-forward loops , namely Ey-Ato and Ato-Sens . These features are similar to the reconstructed regulatory networks underlying early embryonic processes [5] , [55] . This finding indicates that exploiting motif predictions in conjunction with expression perturbations allows discovering similar regulatory networks as with ChIP-chip or ChIP-Seq approaches , where more material ( e . g . , large embryo collections ) and specific reagents ( e . g . , high-quality antibodies ) are required . Finally , these predictions represent a useful resource for future experiments aimed at dissecting the mechanistic basis of sensory specification .
Fly strains used were ato-GAL4 ( NP6558 ) , GAL4/7 , UAS-ato , UAS-sens ( a gift from H . Bellen ) , UAS-scute ( a gift from J . Modolell ) , dpp-GAL4 , yw , M ( eGFP . vas-int . Dm ) ZH-2A; M ( RFP . attP' ) ZH-22A ( a gift from K . Basler ) ; yw , M ( eGFP . vas-int . Dm ) ZH-2A; y+ attP' VK37 , and VK16 ( a gift from H . Bellen and K . Venken ) , CantonS , and yw . All flies were raised at 18°C on standard fly food and vials were transferred to 28°C for 24 h before dissections of imaginal discs . Imaginal discs of wandering third instar larva were dissected and processed as described [56] . Antibodies used were anti-ato antibody ( gift from A . Jarman and P . zur Lage ) , anti-GFP ( Invitrogen ) , and anti-Sens ( gift from H . Bellen ) . Dissection of eye-antennal discs was done in RNA Later ( Ambion ) and RNA extraction with mini RNA isolation kit ( ZymoResearch ) . For relative quantitation of positive control genes ( ato , sens , sca , dap ) , we used the comparative ddCt method ( SDS User bulletin 2; Applied Biosystems ) with the qPCR Mastermix Plus for SYBR Green I ( Eurogentec ) on the ABI PRISM 7000 instrument . Total RNA was converted to cDNA using QuantiTect Reverse Transcription ( Qiagen ) . Primers were designed with PrimerExpress software ( Applied Biosystems ) and are available on request . As housekeeping genes we used rpl32 , rps13 , and gapdh . After an initial denaturation step for 10 min at 95°C , thermal cycling conditions were 15 s at 95°C and 1 min at 60°C for 40 cycles . Eight control samples were extracted , namely two biological repeats for four lines ( cantonS wild type , UAS-ato , ato-Gal4 , Gal4/7 ) . For Ato GOF , three biological repeats were extracted for atoGal4 × UASato and three for Gal4/7 × UASato ( thus six Ato GOF samples in total ) . For Ato LOF , +;ato1/hshid stock was heat-shocked on three consecutive days starting at first instar stage , and three independent repeats were extracted . For sens GOF , three atoGal4 × UASsens samples were extracted . Motif prediction in sets of related enhancers , such as the 21 Ato target enhancers in Figure 6 , are performed with Clover [14] , using all 5-kb upstream and intronic sequences as background sequences and using 10 , 000 randomizations ( −r 10 , 000 ) . Clover output is transformed to GFF format using a perl script . Visualization of enhancers and predicted binding sites is done in TOUCAN [60] . All Clover motifs are shown with motif score greater than 6 ( default Clover parameter ) . The Ato binding-site predictions that were mutated ( Figure 3B ) are those given by Cluster-Buster with the Ato-PWM , with motif score greater than 6 ( default Cluster-Buster parameter ) . Microarray data are available from the Gene Expression Omnibus as Series GSE16713 ( http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE16713 ) . Positive and negative enhancer data will available from the REDfly ( http://redfly . ccr . buffalo . edu/ ) [61] and ORegAnno ( http://www . oreganno . org ) [62] databases of regulatory annotation .
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Tens of thousands of regulatory elements determine the spatiotemporal expression pattern of protein-coding genes in the metazoan genome . Each regulatory element , when bound by the appropriate transcription factors , can affect the temporal transcription of a nearby target gene in a particular cell type . Annotating the genome for regulatory elements , as well as determining the input transcription factors for each element , is a key challenge in genome biology . In this study , we introduce a computational method , cisTargetX , that predicts transcription factor binding motifs and their target genes through the integration of gene expression data and comparative genomics . We first validate this method in silico using public gene expression data and , then , apply cisTargetX to the developmental program governing photoreceptor neuron specification in the retina of Drosophila melanogaster . Particularly , we perturbed predicted key transcription factors during the initial steps of neurogenesis; measure gene expression by microarrays; identify motifs and predict target genes; validate the predictions in vivo using transgenic animals; and study several functional and evolutionary aspects of the validated regulatory elements for the proneural factor Atonal . Overall , we show that cisTargetX efficiently predicts genetic regulatory interactions and provides mechanistic insight into gene regulatory networks of postembryonic developmental systems .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/genomics",
"computational",
"biology/sequence",
"motif",
"analysis",
"computational",
"biology/transcriptional",
"regulation",
"genetics",
"and",
"genomics/gene",
"expression",
"computational",
"biology/comparative",
"sequence",
"analysis",
"developmental",
"biology/developmental",
"evolution",
"developmental",
"biology/cell",
"differentiation",
"molecular",
"biology/bioinformatics",
"developmental",
"biology/neurodevelopment",
"computational",
"biology/signaling",
"networks"
] |
2010
|
Robust Target Gene Discovery through Transcriptome Perturbations and Genome-Wide Enhancer Predictions in Drosophila Uncovers a Regulatory Basis for Sensory Specification
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Vibrio vulnificus is a pathogen that causes both severe necrotizing wound infections and life-threatening food-borne infections . Food-borne infection is particularly lethal as the infection can progress rapidly to primary septicemia resulting in death from septic shock and multiorgan failure . In this study , we use both bioluminescence whole animal imaging and V . vulnificus bacterial colonization of orally infected mice to demonstrate that the secreted multifunctional-autoprocessing RTX toxin ( MARTXVv ) and the cytolysin/hemolysin VvhA of clinical isolate CMCP6 have an important function in the gut to promote early in vivo growth and dissemination of this pathogen from the small intestine to other organs . Using histopathology , we find that both cytotoxins can cause villi disruption , epithelial necrosis , and inflammation in the mouse small intestine . A double mutant deleted of genes for both cytotoxins was essentially avirulent , did not cause intestinal epithelial tissue damage , and was cleared from infected mice by 36 hours by an effective immune response . Therefore , MARTXVv and VvhA seem to play an additive role for pathogenesis of CMCP6 causing intestinal tissue damage and inflammation that then promotes dissemination of the infecting bacteria to the bloodstream and other organs . In the absence of these two secreted factors , we propose that this bacterium is unable to cause intestinal infection in humans .
Vibrio vulnificus is a motile , Gram-negative , opportunistic human pathogen capable of causing severe to life-threatening infection in individuals with predisposing conditions , including liver damage , hereditary hemochromatosis and compromised immune systems [1]–[3] . Infection can result from consumption of contaminated seafood or from exposing an open wound to water harboring the pathogen . Wound infection can progress to edema , cellulitis , ecchymoses and necrotizing fasciitis at the site of infection [4] , [5] . The mortality of wound infection is about 25% because primary septicemia does not frequently occur [5] , [6] . By contrast , V . vulnificus food-borne infection rapidly progresses to primary septicemia with symptoms that include high fever , chills , decreased blood pressure and septic shock [5] , [7]–[9] . These infections result in a much higher mortality rate ( ≥50% ) with rates as high as 100% in the absence of antibiotic therapy [5] , [6] , [10] . Hence , a critical aspect of V . vulnificus pathogenesis is its ability to infect a host via the gastrointestinal tract and then rapidly spread from the small intestine to the blood stream . Although several secreted virulence factors of V . vulnificus have been identified [3] , [11] , only two have been previously associated with increased death during intestinal infection: the secreted cytolytic/hemolysin pore-forming toxin encoded by vvhA [12] and the multifunctional autoprocessing RTX ( MARTXVv ) toxin encoded by gene rtxA1 [13]–[15] . In vitro , both of these toxins are cytolysins associated with lysis of a variety of cell types including erythrocytes , epithelial cells and macrophages , albeit by different molecular mechanisms [12] , [16]–[22] . The role of these toxins in vivo during infection has been less well-characterized . When injected directly to the bloodstream , purified VvhA is lethal at sub-µg levels and causes hypotension and tachycardia , along with skin and pulmonary damage [12] , [23] . However , deletion of vvhA from V . vulnificus had either a slight or no defect in virulence when delivered intraperitoneally ( i . p . ) and no defect when delivered intradermally ( i . d . ) [23] . When delivered intragastically ( i . g . ) to neutropenic mice , loss of vvhA resulted in a detectable , albeit modest , 4–5 fold increase in median lethal dose ( LD50 ) [12] , [23] . In comparison to VvhA , MARTXVv has been shown to have a significantly greater contribution to mouse lethality . A mutant in rtxA1 has a 100- to 500-fold increase in LD50 compared to wild-type when inoculated i . p . [14] , [17] , [18] and a 13-fold increase when inoculated subcutaneously ( s . c . ) [19] . A deletion of rtxA1 caused a 180 to 2600-fold increase in LD50 in an i . g . infection model with the contribution of the gene deletion to virulence varying depending on the specific toxin variant that is expressed [15] . Comparison across different studies suggest that the MARTXVv toxin is the most significant virulence factor of V . vulnificus and both MARTXVv and VvhA exert a greater effect on i . g . and septicemic infection compared to i . p . , s . c . or i . d . infection . In this study , we sought to understand how cytotoxins MARTXVv and VvhA contribute to food-borne infection by highly virulent V . vulnificus strains that produce a particularly potent variant of the MARTXVv toxin [15] . We used bioluminescence imaging ( BLI ) and measurement of bacterial colonization to monitor early events in growth and dissemination of V . vulnificus strain CMCP6 in mice after i . g . infection . This study shows that both MARTXVv and VvhA from CMCP6 contribute to the onset of colonization and to significant bacterial growth sooner after inoculation . These data are consistent with a role of both toxins in disabling innate immune cells in the small intestine allowing for more rapid growth . However , the effect of the toxins is not limited to innate cells as these toxins are also here shown to directly cause epithelial tissue damage . The combination of rapid growth and tissue damage is essential for the dissemination to the bloodstream earlier during the infection cycle and this rapid dissemination is the leading factor promoting death .
V . vulnificus CMCP6 can cause lethal infection of adult mice inoculated i . g . [15] . To more directly measure how disease progresses during early infection , we transformed V . vulnificus CMCP6 with plasmid pHGJ1 that expresses the Photorhabdus luminescens lux genes from the constitutively active Vibrio cholerae ompC promoter ( see material and methods ) . The LD50 for the resulting strain CMCP6lux ( HG0905 ) was 3 . 1×105 CFU ( Table S1 ) , about 13-fold higher than the LD50 of 2 . 4×104 CFU previously determined for the parent strain CMCP6 in this mouse infection model [15] . The presence of the plasmid also caused an in vitro defect in growth in antibiotic-free culture media ( Figure S1 ) . The difference in both in vitro growth and in vivo virulence between the parent strain CMCP6 and CMCP6lux ( HG0905 ) is likely due to spontaneous bacterial death upon plasmid loss . To maintain the lux reporter without antibiotic selection , the lux plasmid ( pHGJ1 ) carries the hok/sok plasmid addiction system [24] . Bacteria that lose the plasmid during cell division will die upon dilution of the less stable antitoxin . The advantage of this system is that only bacteria that produce luciferase survive and thus there is no contribution to infection from bacteria that are not lux+ . In addition , the expression of the luciferase genes also probably contribute to the reduced virulence of HG0905 compared to CMCP6 since a CMCP6 that carries a plasmid deleted of the lux genes ( HG0909 ) was slightly more virulent than HG0905 ( Figure S1 ) . In this paper , we compared lux+ strains derived from parent strain CMCP6 and we confirmed that there was no in vitro growth difference in mutant strains CMCP6luxΔrtxA1 ( HG0906 ) , CMCP6luxΔvvhBA ( HG0907 ) and CMCP6luxΔrtxA1vvhBA ( HG0908 ) containing plasmid pHGJ1 in antibiotic-free culture media ( Figure 1G ) compared to the isogenic parent CMCPlux ( HG0905 ) . Further , we confirmed by lactate dehydrogenase ( LDH ) release assays that the mutants carrying pHGJ1 have defects in HeLa cell lysis consistent with previous findings [14] , [18] . Specifically , the ΔrtxA1 mutation reduced rapid HeLa cell lysis while the ΔvvhBA mutation eliminated slow cell lysis . The double mutant did not lyse cells ( Figure S2 ) . The effect on virulence of the lux+ plasmids was less evident in the mutant strains since these strains likely are not growing in vivo and thus less likely to lose the plasmid during rapid replication . Thus , the LD50 for CMCP6luxΔrtxA1 ( HG0906 ) matched our previously determined LD50 of 8 . 0×107 CFU for plasmid-free CMCP6ΔrtxA1 and thus the deletion of rtxA1 exhibited only a 260-fold effect on virulence due to the reduced virulence of the isogenic wild type . Deletion of vvhA caused a 61-fold decrease in virulence with an LD50 of 1 . 9×107 . This result was surprising since previous studies of i . g . infection with a ΔvvhA mutant in isolate YJ016 revealed only a modest 5-fold virulence defect [12] . However , the previous study was conducted in highly susceptible iron-overloaded , neutropenic mice , which may have masked the importance of this factor for intestinal infection . Consistent with a role of both factors in infection by CMCP6 , the CMCP6luxΔrtxA1ΔvvhBA double mutant ( HG0908 ) was essentially avirulent with an LD50>109 ( Table S1 ) . To monitor how rapidly V . vulnificus bacteria expand in vivo , bioluminescence from mice infected with CMCP6lux ( HG0905 ) was observed and quantified at defined intervals using an IVIS 100 bioluminescence imager ( Xenogen Corp . ) . As previously described by others [25] , use of anesthesia during orogastric inoculation can result in accidental lung infection due to contamination of the larynx during infection . In pilot studies , we similarly found that some mice developed lung infection . These mice usually died rapidly , often by 6 hours after infection . In our study , 3 mice infected with HG0905 developed infection of the lung detectable by IVIS imaging by 4 hr . These mice were euthanized and removed from analysis . All other mice did not show detectable lung infection by IVIS imaging . At a dose of 1×106 CFU , 100% of mice with an intestinal infection died between 12 and 22 . 5 hr post-inoculation ( Figure 1B ) . Prior to death , all of the CMCP6lux ( HG0905 ) inoculated mice ( 6/6 ) showed detectable levels of photon flux . Two of the mice reached our preset detection limit by 4 hr , 3 mice by 8 hr , and all mice had detectable levels by 12 hr ( Figure 1A and B ) . Of note , all mice showed a steady rise ( mean slope between onset and peak ( m ) equals 0 . 174; Figure 1F ) in light emission until the animal was sacrificed for severe morbidity between 12 and 22 . 5 hr demonstrating constant replication of the wild-type bacteria . However , even though all mice reached at least 7 . 3 RLU ( Relative Luminescence Unit on a logarithmic scale; Luminescence Unit represents the photons s−1 cm−2 sr−1 ) before death , attainment of this level did not predict eminent death as three mice survived for 4–22 . 5 hr after crossing this threshold . To demonstrate that photon flux is representative of changes in intestinal colonization , in a separate experiment , 5 mice inoculated with 106 CFU were euthanized at both 8 and 12 hr . In accordance with flux readout in the previous experiment ( Figure 1A and B ) , there was variability in recovered CFU from the small intestine at 8 hr ranging from 104–108 CFU or −2 to +3 log unit change from the inoculation dose ( Figure 2A ) . By 12 hr , all mice were colonized above the infection dose representing 2–5 log units growth ( Figure 2B ) . In addition , there was dissemination of the bacteria to the liver and spleen by 8 hr , indicating progression to septicemia in all mice during the earliest stages of infection ( Figure 2C and D ) . Overall , V . vulnificus CMCP6lux ( HG0905 ) was shown to expand in vivo and this rapid growth occurred coincident with dissemination to other tissues shortly after inoculation . To determine if MARTXVv is the factor that promotes the rapid growth seen in mice after i . g . infection , we monitored the effect of deletion of the rtxA1 gene from CMCP6lux on disease progression . The most apparent phenotype of the resulting strain HG0906 compared to CMCP6lux ( HG0905 ) was a delay in the time required to the BLI detection limit ( Figure 1A ) . One of 9 mice was sacrificed due to lung infection and one mouse was not infected . Among the 7 infected mice , light production was detectable in only one ( 14% ) by 4 hr and 3/7 ( 43% ) of mice by 8 hr ( Figure 1C ) . Four mice ( 50% ) showed delayed onset with detectable light emission only after 8–12 hr . After onset , the average rate of growth in all infected mice was similar to wild-type ( m = 171 , Figure 1F ) . However , unlike CMCP6lux ( HG0905 ) infected mice , 3 of 7 mice ultimately survived to 36 hr despite the in vivo bacterial load . In addition , several of the mice succumbed only late in the experiment indicating , as suggested by LD50 studies ( Table S1 ) , that more mice might have survived except for the stress imposed by repeated anaesthetic regimen necessary for imaging . Thus , the major effect of loss of the MARTXVv toxin was delayed onset of bacterial growth to detectable levels . Further , among the mice that attained high bacterial loads , half failed to progress to death and the mice cleared the infections . As further evidence for delayed growth , there was a trend toward reduced colonization of the small intestine by the CMCP6luxΔrtxA1 mutant ( HG0906 ) at 8 hr that reached statistical significance by 12 hr ( Figure 2A and B ) . In addition , by 8 hr , there was significantly reduced dissemination of bacteria to the liver and spleen ( Figure 2C and D ) . Overall , our results suggest that loss of rtxA1 results in an inability to consistently establish an infection that can progress to other organs shortly after ingestion of bacteria and thus the infections are delayed and less severe at least 50% of the time . To test if VvhA also contributes to infection , we next tested a CMCP6luxΔvvhBA mutant ( HG0907 ) . Results were intermediate between CMCP6lux and the isogenic ΔrtxA1 mutant with 7 of 9 mice showing increased light emission beginning 4–12 hr after inoculation rising to values greater than 7 . 3 RLU . Similar to both CMCP6lux and the isogenic ΔrtxA1 mutant , the 7 mice successfully infected with CMCP6luxΔvvhBA mutant showed a similar rate of increasing light emission with other strains indicating it does grow in vivo ( m = 0 . 142; Figure 1F ) . 1 of these 7 mice reversed course and began to clear the infection while the other 6 succumbed to infection by 22 . 5 hr . When assessed for colonization , there was a consistent trend for reduced colonization of CMCP6luxΔvvhBA mutant ( HG0907 ) in the intestine at 8 hr and 12 hr ( Figure 2A and B ) and reduced dissemination during early infection to the liver and spleen but these values did not achieve statistical significance ( Figure 2C and D ) . Thus , mice infected with the ΔvvhBA mutant showed detectable decreases in numerous parameters of infection including delayed and reduced death but this cytolysin does not exert the same impact on progression of CMCP6 infection as MARTXVv . Despite its minimal effect when the rtxA1 is intact , expression of VvhBA by CMCP6lux does account for the residual virulence of the CMCP6luxΔrtxA1 mutant . A CMCP6lux double mutant eliminating both vvhBA and rtxA1 ( HG0908 ) was nonlethal in mice at 1×106 CFU ( Figure 1E ) except for one mouse sacrificed due to lung infection . Three of the mice were overall defective for bacterial growth and did not achieve 7 . 3 RLU at any time point and 1 was not infected at all . In 2/8 ( 25% ) mice there was long 12 hour delay to detectable light production ( Figure 1E ) . When bacteria did expand in vivo , the mean slope from onset of detection to peak ( m = 0 . 164 ) was similar with those of wild type ( Figure 1F ) . However , in all cases where mice did achieve high bacteria loads , the emission of light reversed from a peak between 15 and 22 . 5 hr post-inoculation and all mice ultimately cleared the infection . When tested for colonization , CFU recovered from the small intestine were significantly reduced at both 8 and 12 hr ( Figure 2A and B ) and the bacteria did not disseminate to the liver and spleen by 8 hr ( Figure 2C and D ) . Overall , these data indicate that in V . vulnificus strain CMCP6 , MARTXVv in conjunction with a secondary additive contribution from VvhA is essential during the early stages of infection to promote initiation of the infection and dissemination to the bloodstream . Lack of bacterial growth during in vivo infection due to loss of secreted factors can often be restored by co-infection wherein mutant bacteria benefit from alteration to the host environment by the co-infecting strain . These data can reveal that mutant bacteria are not defective in their ability to replicate in vivo per se , but lack the capacity to modify the host environment to promote their growth . To test if HG0908 could be restored for in vivo growth by co-infection , we transferred a plasmid from which the luxCDABE operon was deleted ( pHGJ2 ) into CMCP6 and the double cytolysin mutant strains and competed strains 1∶1 with the lux+ double mutant ( HG0908 ) . Thereby , if HG0908 is rescued by co-infection , total flux during co-infection should increase since all light signal would originate from HG0908 and not the cytolysin producing co-infecting strain . When 5×105 CFU of lux− wild-type ( HG0909 ) and 5×105 CFU of lux+ double mutant ( HG0908 ) were co-inoculated , median light production produced by the double mutant was 6 . 9 RLU at 8 hr post-infection and reached 7 . 6 RLU after 12 hr infection . By contrast , mice infected with 5×105 CFU of lux+ double mutant ( HG0908 ) and 5×105 CFU lux− double mutant ( HG0910 ) was 5 . 7 RLU at 8 hr post-infection and 5 . 9 RLU after 12 hr ( Figure 2E and F ) . Thus , the in vivo growth defect of the double cytolysin mutant HG0908 can be restored by co-infection with a cytolysin producing strain indicating that HG0908 is not defective in its ability to replicate in vivo but in its ability to modify the host environment . We have demonstrated that MARTXVv and VvhA of V . vulnificus CMCP6 are required for earlier onset of in vivo growth after i . g . inoculation . This result is consistent with a recent study conducted by s . c . infection to model wound-induced infection using strain YJ016 . The s . c . study of YJ016 suggested the requirement for MARTXVv during infection is primarily for protection from phagocytes to promote growth [19] . This conclusion seems to conflict with evidence that cytotoxins of CMCP6 , YJ016 , and other V . vulnificus strains are linked to lysis of both epithelial cells and macrophages in vitro [14] , [16]–[19] , [21] . To reveal whether the cytotoxins have an additional role beyond promoting rapid growth during intestinal infection , mice were inoculated with a lethal dose of CMCP6lux ( HG0905 ) and the terminal ileal tissue was collected after 8 hr infection for various histopathological staining . Severe disruption of the intestinal barrier occurred in the ileum infected with HG0905 ( Figure 3A–C ) with many broken villi and barrier disruptions , consistent with pathology reported in earlier studies in neutropenic mice using strain YJ016 [12] . Excessive amounts of epithelial cell debris and heavy cellular infiltration of lamina propria were observed in the lumen and mucosa of the ileum from mice infected with the wild type ( Figure 3A–C ) . Staining with anti-CD45 showed extensive influx of monocytes and other immune cells to the tissue and the lumen ( Figure 3D ) . Within the destroyed tissue , F4/80 positive macrophages are present and proinflammatory cytokine IL-1β is secreted and found distributed in the ruptured tissue ( Figure 4A ) . The lumen is filled with epithelial debris ( stained positive with β-catenin ) , lysed macrophages , and IL-1β presumably release from necrotic macrophages ( Figure 3C and 4A ) . By contrast , mice infected with 1×106 CFU of either the CMCP6luxΔrtxA1 mutant ( HG0906 ) or the CMCP6luxΔvvhBA mutant ( HG0907 ) showed no destruction of the villi architecture and infiltration of the lamina propria except only slight swelling ( Figure 3E and F ) . Mice infected with double mutant HG0908 showed no pathology distinct from PBS mock control ( Figure . 3G and H ) . However , the absence of tissue damage cannot be conclusively linked to the toxins by this approach because the bacterial load of wild type in the ileum 8 hours after infection of 106 CFU would be much higher than that of single and double mutant strains due to affects of the loss of cytotoxins on bacterial growth ( Figure 2A and B ) . Therefore , we infected mice with increasing CFU so that the bacterial load in ileum at the point of euthanasia would be equalized . In mice infected with a CMCP6luxΔrtxA1ΔvvhBA double mutant ( HG0908 ) at a dose of 5×107 or 1×109 CFU ( n = 6 ) , there was no tissue damage in the ileum of any of the mice ( Figure 5A ) and the epithelial lining appears similar to the PBS mock infected control group ( Figure 3H and I ) . Notably , even at the dose high enough to kill 1/6 mice in 8 hour , no tissue damage occurred . This finding is significant because it indicates that no other secreted protease or toxin produced by strain CMCP6 is sufficient to cause visible tissue damage in the absence of MARTXVv or VvhA , even when a concentration of bacteria in the lumen exceeded that normally found for wild type by 8 hr post inoculation . Staining shows macrophages are present in the lamina propria at low dose infection of double mutant and are secreting only low amounts of IL-1β consistent with the low levels of colonization at 8 hours post inoculation ( Figure 4A ) . By contrast , at a high dose , macrophages are less apparent in the lamina propria and may have moved to the lumen , where high density staining of IL-1β is seen ( Figure 4 ) . This focusing of a proinflammatory immune response to the lumen is an effective response since infected mice are surviving a dose that would kill 100% within 8 hr if infected with CMCP6lux ( HG0905 ) . To determine whether MARTXVv and/or VvhA is directly responsible for the tissue damage caused by CMCP6lux , similar increasing dose infections were performed with the single toxin deletion strains . When 1×109 CFU of either the isogenic ΔrtxA1 mutant ( n = 5 ) or the ΔvvhBA mutant ( n = 5 ) was inoculated , 40% or 80% of the mice died within 8 hr post-infection , respectively ( Figure 5B and C ) . The difference in survival compared to the double mutant HG0908 shows that the toxins are able to function independently while the difference between HG0906 and HG0907 further exemplifies the relative import of MARTXVv over VvhA for overall survival of CMCP6lux . At the intermediate infection dose of 5×107 CFU , the median number of each single mutant recovered from the small intestines were 8 . 1 and 8 . 2 Log CFU/organ , respectively ( Figure 5B and C ) ; not significantly different from the 7 . 9 Log CFU/organ recovered from CMCP6lux ( HG0905 ) inoculated at only 1×106 CFU . Note that these median values were calculated from surviving , colonized mice that were sacrificed for histopathology and do not include mice that rapidly succumbed to infection or noncolonized mice with recovered CFU below the detection limit . In the CMCP6luxΔrtxA1 mutant infected mice , only a small portion of the villi showed necrotic epithelial cells and hypercellularity in the lamina propria ( Figure 5B ) indicating , in the absence of MARTXVv , VvhA induces mild tissue damage accounting for the modest dissemination of this mutant to the liver ( Figure 2C ) . By contrast , samples of intestines from mice infected by the MARTXVv+ strain ΔvvhBA mutant showed sloughed villi and an infiltration of the lamina propria into the lumen indicating MARTXVv induces tissue damage that is more severe than associated with VvhA ( Figure 5C ) . This finding is consistent with the significant ability of this mutant to disseminate to the liver ( Figure 2C ) . Importantly , we did not observe the severe tissue destruction similar to that found in the CMCP6lux ( HG0905 ) infected group in any of these mice suggesting that both cytotoxins target intestinal epithelial cells and both cause tissue damage that is additive or possibly even synergistic .
Successful and rapid in vivo growth of V . vulnificus is generally regarded as an essential step in its pathogenesis [11] , [26] . We developed a BLI system using the highly virulent V . vulnificus strain CMCP6 to directly observe how rapid in vivo growth during early infection can influence the outcome of infection . Wild-type V . vulnificus CMCP6lux infection expanded quickly in mice very early after intestinal infection and all the animals progressed to lethality . By contrast , deletion of one or both of the rtxA1 and vvhA genes led to bacteria with a in vivo growth delay leading to reduced CFU in animals by 8–12 hr post infection in the small intestine and other organs , although there was no in vitro growth defect ( Figure 1 ) . However , while some mice infected with strains missing just one toxin showed little or no growth , many mice still died from infection and others that survived infection emitted a high level of light up to 15 hr ( Figure 1A–E ) . In these mice with detectable light , the slope of light emission representing the growth rate was similar to wild type from first detection to peak infection . Furthermore , co-infection studies revealed the mutant that produces no cytotoxins has the capacity to grow in vivo , but does not have the capacity to alter the host environment to promote its own growth . These findings suggest that the cytolysins cause another phenomenon beyond simply manipulating bacterial load in the animal and that this event is important to cause lethal infection . Previous studies indicate that dissemination of infection to the liver is a major predictor of mouse mortality after wound infection [9] , consistent with clinical reports indicating that hepatic hemorrhage is a frequent cause of death of patients after both wound and food-borne V . vulnificus infection [27]–[29] . MARTXVv and VvhBA from strain CMCP6 are here shown to not only be associated with enhanced in vivo growth , but also with necrosis of tissue in the small intestine and translocation of V . vulnificus from the small intestine to the liver ( Figure 2C ) . Notably , the small intestine has already been recognized as the site of the most severe tissue necrosis in human autopsy of V . vulnificus patients [27] . Although both single toxin gene mutants induced from moderate to severe necrosis and dissemination , the double mutant was completely restricted to the intestine and no damage was evident ( Figure 3 ) . Close examination of Figure 1A suggests that light signal from the double mutant occurred predominantly in the lower abdomen compared to wild type light emission from the mid-abdomen , an observation consistent with the ability of the double mutant to grow in vivo during transit through the upper and lower bowel , accounting for the increased light signal , but the infection never progressed to the liver . We next sought to understand if the role of the MARTXVv in dissemination of CMCP6 during gut infection was to increase the growth rate within the small intestine during the first few hours to create a larger pool of bacteria to express VvhA , proteases or other cytolysins to promote dissemination as proposed by Lo et al . [19] for wound related infections or , if the role of the toxins is to utilize the cytolytic activity to directly lyse intestinal epithelial cells to create a pathway through which the bacteria could disseminate as proposed by Kim et al . [14] . Our study found that , for strain CMCP6 , both MARTXVv and VvhA function additively to cause intestinal tissue necrosis . We also found that in vivo growth does occur in the absence of toxins but is restricted to the intestine , possibly to the colon as recovered CFU from the small intestine at 12 hr was decreased compared to wild type despite strong light emission in some animals . Indeed , studies examining wound infection with strain YJ016 also show less dermal tissue damage upon deletion of rtxA1 , but the effect was negated as secondary to the effect on decreased bacterial load [19] . Using identical inocula , we came to the same conclusions . It was only after we used increased inocula such that bacterial load at the time of euthanasia was equivalent that the role of toxins on tissue necrosis became evident . Thus , it is possible that MARTXVv and VvhA will be shown to also be involved in tissue necrosis during wound infection , at least for strains CMCP6 and YJ016 . However , lethal dose studies have shown that MARTXVv and VvhA are in general less important to wound induced lethality than i . g . infection [12] , [15] , [18] , [19] , [23] , supporting the conclusion by Lo et al . [19] that alternate factors have a significant role during wound infection . While our data support that the cytotoxins target the intestinal epithelium , our results do not negate that the toxins have a significant role in innate immune defense as well . Both in vitro and in vivo , V . vulnificus is also known to cause killing of phagocytic cells [19] , [21] . In vitro , both toxins are known to induce NLRP3 dependent caspase-1 activation resulting in necrosis of macrophages [21] . The absence of phagocytes in hepatic tissue has been previously noted as a factor that can contribute to patient mortality [30] . However , our study reveals that V . vulnificus is inducing massive inflammation , leading to recruitment of monocytes , neutrophils and F4/80-positive macrophages . These results are consistent with an increase of the proinflammatory cytokines TNF-α , IL-6 and IL-1ß that were detected in the sera of V . vulnificus septicemic patients [31] . In our study , the increase of IL-1ß secretion in mice inoculated with a lower concentration of CMCP6lux suggest that in vivo , the action of toxins against macrophages induces pyroptosis , just as it does in vitro [21] . A question that remains is whether the toxins are simultaneously promoting inflammation while attempting to keep it at bay by killing the recruited cells . A recent study has revealed that some gut pathogens specifically induce inflammation as a mechanism to promote rapid growth . Salmonella is known to induce pyroptosis leading to inflammation [32] . This inflammation then allows Salmonella to use tetrathionate respiration in the anaerobic environment of the gut , which promotes bacterial replication and transmission [33] , [34] . In the present paper , the tissue damage of villi in small intestine was clearly apparent ( Figure 3 and 5 ) and the inflammation as early as 8 hr post infection is severe ( Figure 3D ) . Thus , while killing of phagocytes is one mechanism that would promote rapid in vivo growth , particularly in the bloodstream , it is possible that inflammation itself may promote growth in the intestine . The ttr genes that encode the tetrathionate respiration system necessary for Salmonella to grow in response to inflammation [34] are present in V . vulnificus YJ016 on what appears to be a pathogenicity island [33] , [35] . However , the other sequenced V . vulnificus strains [36] , including CMCP6 used in this study , do not seem to have acquired this island . If the fact that this pathogen induces the host inflammation to promote their own outgrowth is a general consequence , our results suggest that a novel mechanism unrelated , ( or in the case of ttr+ YJ016 in addition to tetrathionate ) is required , depending on the strain isolate . A final important finding of our work is the evidence that MARTXVv and VvhBA are directly linked to death of the host . One mechanism that accounts for the linkage to cell death is that the cytotoxins promote movement of the bacteria to bloodstream leading to primary septicemia and septic shock . High bacterial load in the bloodstream and high serum TNFα concentrations have been directly linked to death of patients [37] , [38] . In addition to septic shock , the toxins might also contribute to multiorgan failure . This can include necrosis of lung tissue and liver , a common finding in autopsy patients [28] , [39] . They could also cause progression of the infections out of the bloodstream into the muscle tissue to cause necrotizing fasciitis , another complication of V . vulnificus infections . Overall , the present study demonstrates that , for V . vulnificus isolate CMCP6 , MARTXVv , along with VvhBA , performs an essential role during food-borne V . vulnificus infection after consumption . These toxins have multiple roles including promotion of rapid in vivo growth , destruction of epithelial tissue , causing inflammation through induction of pyroptosis , and possibly causing patient death through tissue destruction in peripheral organs . Similar studies performed in other V . vulnificus clinical isolates will be necessary to determine if findings here with the highly virulent strain CMCP6 will be broadly applicable to all V . vulnificus clinical isolates . In particular , we recently found that the predominance of US clinical isolates from patients with primary septicemia ( represented by strain MO6-24/O ) carry a variant of the MARTXVv toxin that arose by a recombination in the rtxA1 gene with the rtxA gene from Vibrio anguillarum . This recombination likely accounts for an overall 10-fold reduced virulence of MO6-24/O in this animal i . g . infection model compared to CMCP6 [15] . Notably , the loss of a domain of unknown function from the MO6-24/O-type MARTXVv variant did not affect the function of MARTXVv as a cytolysin in vitro [17] suggesting it would likely retain the ability to induce necrotic tissue damage during intestinal infection . However , it is possible that the reduced potency of the toxin variant could impact the relative contribution of the toxin to in vivo growth and tissue damage such that , perhaps , MARTXVv and VvhA could be found to have a more equal contribution to intestinal infection by MO6-24/O and similar strains , although we predict that both toxins would continue to have an additive contribution to virulence . Alternatively , the kinetics of infection could be altered such that critical levels of colonization and/or damage will occur later in the infection cycle . In addition to MO6-24/O and CMCP6-type MARTXVv variants , other variants of the toxin have been described that have undergone more significant changes by horizontal gene transfer and homologous recombination , including the acquisition of the ability to covalently crosslink actin in epithelial cells [15] , [40] . One might predict that these rare variants will have an even more distinct infection profiles when compared to CMCP6 . In any event , results in this study have established that both MARTXVv and VvhA contribute to virulence and provide a baseline for determining if other isolates have similar patterns of disease progression or whether V . vulnificus infection develops differently dependent upon the variant of MARTXVv it expresses . Finally , while it has been shown here that MARTXVv and VvhA are critical to infection , these are very likely not the only important virulence factors necessary for intestinal infection . Most notably , V . vulnificus Biotype 1 can be separated into two distinct evolutionary lineages: a clinical lineage and environmental lineage [41] . We have recently shown that bacteria from both lineages carry genes for both cytolysins [15] , even though strains from the environmental lineage rarely cause clinical infection and are less virulent in mice [9] . Thus , there must be additional V . vulnificus factors to define host selection that have yet to be identified and characterized . These would then work in concert with the cytotoxins perhaps by improving growth in the human intestine during infection or by facilitating colonization of the small intestine by interacting with a human epithelial receptor . Regardless of the nature of this other virulence gene , the importance of the cytotoxins cannot be negated since our work demonstrates that the additive destruction of these toxins is essential to disease progression .
This study was carried out in strict accordance with the recommendations in the United States Public Health Service ( USPHS ) regulations and applicable federal and local laws . The protocol ( Protocol No . 2009-1016 ) was approved by the Northwestern University Institutional Animal Care and Use Committee ( IACUC ) as detailed in methods . All surgery was performed under ketamine-xylazine and isoflurane anesthesia , and all efforts were made to minimize suffering . The strains and plasmids used in this study are listed in Table 1 . Escherichia coli strains used for DNA replication or conjugational transfer of plasmids and Vibrio vulnificus strains were grown in Luria-Bertani ( LB ) . When appropriate , antibiotics were added to media at the following concentrations: kanamycin ( 50 µg/ml ) , rifampicin ( 50 µg/ml ) and chloramphenicol ( 5 µg/ml ) . Bacterial growth in LB was monitored using a Beckman DU530 Spectrophotometer . To inactivate rtxA1 , vvhBA and rtxA1vvhBA , overlapping PCR was applied for the construction of rtxA1 ( HG0901 ) , vvhBA ( HG0902 ) and rtxA1vvhBA ( HG0903 ) deletion mutants [42] ( Table 1 ) . The 9635 bp deleted rtxA1 and the 793 bp deleted vvhBA open reading frame ( ORF ) were ligated with SalI-SacI and XbaI-SacI digested pDS132 [43] forming pHGJ3 and pHGJ4 . To generate the ΔrtxA1 and ΔvvhBA mutants by homologous recombination , E . coli SM10λpir and S17λpir ( containing pHGJ3 and pHGJ4 ) were used as a conjugal donor to V . vulnificus CMCP6 with spontaneous rifampicin resistance . The ΔrtxA1vvhBA double mutant was also generated through conjugation of pHGJ3 to HG0902 ( Table 1 ) . The conjugation and isolation of the transconjugants were conducted using sucrose counter selection previously described [42] . A pCM17 containing luxCDABE and hok/sok plasmid [24] was used for generation of bioluminescent V . vulnificus strains . To create the conjugatable plasmid , 251 bp of oriT DNA from pGP704 was inserted into NheI-SalI digested pCM17 to create pHGJ1 . pHGJ1 was then digested with HindIII followed by religation to inactivate the luciferase genes and create pHGJ2 ( Table 1 ) . CMCP6lux ( HG0905 ) and isogenic rtxA1 ( HG0906 ) , vvhBA ( HG0907 ) and rtxA1vvhBA ( HG0908 ) mutants were generated by conjugal transfer of pHGJ1 and HG0909 and HG0910 were generated by conjugal transfer of pHGJ2 ( Table 1 ) . The roles of the V . vulnificus CMCP6 MARTXVv and VvhA in pathogenesis were examined using a mouse model . Female C57BL/6 mice ( 5–6 weeks old , Harlan , Indianapolis , IN ) were individually anesthetized with an i . p . injection of 100 µl of PBS solution containing 10 µg/ml ketamine and 2 µg/ml xylazine per mouse , i . g . inoculated with 50 µl of 1×106 CFU of the indicated V . vulnificus strains . Images were acquired using an IVIS 100 ( Xenogen Corporation , Alameda , CA ) . During image acquisition , mice were anesthetized using ketamine-xylazine cocktail at each image cycle . All images were acquired at a preset exposure of 20 sec with medium binning and f/stop = 1 so images could be compared over time . Photons per second emitted by each mouse were quantified and analyzed by defining regions of interest ( ROI ) , using the Living Image 1 . 0 software . Severely moribund mice unlikely to survive to the next imaging cycle were euthanized after imaging and counted as non-survivors . To observe the tissue damage in mice small intestine , infected mice were sacrificed at specific time points and 1 cm of ileum immediately adjacent to the cecal-ileal junction was fixed by 10% neutral phosphate buffered formaldehyde solution ( Sigma , St . Louis , MO ) for 16 hr . Histopathology was performed at the Northwestern University Pathology Core Facility . The ileum was embedded in paraffin , and stained with hematoxylin and eosin ( H&E ) . A single immunohistochemical staining procedure was performed to characterize the necrotized cells and to detect the cytokines secretion . Briefly , tissue sections were placed in a 60°C oven overnight for tissue to adhere . The sections were dewaxed in xylene , rehydrated through graded alcohols to water . Antigen retrieval was done by placing the slides in citrate buffer and pressure cooked up to 125°C for 30 sec and gradually reduced to 90°C over 40 min . Slides were then cooled down at room temperature for 20 min and placed in DAKO butter ( DAKO , Carpineteria , CA ) . Then immunostaining for ß–catenin , CD45 , F4/80 , and IL-1ß was performed on a DAKO Autostainer Plus using a DAKO Envision system ( DAKO ) . Sections were first quenched with hydrogen peroxide ( H2O2 ) for 10 min and incubated with primary antibodies for 60 min . Primary rabbit polyclonal antibodies to ß-catenin , CD45 and IL-1ß ( Abcam , Cambridge , UK ) , at 1∶50 dilution , and rat monoclonal antibodies to F4/80 ( Abcam ) , at a 1∶100 dilution , were used . Secondary antibodies were applied at a dilution of 1∶200 for 30 min , followed by incubation with polymer link streptavidin-horseradish peroxidase ( HRP ) reagent and 3 , 3′-diaminobenzidine ( DAB; DAKO ) . The slides were counter-stained with blue Mayer's Hematoxylin and primary antibodies were omitted in negative controls . Then the stained slides were photographed using a Zeiss Axioskop ( MicroImaging , Thornwood , NY ) microscope with Nuance spectral camera ( CRI , Woburn , Mass ) . Mouse colonization assays were performed essentially as described earlier for Vibrio cholerae infection [44] . Briefly , five C57BL/6 female mice ( 5–6 weeks old , Harlan , Indianapolis , IN ) per each group were euthanized by cervical dislocation under anesthesia at 8 or 12 hr after inoculation of the indicated V . vulnificus strains or PBS . After terminal ileum dissection for histology , the liver , spleen and remaining small intestine were dissected . Then it was homogenized in 5 ml ( small intestine and liver ) or 3 ml ( spleen ) of PBS and serially diluted for plate counts of recovered colony forming units ( CFU ) on LB plate containing rifampicin . Mice for which fewer than 10 colonies were recovered from 50 µl of the homogenated extract were plotted below the detection limit line . Recovery of bacteria is reported as a Colonization Index ( Col . Ind . ) calculated as CFUrecovered/CFUinoculated or CFU/organ at logarithmic scale . The remaining homogenates of small intestine was centrifuged at 13400×g for 5 min at 4°C and the supernatant were kept at −80°C . The supernatants were thawed on ice immediately prior to assay . IL-1ß levels in small intestines were determined from homogenated extracts by ELISA ( Enzyme-linked immunosorbent assay , BioLegend , San Diego , CA ) kits according to manufacturer's instruction . Virulence of lux+ V . vulnificus strains was determined in a morbidity assay as previously described [15] and the LD50 for each strain was calculated by the method of Reed and Muench [45] . To examine the cytotoxicity of lux+ V . vulnificus strains , the HeLa cells were grown in Dulbecco's modified Eagle's medium containing 10% fetal bovine serum and seeded in 12 well culture plates to a density of 8 . 5×105 cells per well . After growing overnight at 37°C in 5% CO2 , the monolayer of HeLa cells were infected with lux+ V . vulnificus strains at a multiplicity of infection of 25 and the cytotoxicity was then determined by measuring the activity of LDH in the supernatant at 1 to 5 hr post-infection using a CytoTox 96 Non-Radioactive Cytotoxicity Assay Kit ( Promega , Madison , WI ) according to manufacturer's instructions . All data were graphed and analyzed using GraphPad Prism 4 for MacIntosh Software ( San Diego , CA ) . Statistical significance for LDH assays , growth curve , and ELISA assays was determined in pairwise comparisons using a student t-test . A Mann-Whitney non-parametric t-test comparing means was used for mouse colonization studies . Significance of survival curves was determined using the log-rank test .
|
Vibrio vulnificus causes disease both by infection of wounds from seawater and by consumption of contaminated foods , especially oysters . Wound infection results in necrotizing fasciitis and edema in extremities with mortality of ∼25% as the incidence of septicemia is low . Contaminated food consumption by contrast can lead to highly invasive infections that progress rapidly from an intestinal infection to primary septicemia . Case-fatality rates are ≥50% , with rates as high as 100% in individuals who receive no antibiotic therapy . The aim of this study is to elucidate virulence mechanisms of food-borne infection of the most highly virulent strains of V . vulnificus . We developed a novel intragastric infection model for a highly virulent clinical isolate from Korea in which we can observe the bacterial load in live mice and applied this to study of wild type and strains genetically altered to delete genes for two secreted cytotoxins . Using this model , we show that both the multifunctional-autoprocessing RTX toxin ( MARTXVv ) and the cytolysin VvhA contribute to rapid in vivo growth of bacteria and that the presence of these factors directly correlates with mouse mortality . These exotoxins are then directly linked to intestinal damage and inflammation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"bacterial",
"diseases",
"infectious",
"diseases",
"biology",
"microbiology"
] |
2012
|
Additive Function of Vibrio vulnificus MARTXVv and VvhA Cytolysins Promotes Rapid Growth and Epithelial Tissue Necrosis During Intestinal Infection
|
The main objective of this study was to identify , describe , classify and analyze the scientific health economic evidence of VL-related technologies . A web search of combinations of free text and Mesh terms related to the economic evaluation of visceral leishmaniasis was conducted on scientific publication databases ( Web of Science , Scopus , Medline via the Pubmed and Lilacs ) . A manual search of references lists of articles previously identified by the authors was also included . Articles written in English , Portuguese , Spanish or French were considered suitable for inclusion . Articles that matched the inclusion criteria were screened by at least two researchers , who extracted information regarding the epidemiologic scenario and methodological issues on a standardized form . The initial search retrieved 107 articles , whose abstracts were inspected according to the inclusion criteria leading to a first selection of 49 ( 46% ) articles . After the elimination of duplicates , the list was reduced to 21 ( 20% ) articles . After careful reading and application of exclusion criteria , 14 papers were eligible according to the description , classification and analysis process proposed by the study . When classified by type of economic evaluation , articles were 7 ( 50% ) cost-effectiveness , 5 ( 36% ) cost-minimization , 1 ( 7% ) cost-benefit , and 1 ( 7% ) budget impact . When classified by methodology , studies were mainly nested to clinical-trials ( “piggy back” ) 8 ( 57% ) . Discount rates for outcomes and costs were present in 3 ( 43% ) of the cost-effectiveness studies , and according to WHO's recommendations , the discount rate of 3% was used in all studies . This article showed that health economic evaluations on visceral leishmaniasis used a wide range of technologies and methods . Nevertheless it is important to point out the geographic concentration of studies , which makes their transferability uncertain to different epidemiological scenarios , especially those concerning visceral leishmaniasis caused by Leishmania infantum .
During past decades , health economic evaluations have become increasingly important to the evaluation of new health technologies . Many countries have addressed common issues related to the process of health technology assessment ( HTA ) while elaborating guidelines regarding the process to evaluate such technologies , and the evidence-based decision making has been adopted by health systems around the world and by academia[1 , 2] Nevertheless , health technology assessment and specifically health economic evaluation are still scarce for the so-called neglected tropical diseases ( NTD ) . This is an alarming fact , considering that NTDs present higher burden of disease than some non-communicable diseases[3 , 4] , and mainly affect the poorest regions and populations of the globe[5–7] , to the point of being classified as diseases of poverty . For that reason , supporting an efficient resource allocation process with health economic evidence is imperative , as poor populations are vulnerable to a wide spectrum of diseases , and are assisted by budget restricted health systems . Visceral leishmaniasis ( VL ) , one of the NTDs , is a life threatening infectious disease affecting around 500 , 000 and killing 50 , 000 individuals a year[8–10] . India , Brazil , Sudan , Nepal , Ethiopia and Bangladesh concentrate 90% of cases[8] . Malaria is currently the sole tropical infectious disease responsible for more deaths than VL . Notwithstanding the public health importance of VL , the availability of new treatments is very restricted and the narrow development pipeline of new strategies to manage this disease ( control , diagnostic , treatment ) highlights the importance of evaluating the technologies already on the market and of forecasting the scenarios of new technology incorporation . The objective of this review was to identify , describe and classify the scientific production on economic evaluations of VL interventions . We adopted a similar methodology to the study by Walker and Fox-Rushby[11] for communicable diseases in developing countries .
A web search combining free text and MeSH terms related to the economic evaluation of Visceral Leishmaniasis was used in Web of Science , Scopus , Medline via the Pubmed and Lilacs ( Table 1 ) , without language or publication date restriction . The search was carried out during January of 2013 . A manual search of references lists of articles previously identified by the authors was also included , although the web search was able to cover the entire reference list identified in articles . The aim of the search strategy was to identify economic evaluation studies . An economic evaluation encompasses the study of costs and outcomes related to the use of a technology . The simplest form of evaluation is a cost-minimization analysis , which consists of comparing only the costs related to the use of a new technology compared to an alternative called the compactor . This approach assumes that there is no difference in the outcomes , so the goal is to identify the least costly option . Another type of economic evaluation is called cost-effectiveness analysis in which a comparison of the various technology options is undertaken , and the costs are measured in monetary units , and then aggregated , and outcomes are expressed in natural ( non-monetary ) units , which are called effectiveness of the technology . A similar kind of economic evaluation consists of comparing various options , in which costs are also measured in monetary units and outcomes are measured in a utility units , usually quality-adjusted life years or disability-adjusted life years . One can also conduct an evaluation of the financial impact of the introduction of a technology on the budgets of a government or agency . The study included all articles that explicitly proposed to conduct economic evaluations on visceral leishmaniasis in the title , abstract or objectives . Articles written in English , Portuguese , Spanish or French were considered suitable for inclusion . After analyses by two researchers , studies that did not describe economic evaluations , or did not analyze interventions to control visceral leishmaniasis , editorials , reviews or methodological articles were excluded . All articles selected were evaluated and data were organized on a standardized spreadsheet prepared to collect relevant information on articles , definitions , methods and results . Articles were evaluated considering the country of origin; the payers perspective; study design; technologies evaluated ( table 2 ) ; comparator; cost-effectiveness threshold adoption; outcomes measures . Protocol registration: The current systematic review protocol was registered on the International prospective registry of systematic reviews—PROSPERO and received registration id: 2014:CRD42014007534
The initial search retrieved 107 articles whose abstracts were screened for inclusion criteria , leading to 49 abstracts that were also screened for duplicates . This strategy generated a list of 21 articles that were evaluated according to defined exclusion criteria by at least two researchers , thus resulting in 14 articles[12–25] included for description and analysis , Fig 1 . The analysis regarding country of origin of the 14 studies selected , Fig 2 , showed a concentration of studies in the same five countries responsible for 90% of LV cases . We should highlight that India concentrates almost 7 ( 50% ) of studies and that , regardless of all its specificities , VL in the Americas accounts for only 1 ( 7% ) article published in 1996 . All articles were published from 1996 to 2012 based on data from 1988 to 2011 , Fig 3 . Articles were classified by type of economic study , whether cost-effectiveness , cost-benefit , cost-minimization or cost-of-illness analysis . Considering this classification , cost-effectiveness studies were the most frequent , representing 7 ( 50% ) of total studies Fig 4 . Technologies assessed are presented in Table 2 , and as expected they mainly refer to drugs , albeit the small number of studies evaluating the most recent drugs available . This pattern was also present in the evaluation of diagnostic methods with only two articles assessing the new rapid test for VL diagnosis . It is also important to point out that technologies aiming at the transmission of VL are very under assessed in the literature reviewed , capturing only one cost-of-illness article that evaluated the use of insecticides , and one cost-effectiveness analysis of vaccines that was only possible by the simulation of scenarios regarding the hypothetical intervention . In relation to methods applied , studies were classified in the following categories: clinical-trials ( piggy-back ) [26] , pharmacoeconomic modelling , and others ( i . e . : surveys ) . It is important to point out that 8 ( 57% ) of economic evaluations of VL were trial based Fig 5 . Among the 13 ( 93% ) articles that declared the perspective of the analysis , 5 ( 38% ) adopted the societal perspective and 8 ( 62% ) performed economical evaluation according to the payers’ perspective , 2 ( 25% ) of which with patients or their families considered as payers , and 6 ( 75% ) to the health system . Outcomes were presented as averted Years of Life Lost , 1 ( 7% ) ; patients cured , 1 ( 7% ) ; monetary units , 2 ( 14% ) ; averted deaths , 3 ( 21% ) ; averted disability-adjusted life years ( DALY ) , 3 ( 21% ) ; and “treatments” , 4 ( 28% ) . Most studies presented monetary units as US$ , 13 ( 93% ) ; and one article presented Nepalese Rupies NR , 1 ( 7% ) . Cost-effectiveness threshold was presented by 4 ( 57% ) of the CE studies; three ( 75% ) of these studies adopted the WHO choice criteria of 1 to 3 x GDP per capita/ averted DALY[27]; and 1 ( 25% ) study adopted a threshold of U$ 25/ averted DALY . It is important to note that only one study presented a CE acceptability curve . The only three ( 43% ) CE studies that applied discount rates to cost or outcomes chose a rate of 3% a year . Sensitivity analyses were performed on 7 ( 50% ) studies , 72% of which used multivariate sensitivity analyses . Modelling was used by 29% of all selected studies , with three ( 75% ) based on decision trees and one ( 25% ) based on Markov chain models . Table 3 presents the outcomes , and inflation adjusted costs for the most cost-effective ( or cost-saving ) intervention of each study . The inflation adjusted costs were also purchasing power parity converted considering the 2013 PPP conversion rate[28] . All articles were evaluated following the Consolidated Health Economic evaluation reporting standards ( CHEERS ) statement[29] , and the result is presented as supplementary material . It’s interesting to note that the majority of studies lacked information on time horizon 12 ( 85 , 7% ) ; measurement of effectiveness 12 ( 85 , 7% ) ; measurement and valuation of preference based outcomes 13 ( 92 , 9% ) ; characterizing uncertainty 12 ( 85 , 7% ) ; characterizing heterogeneity 12 ( 85 , 7% ) ; and conflicts of interest 12 ( 85 , 7% ) .
Visceral leishmaniasis is still an important infectious disease in many countries , especially in developing ones , so its control is of great public health importance . The development of new technologies is imperative in order to properly address VL in order to control epidemics and reduce its impact on society[34] . The present review has showed that health-economic studies , which are an essential part of the health technology assessment and incorporation process , were not able to overcome gaps in knowledge of strategies to deal with such a debilitating disease . It is also important to underscore that the majority of studies accessed by this article did not consider the societal perspective to guide the evaluation; they mainly adopted the payers' perspective , which does not necessarily express the entire dimension of the health intervention evaluated . Transmission control was only assessed by three studies , which may reflect the difficulty of evaluating these strategies due to the interval between intervention and epidemiologic impact , or the difficulty of linking intervention and impact . Despite the representation of endemic countries on the investigation teams , the majority of articles were produced with foreign cooperation , suggesting that the capillarity of the techniques used in economic evaluations of health interventions are still a challenge for developing countries . Most recent treatments for VL ( ex . miltefosine , and paramomycin ) were evaluated only a few times , and should be evaluated in different epidemiological scenarios . Future studies should consider a longer time horizon , so that the infectious disease characteristics and peculiarities of visceral leishmaniasis could be better expressed and accounted for .
|
Visceral leishmaniasis ( VL ) , also known as kala azar , is a neglected tropical disease caused by parasitic protozoa of the genus Leishmania . VL is related to poverty and its consequences , which leads to its status of neglected disease . For that reason , cost-effective forms of diagnoses and treatment are very important and still needed . This research aimed at a better understanding of the publications about the technologies currently available , from the standpoint of their economic value . For that purpose , we conducted a systematic review of the literature in order to identify the papers that conducted economic evaluations of technologies used in VL . We initially retrieved 107 articles , which were inspected according to specific guidelines for systematic reviews . After that process , 14 articles matched the inclusion criteria in our review . We classified those studies according to the type of economic evaluation they made , and the methodology used in each one . We found evaluations about a variety of technologies , but the studies were geographically concentrated in Asia , more specifically in India . This concentration is not good because the disease also affects other continents and it is not possible to transfer the economic evaluation from one country or epidemiologic scenario to another .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Health Economic Evaluations of Visceral Leishmaniasis Treatments: A Systematic Review
|
Although positive incentives for cooperators and/or negative incentives for free-riders in social dilemmas play an important role in maintaining cooperation , there is still the outstanding issue of who should pay the cost of incentives . The second-order free-rider problem , in which players who do not provide the incentives dominate in a game , is a well-known academic challenge . In order to meet this challenge , we devise and analyze a meta-incentive game that integrates positive incentives ( rewards ) and negative incentives ( punishments ) with second-order incentives , which are incentives for other players’ incentives . The critical assumption of our model is that players who tend to provide incentives to other players for their cooperative or non-cooperative behavior also tend to provide incentives to their incentive behaviors . In this paper , we solve the replicator dynamics for a simple version of the game and analytically categorize the game types into four groups . We find that the second-order free-rider problem is completely resolved without any third-order or higher ( meta ) incentive under the assumption . To do so , a second-order costly incentive , which is given individually ( peer-to-peer ) after playing donation games , is needed . The paper concludes that ( 1 ) second-order incentives for first-order reward are necessary for cooperative regimes , ( 2 ) a system without first-order rewards cannot maintain a cooperative regime , ( 3 ) a system with first-order rewards and no incentives for rewards is the worst because it never reaches cooperation , and ( 4 ) a system with rewards for incentives is more likely to be a cooperative regime than a system with punishments for incentives when the cost-effect ratio of incentives is sufficiently large . This solution is general and strong in the sense that the game does not need any centralized institution or proactive system for incentives .
Even though society is based on cooperation , achieving cooperation in social dilemmas is still a big challenge . The free-rider problem , for example , hinders cooperation . Many studies have addressed this problem , and the methods proposed for solving it include giving players sufficient ability to remember their direct [1] or indirect experiences [2] . The idea is that this additional information generates cooperation through direct or indirect reciprocity . Other research has attempted to solve the problem by assigning tags [3] , reputations [4] , spatial structures [5] or networks [6] to players in the game . A third approach is to give players choices other than simply whether or not to contribute . A drawback of this approach , however , is that there may be a loner , i . e . , a player who does not participate in the game [7 , 8] , or a joker , i . e . , a destroyer who damages the public good [9] . Some researchers have devised another sort of game that promotes cooperation by giving players incentives explicitly . Important incentives for the evolution of cooperation are rewards and punishments as they tend to capture strong views of human nature [10 , 11] . The approach we took integrates positive incentives such as rewarding and negative ones such as punishing into a system for promoting cooperation . A meta-analysis of reviews on reward and punishment systems using a common framework [12] revealed that this approach is still controversial . Here , we tackle this issue by focusing on three perspectives: ( 1 ) the contrast between an individual incentive system and a centralized institutional one , ( 2 ) an incentive-integrated punishment and reward system , and ( 3 ) incentives on a meta-level . First , many studies have focused on either individually-dealt-with punishments or rewards . While some studies [13–16] have shown that a costly punishment can effectively achieve cooperation , others [17–21] have shown just the opposite . Some researchers claim that the findings of peer-punishment studies may not be broadly applicable to modern human societies , because rewards and punishments are typically carried out by rules-bound institutions [22 , 23] rather than by individuals . The reason might be a difficulty of establishing and maintaining such a peer-punishment system because it does not involve a nomocracy , i . e . , a proactive or ex-ante commitment . Second , should a free-rider be punished and/or should a contributor be rewarded ? Experimental studies [24 , 25] have indicated that rewards and punishments induce similar levels of cooperation when the incentive is very large . Economists have used experimental games to study the effects of positive and negative incentives ( i . e . , rewards and punishments ) on the propensity to collaborate [26] . A theoretical study [27] showed that a punishment is more effective than a reward because these incentives have an asymmetric relationship with one another . In other words , punishments are not needed once cooperation is established , and thus , cooperators do not need to pay the costs of punishment . On the other hand , attaining rewards requires participants to pay costs by following a cooperative strategy . The threat of a strong punishment can achieve cooperation at a very low cost [28] . For an intermediate level of incentive , however , although punishments can induce greater cooperation than rewards [25] , they cannot do so consistently [29] . Moreover , cooperation easily breaks down if both forms of incentive are removed [30] . Compared with the numerous studies on punishments , there have been relatively few on rewards [12 , 24 , 25 , 29] . For example , an experimental study [31] explored the situation in which unkind newcomers are strictly exploited and found that indirect rewards are effective in such situations . Third , in the approach we took , the focus is on meta-level incentives . When the incentive for cooperation is either a punishment or reward , there is still the second-order free-rider problem . The effort made to maintain a cooperative society is a cost that must be defrayed by someone . Generally , a player who contributes to a game but never defrays the cost for providing incentives is more evolutionarily adaptive than one who contributes to the game and does pay the cost . This means that eventually no one defrays the cost of maintaining the incentive system . The second-order free-rider problem can come down to the problem of costly incentives [32] . One solution is to implement a second-order incentive system . The pioneering work on meta-level incentives was performed by Axelrod [33] . He attempted to evolve cooperation by imposing a second-order punishment on those who do not impose a first-order punishment when one is called for . His model linked punishments against non-punishers with punishments against non-cooperators . This assumption , that first-order incentives and second-order ones are linked , or FO-SO-linkages , is critical for our study . Yamagishi and Takahashi [34] were the first to point out the linkage issue and demonstrated that a cooperative regime emerges if it is assumed that players have linkages between cooperation and first-order incentives , or C-FO-linkages . Related studies have developed models that assume C-FO-linkages have been analyzed [35 , 36] . The existence of a C-FO-linkage , however , is still not a foregone conclusion [37] . Some experimental studies [38 , 39] showed that sanctions enhance norm and cooperative behavior . An experimental study [40] , conversely , concluded there is a negative correlation between cooperative behaviors in prisoner’s dilemma games and refusal behaviors in ultimatum games . Moreover , an analysis of large-scale panel data of Germany [41] concluded that rewards and punishments have no relationship . Another experiment [42] found that cooperation is not correlated with norm-enforcing punishments . An experimental study [37] , on the other hand , showed a significant correlation between cooperation and punishment; however , they unostentatiously admitted that their result was insufficient evidence for the existence of C-FO-linkages . The C-FO-linkage issue is a relationship between two behaviors which differ qualitatively . Considering this point , the relationship between first-order incentives and second-order incentives is an alternative issue because they are both incentives . Kiyonari and Barclay [43] focused on the FO-SO-linkage and showed its existence in their experiments on one-shot public good games . Their study opened the door on analyses of models that assume FO-SO-linkages . Hilbe et al [44] experimentally showed the rationality of a second-order punishment in an authorized sanction system . We , in this paper , test a model that assumes a FO-SO-linkage in a peer-to-peer incentive system . We have developed a model of a meta-incentive game ( MIG ) that has second-order incentives , i . e . , incentives for other players to provide incentives . This analytical model can describe the carrot-and-stick issue uniformly and comparatively . The model targets an individual incentive system that integrates a positive side ( reward ) and a negative side ( punishment ) [45] . The incentive we consider is an ex-post type applied after players engage in donation games , and thus , no centralized institution with incentives is needed . An institution requires an ex-ante commitment among players . Players should decide whether or not they will participate in the institution before playing the donation games [23 , 46] . In order to resolve the second-order free-rider problem , we suppose that there are three types of players in the MIG , i . e . , a non-cooperative incentive-non-provider as a first-order free-rider , a cooperative incentive-non-provider as a second-order free-rider , and a cooperative incentive-provider , and we will explore the conditions under which cooperative incentive-providers survive .
MIG players first play donation games and then provide incentives in answer to their actions in the games . Incentives are provided not only for or against the others’ cooperative or non-cooperative actions but also for the others’ incentive behaviors or lack thereof on third-party players . Players are divided into three sorts of strategist: a cooperative incentive-provider ( CI ) , a cooperative incentive-non-provider ( CN ) , and a non-cooperative incentive-non-provider ( NN ) . Fig 1 shows an illustration of the MIG . The MIG consists of three stages ( games ) : a donation game ( DG ) , a first-order incentive game ( FIG ) , and a second-order incentive game ( SIG ) . Note that each player has perfect information , so each one knows all the players’ actions . The population is infinitely large and well-mixed . The frequencies of the three strategies follow replicator dynamics describing a natural law whereby the higher one’s payoff is the more frequent one’s strategy becomes . Let x , y , and z be the frequencies of CI , CN , and NN , respectively . Naturally , x+y+z = 1 . The equations are formulated as x ˙ = x ( U C I - U ¯ ) , y ˙ = y ( U C N - U ¯ ) , z ˙ = z ( U N N - U ¯ ) , ( 1 ) where UCI , UCN , UNN , and U ‾ are , respectively , the average payoffs of CI , CN , NN , and all the players . U ‾ is given by U ¯ = x U C I + y U C N + z U N N . ( 2 ) Now let us describe the parameter notations needed to calculate a player’s ( expected ) payoff . Let c be the cost of donation , b be the receiver’s benefit , F1 be the fine imposed as a first-order punishment , P1 be the cost of a first-order punishment , A1 be the amount of a first-order reward , R1 be the cost of a first-order reward , FP be the fine for freeriding for the first-order punishment , PP be the cost for freeriding for the first-order punishment , AP be the amount of the reward for the punisher , RP be the cost of the reward for the punisher , FR be the fine for freeriding for giving a reward , PR be the cost for freeriding for giving a reward , and AR be the amount of rewarding a rewarder , and RR be the cost of rewarding a rewarder . All these values should be non-negative constants . We will avoid analytical difficulties due to the usage of many parameters by defining a simple meta-incentive game ( S-MIG ) using two parameters: the incentive cost-effect ratio ( μ ) , which represents the proportion of a fine or award that incentive-receivers should pay or receive relative to its cost , and the discount factor of costs on the level of incentive ( δ ) , where μ = F 1 P 1 = F P P P = F R P R = A 1 R 1 = A R R R = A P R P , and δ = P 1 c = R 1 c = P P P 1 = R R R 1 = P R P 1 = R P R 1 . We assume that μ > 1 and 0 < δ < 1 . We can set c = 1 without loss of generality . An S-MIG is perfectly described by a duplet ( μ , δ ) . Finally , before analyzing our model , we define all 24 possible configurations of MIG in Fig 2 . For example , the P-type MIG has only first-level punishments . In this type , players can give or receive neither a first-level reward nor a second-level incentive . The PR type has first-level punishments for non-cooperators and second-level rewards for punishers . We explore the conditions under which a cooperative equilibrium ( x > 0 ) emerges by analyzing the replicator dynamics on different types of S-MIG . As shown in Methods , the dynamics of S-MIGs can be classified into four groups . Fig 2 illustrates the existence condition for the basin of attraction and the local stabilities on the point ( x , y , z ) = ( 1 , 0 , 0 ) of all types , and Fig 3 shows the phase portraits of the representative S-MIGs on a 2-dimensional simplex . We identified certain features of the second-order free-rider problem . First , as the second-order free-rider problem warns , cooperation cannot be maintained in any system with only first-order incentives . Similarly , cooperation does not arise even if the system has second-order incentives but no second-order incentives for first-order rewards . This is because rewarding others is equivalent to a prisoner’s dilemma game or a donation game , so incentive-providers as well as cooperators diminish over time . Second , because of the neutral drift effect , no system without a reward side can keep its position when cooperation dominates . This is another aspect of the second-order free-rider problem . These results reveal two important facts about reward systems: a second-order incentive system for first-order rewards is better than a non-incentive system on the reward side for promoting cooperation , and a system without second-order incentives for first-order rewards is worse than the non-incentive system . Third , any system with second-order incentives for first-order rewards can produce a stable cooperative regime under specific conditions . To do so , a certain number of cooperative incentive-providers are needed . If their numbers are small , they cannot survive . Fourth , the conditions under which a cooperative regime emerges depend on the system . A system with second-order rewards has the strictest condition , and the conditions become less stringent for a system with second-order punishments and a system with second-order both rewards and punishments . Finally , the condition on the frequency of incentive-providers under which a cooperative regime can be sustained also depends on the system . When the cost-effect ratio of incentives is sufficiently large , the lower limit of the frequency of incentive-providers in a system with second-order rewards for first-order incentives is lower than that in a system with second-order punishments for first-order incentives .
What can resolve the second-order free-rider problem ? Sigmund et al [23] achieved stabilizing cooperative regimes by using pool punishments instead of peer punishments . Is it possible to maintain such regimes without any proactive institution ? Our model demonstrates that assuming first-order and second-order incentives are linked can lead to a solution without any social costs or a punishment fund . The assumed linkage means that individuals who are willing to provide incentives would automatically provide meta-incentives as well . The consequences are that although the model allows second-order free riders , it does not allow third-order free riders . ( If they were allowed , they would again destabilize cooperation . ) Moreover , efficiency is traded for stability in Sigmund et al’s model , because individuals pay for a punishment fund without as yet knowing who the free riders to be punished are [23] . In cotrast , our model can search for an efficient incentive level for maintaining cooperative regimes , because individuals can reactively incur costs for incentives with knowing who is to be given an incentive . Thus , one of the implications of the linkage assumption is that a more efficient incentive system for stabilizing cooperation would be an intermediate of the traditional peer and pool incentive systems . We note that a similar amount of cooperation could also be achieved if there was a linkage between cooperation and first-order incentives ( C-FO linkage , see Methods ) . This linkage means an alternative model with two strategies , i . e . , defectors and cooperators who automatically also provide first-order incentives . As shown in Methods , our model can indeed cover a case that assumes C-FO linkages instead of FO-SO linkages in specific parameters . For our model incentives for reward are necessary for cooperative regimes . If players play in a non-incentive system on the reward side , a punishment function does not work when cooperation is achieved . That is to say , they cannot respond to an invasion of neutral mutants who do not provide incentives . As a result , cooperation suddenly collapses . This is why Axelrod’s simulation [33] cannot keep a cooperative regime for a long time [47–49] . Therefore , if players play in a non-incentive system on the reward side , another mechanism is needed , e . g . , a social vaccine proposed by Yamamoto and Okada [50] , to maintain cooperative regimes . How about a system with first-order reward and without second-order incentives for the reward ? Here , worse comes to worst because it becomes free from the possibility of staying a cooperative regime temporally . When a cooperative regime is achieved , players do not need their punishment functions , and thus , only the first-order reward function works . As the second-order free-rider problem indicates , cooperative incentive-non-providers beat cooperative incentive-providers because they don’t bear the burden of paying for rewards . Second-order incentives for first-order rewards are necessary for achieving and maintaining robust cooperative regimes and to resolve the second-order free-rider problem . That is , a mechanism is needed to make it beneficial for a player to give a first-order reward . Assuming that players who tend to provide incentives for other players’ cooperative or non-cooperative behaviors also tend to provide incentives for their incentive behaviors , the second-order free-rider problem can be completely resolved without any third-order or higher ( meta ) incentive . In our model , moreover , incentives are performed ex-post and individually , and thus the system does not need a centralized institution or ex-ante commitment . Many studies on non-meta-level incentives have shown that punishment is more effective than a reward . We have identified a possible explanation as to why people prefer second-order rewards to second-order punishments . Kiyonari and Barclay’s experimental study [43] supports this—they found that people readily provided second-order rewards toward those who rewarded cooperators while they did not administer second-order punishments to non-punishers because the reward systems were more easily supported by higher order incentives and were thus more likely to persist . Which incentive should a designer of an MIG choose ? If the cost-effect ratio of incentives is sufficiently large , even a handful of cooperative incentive-providers can beat non-cooperative incentive-non-providers if the designer uses a system with rewards for incentives instead of a system with punishments for incentives . Our work differs from Sasaki et al’s model of integrating rewards and punishments , which was designed for an institutional system with a compulsory entrance fee and thus no option of second-order free riders [51] . Kendal et al [52] analyzed a model of second-order peer rewards for punishers , but did not consider rewards for cooperators who contribute in the game . Our paper is a pioneering analysis of MIGs; as such , we only dealt with a minimum deviation and analyze pro-social incentives and left anti-social punishments [53] and anti-social rewards as topics for future study . Our model assumes an infinitely well-mixed population , and this assumption should be loosened in the future . Szolnoki and Perc [54] compared the effect of a reward with that of a punishment in a spatial public goods game . Chen and Perc [55] studied the optimal distribution of institutional incentives in a public goods game on a scale-free network . The asymmetry of the effects of rewards and punishments might be evident in the cost-effect ratio . Although it may be natural that a fine handed out as punishment should be larger than its cost , can the reward be larger than its cost ? We believe that this balances out the risk of second-order free-riders emerging . Of course , we leave open the possibility of incorporating asymmetric reward and punishment effects as a future extension of a system .
The expected payoffs of the players are U C I = b ( x + y ) - c - [ ( x + y ) R 1 + z P 1 ] + x A 1 - [ ( x + y ) x R R + ( x + y ) ( y + z ) P R + z x R P + z ( y + z ) P P ] + x [ ( x + y ) A R + z A P ] , U C N = b ( x + y ) - c + x A 1 - x [ ( x + y ) F R + z F P ] , U N N = b ( x + y ) - x F 1 - x [ ( x + y ) F R + z F P ] . ( 3 ) Let us explain the terms of UCI . The first term represents the benefit of donation , whereas the second term gives the cost of donation ( because one is a cooperator ) in the DG stage . The third term represents the costs of the first-order incentivization: rewarding a cooperator and punishing a non-cooperator . The fourth term represents the first-order reward for cooperating . The fifth term represents the costs of the second-order incentivization and consists of four parts , i . e . , rewarding those who have rewarded a cooperator , punishing those who have not rewarded a cooperator , rewarding those who have punished a non-cooperator , and punishing those who have not punished a non-cooperator . Finally , the last term describes the second-order rewards for rewarding a cooperator and punishing a non-cooperator . Similar explanations can be applied to the other expected payoffs , UCN and UNN . Using Eqs ( 1 ) , ( 2 ) , and ( 3 ) , the replicator equations are calculated as x . = x f 1 , y . = y f 2 , and z . = z f 3 , where f 1 = - c z - ( y + z ) [ ( x + y ) R 1 + z P 1 ] + x z ( A 1 + F 1 ) - ( y + z ) [ ( x + y ) x R R + ( x + y ) ( y + z ) P R + z x R P + z ( y + z ) P P ] + x [ ( x + y ) ( y + z ) ( A R + F R ) + z ( y + z ) ( A P + F P ) ] , f 2 = - c z + x [ ( x + y ) R 1 + z P 1 ] + x z ( A 1 + F 1 ) + x [ ( x + y ) x R R + ( x + y ) ( y + z ) P R + z x R P + z ( y + z ) P P ] - x [ ( x + y ) x ( A R + F R ) + z x ( A P + F P ) ] , f 3 = c ( x + y ) + x [ ( x + y ) R 1 + z P 1 ] - x ( x + y ) ( A 1 + F 1 ) + x [ ( x + y ) x R R + ( x + y ) ( y + z ) P R + z x R P + z ( y + z ) P P ] - x [ ( x + y ) x ( A R + F R ) + z x ( A P + F P ) ] . ( 4 ) We prove that there is no equilibrium at any internal point ( x , y , z ) in S-MIG in the following subsection , and thus , here , it is enough to conduct an analysis of the borders . Moreover , there is no equilibrium at any point on the line x = 0 , except two corners ( ( y , z ) = ( 0 , 1 ) , ( 1 , 0 ) ) . This is because , y . = − y ( 1 − y ) < 0 on the line x = 0 , and thus , y always decreases . Hence , we will only deal with the x . function on the lines y = 0 and z = 0 . On those two lines , that function is calculated as x . ∣ y = 0 = x ( 1 − x ) f ( x ) and x . ∣ z = 0 = x ( 1 − x ) g ( x ) , where f ( x ) = - 1 - x R 1 - ( 1 - x ) P 1 + x ( A 1 + F 1 ) - x 2 R R - x ( 1 - x ) P R - x ( 1 - x ) R P - ( 1 - x ) 2 P P + x 2 ( A R + F R ) + x ( 1 - x ) ( A P + F P ) , g ( x ) = - R 1 - x R R - ( 1 - x ) P R + x A R + x F R . ( 5 ) In order to exemplify how to analyze each type of the S-MIG , we deal with the RB type as a representative and derive their equations of f ( x ) and g ( x ) . In the type , F1 , P1 , FP , PP , AP and RP should be zero because there has no incentive system on the punishment side . Moreover , using c = 1 and definitions of μ and δ , R1 = δ , A1 = μδ , RR = PR = δ2 and AR = FR = μδ2 . Substituting them into f1 in Eq ( 4 ) , we get x ˙ = x [ - z - δ ( y + z ) ( x + y ) + μ δ x z - δ 2 ( y + z ) ( x + y ) + 2 μ δ 2 x ( x + y ) ( y + z ) ] . Therefore , we derive Eq ( 5 ) of the RB-type S-MIG as x ˙ | y = 0 = x ( 1 - x ) ( - 1 - δ x + μ δ x - δ 2 x + 2 μ δ 2 x 2 ) , and x ˙ | z = 0 = x ( 1 - x ) ( - δ - δ 2 + 2 μ δ 2 x ) . Table 2 shows the equations for x . ∣ y = 0 and x . ∣ z = 0 for each type of S-MIG . Using Eq ( 5 ) , we can calculate an existence condition for the basin of attraction on the point ( x , y , z ) = ( 1 , 0 , 0 ) . If x . ∣ x = 1 , y = 0 > 0 and x . ∣ x = 1 , z = 0 > 0 are satisfied , the point ( x , y , z ) = ( 1 , 0 , 0 ) is asymptotically stable , and thus , a cooperative regime emerges . The dynamics of each type of S-MIG are classified into four groups . In what follows , we will deal with one representative type in each group . The remainder can be similarly derived . The R , P+R , PP+R , PR+R , and PB+R types belong in the first group . Each type of this group has a globally stable point ( x , y , z ) = ( 0 , 0 , 1 ) , so cooperation is never achieved regardless of the values of ( μ , δ ) . x . ∣ z = 0 < 0 is satisfied; thus , ( 1 , 0 , 0 ) is unstable . Therefore , the only stable equilibrium point is ( 0 , 0 , 1 ) , as shown in Fig 3 ( A ) . The P , PP , PR , and PB types belong in the second group . The whole line z = 0 consists of fixed points because x . ∣ z = 0 = 0 is always satisfied . Each type of this group has two patterns of behavior depending on the values of ( μ , δ ) . In one , all the fixed points on the line z = 0 are unstable , and thus , there is a globally stable point ( 0 , 0 , 1 ) , as in the first group . In the other , some of the fixed points are stable if x > 1 μ δ when μ > 1 δ , as shown in the following subsection . Note that this behavior also satisfies an existence condition for the basin of attraction on the point ( x , y , z ) = ( 1 , 0 , 0 ) on y = 0 . Let us verify the PR case as a representative example . ∂ f 2 ∂ 2 x ( x ) = 2 δ 2 ( 1 − μ ) < 0 and ∂ f ∂ x ( 1 ) = μ δ ( 1 − δ ) + δ ( 1 + δ ) > 0 prove that f ( x ) is an increasing function . Moreover , f ( 1 ) = −1+μδ > 0 implies that the point ( x , y , z ) = ( 1 , 0 , 0 ) is asymptotically stable . Even in the second pattern , however , the point ( 1 , 0 , 0 ) is not stable for a long time . This is because , the whole line z = 0 consists of fixed points , and thus , neutral drift is possible . Occasionally , x moves to an unstable equilibrium , and this type eventually reaches ( 0 , 0 , 1 ) . The phase diagram of the PP-type S-MIG is shown in Fig 3 ( B ) . The third group , consisting of types which include either the RP or RR , and P+RB , PP+RB , PR+RB , and PB+RB ( Full ) types , has two patterns of behavior depending on the values of ( μ , δ ) . In one , there is a globally stable point ( 0 , 0 , 1 ) , as in the first group . In the other , another locally asymptotically stable point ( 1 , 0 , 0 ) can exist . This group has three existence conditions for the basin of attraction on the point ( x , y , z ) = ( 1 , 0 , 0 ) : μ > 1 + 1 δ for the types which include RR , μ > 1 δ for the types which include RP , and μ > 1 + δ 2 δ for the P+RB , PP+RB , PR+RB , and PB+RB ( Full ) types . Let us examine the PB+RB ( Full ) -type S-MIG as a representative type . Here , x . ∣ z = 0 = δ x ( 1 − x ) ( 2 μ δ x − δ − 1 ) ; hence , the dynamics on the line z = 0 , on one hand , are bistable and x = 1 + δ 2 μ δ is a fixed point that separates the two basins of attraction when μ > 1 + δ 2 δ is satisfied . On the other hand , the dynamics on y = 0 depends on f ( x ) . ∂ f ∂ x ( x ) = 2 μ δ ( 1 + δ ) > 0 shows that x . ∣ y = 0 is an increasing function . f ( 0 ) < 0 and f ( 1 ) = −1−δ−δ2+2μδ ( 1+δ ) > 0 when the existence condition for the basin of attraction on the point ( x , y , z ) = ( 1 , 0 , 0 ) is satisfied . Therefore , the dynamics on y = 0 are also bistable , and x = 1 + δ + δ 2 2 μ δ ( 1 + δ ) is a fixed point that separates the two basins of attraction . Fig 3 ( C ) shows the phase diagram of the Full-type S-MIG . The final group , consisting of only the RB type , is the same as the third except for the direction of the dynamics in the internal space of the basin of attraction for cooperation ( see Fig 3 ( D ) ) . In this type , the unstable equilibrium point on the line z = 0 is a source while those in the third group are saddles . Likewise , the unstable equlibrium point on y = 0 in the RB type is a saddle , while those in the third group are sources . Using the analytical method of the following subsections , we can calculate the eigenvalues of the matrices derived by linearization of the dynamics around the equilibrium point . The eigenvalues of the equilibrium on the line z = 0 are 1 − δ 2 and ( 1 − 1 + δ 2 μ δ ) δ ( 1 + δ ) , and both are positive . Local stability theory says that an equilibrium with two positive eigenvalues is unstable and an equilibrium with one positive eigenvalue and one negative eigenvalue is a saddle . We can verify that the types of the third group have inverse stabilities . First , we deal with the Full-type S-MIG . Let λ1 and λ2 be the eigenvalues of the matrix derived by linearization of the dynamics around the equilibrium point ( x* , y* , 0 ) on the line z = 0 . λ1 λ2 = −y* δ2 ( 1+δ ) < 0 . Moreover , in the case of the RR-type S-MIG , λ1 λ2 = −x* y* δ2 < 0 . The other cases are omitted . Finally , we compare the lower limits of x of the basins of attraction for cooperation ( x , y , z ) = ( 1 , 0 , 0 ) on y = 0 . Let fD ( x ) and xD be x . ∣ y = 0 x z and the lower limit of x of the basin of attraction on the line y = 0 in the D-type S-MIG for D ∈ {P , R , … , PB+RB} . Basically , the more complex the type is , the lower its lower limit of x becomes . For example , we will prove xP+RB < xP+RR . fP+RB ( x ) = −1−δ+2μδx−δ2 x+2μδ2 x2 and fP+RR ( x ) = −1−δ+2μδx−δ2 x2+μδ2 x2 are in Table 2 . ∂ f P+RB ∂ x ( x ) > 0 and ∂ f P+RR ∂ x ( x ) > 0 imply that both functions are increasing . Note that fD ( xD ) = 0 . fP+RB ( xP+RR ) = δ2 xP+RR[xP+RR ( 1+μ ) −1] and f P+RR ( 1 1 + μ ) < 0 then fP+RB ( xP+RR ) > 0 . Therefore , xP+RB < xP+RR . Moreover , some equivalence relations can be derived . For example , let us compare xRR with xRP . fRR ( x ) and fRP ( x ) are increasing functions because their partial derivatives are positive in 0 < x < 1 . fRR ( xRP ) = δ2 xRP ( 1−2xRP ) and f RP ( 1 2 ) = ( μ − 1 ) δ ( 2 + δ ) 4 − 1 are derived . Therefore , if μ > 4 δ ( δ + 2 ) + 1 , then x RP < 1 2 and xRR < xRP . μ > μ 0 ⇔ x R < x P , μ > μ 10 ⇔ x PR < x PP ⇔ x RR < x RP , μ > μ 11 ⇔ x P + RR < x P + RP ⇔ x PR + R < x PP + R , μ > μ 12 ⇔ x PP + RR < x PP + RP ⇔ x PR + RR < x PR + RR ⇔ x PR + RR < x PP + RR ⇔ x PR + RP < x PP + RP , μ > μ 13 ⇔ x PB + RR < x PB + RP ⇔ x PR + RB < x PP + RB , where μ 0 = 1 + 2 δ , μ 10 = 1 + 4 δ ( 2 + δ ) , μ 11 = 1 + 4 δ ( 4 + δ ) , μ 12 = 1 + 2 δ ( 2 + δ ) , μ 13 = 1 + 4 δ ( 4 + 3 δ ) , and μ0 > μ10 > μ11 > μ12 > μ13 . The MIG assumes FO-SO-linkages . In this subsection , we analyze the replicator dynamics of a model that assumes C-FO-linkages instead of FO-SO-linkages . Accordingly , cooperators automatically provide first-order incentives , and thus , there is no CN , and no second-order incentive is needed in the MIG . When y is set to zero and all the parameters for the second-order incentives are also zero ( FP = PP = AP = RP = FR = PR = AR = RR = 0 ) , this new version , or an incentive game ( IG ) , can be regarded as a model of the C-FO-linkages . We denote the three possible IG configurations as the P-type , R-type , and P+R type . The replicator equation of IG is x ˙ = x ( 1 - x ) [ ( A 1 + F 1 - R 1 + P 1 ) x - ( c + P 1 ) ] . We can devise a simple incentive game ( S-IG ) by using μ , δ , and c = 1 . All S-IG types have two patterns of behavior depending on the values of ( μ , δ ) . In one , there is a globally stable point ( x , z ) = ( 0 , 1 ) . In the other , another locally asymptotically stable point ( x , z ) = ( 1 , 0 ) can exist . The existence condition for the basin of attraction at the stable point ( 1 , 0 ) is μ > 1 δ for a P-type , μ > 1 + 1 δ for an R-type , or μ > 1 + δ 2 δ for a P+R-type S-IG . In this subsection , we prove that there is no equilibrium at any internal point on the 2-dimensional simplex Δ = { ( x , y , z ) : x , y , z ≥ 0 , x+y+z = 1} . Assume that ( x* , y* , z* ) is an internal equilibrium . On that point , Eq ( 4 ) should be 0 . This is because x* ( y* , z* ) is not zero , and thus , the function x . ( y . , z . ) can be divided by x* ( y* , z* ) . Using y . ∣ y = y * y * = z . ∣ z = z * z * = 0 , one gets x * = 1 A 1 + F 1 . Similarly , using x . ∣ x = x * x * = y . ∣ y = y * y * = 0 , one gets z * ( R 1 + P R - P 1 - P P + R R - P R - R P + P P + A P + F P - A R - F R A 1 + F 1 ) = R 1 + P R + R R - P R - A R - F R A 1 + F 1 ( 6 ) Table 3 shows the equations and solutions of z* in Eq ( 6 ) for all 24 types of S-MIG . The solutions of z* in the PP and RP types are not unique when μδ = 1 . At that time , however , x* must be 1 . Therefore , there is no equilibrium for both types when 0 < δ < 1 . If δ = 1 is permitted , the PB and RB types have internal equilibria at all points on x = x * = 1 μ . However , they are not stable . We will prove this and deal with the PB type as a representative . When δ = 1 , Eq ( 1 ) in the PB type is x ˙ = x z ( μ x - 1 ) ( 3 - 2 x ) , y ˙ = y z ( μ x - 1 ) ( 1 - 2 x ) . Now let us analyze the local stability of the point ( x , y , z ) around the equilibrium point ( x* , y* , z* ) . Let εx = x−x* , εy = y−y* and ɛ = ( εx , εy ) T , where T means transposition . As a result of the linearization of the dynamics , d ɛ d t = M ɛ where M = ( μ x * z * ( 3 - 2 x * ) 0 μ y * z * ( 1 - 2 x * ) 0 ) , because μx*−1 = 0 . The eigenvalues of M are λ = 0 , z* ( 3−2x* ) . These are non-negative , and thus , any internal equilibrium is non-isolated and unstable . The eigenvalues in the case of the RB type are λ = 0 , z*+2 ( x*+y* ) ( y*+z* ) , and thus , the same conclusion is reached . In this subsection , we analyze the local stabilities of the line z = 0 in the P , PP , PR , and PB types of S-MIG . Here , we deal with the P-type S-MIG as a representative . Eq ( 1 ) becomes x ˙ = x z [ μ δ x - δ ( y + z ) - 1 ] , y ˙ = y z [ μ δ x + δ x - 1 ] . As is shown in the previous subsection , the linearization of the dynamics around the equilibrium point ( x , y , z ) = ( x* , y* , 0 ) leads to d ɛ d t = M ɛ , where εx = x−x* , εy = y−y* , ɛ = ( εx , εy ) T , and M = ( x * [ 1 + δ - ( μ + 1 ) δ x * ] x * [ 1 + δ - ( μ + 1 ) δ x * ] y * [ 1 - ( μ + 1 ) δ x * ] y * [ 1 - ( μ + 1 ) δ x * ] ) . The eigenvalues of M are λ = 0 , 1−μδx* , and thus , the whole line z = 0 consists of fixed points and it is stable ( unstable ) when x > 1 μ δ ( x < 1 μ δ ) as well as the PP , PR , and PB types .
|
Although social dilemmas can be resolved if punishing non-cooperators or rewarding cooperators works , such rewards and punishments , i . e . , external incentives , entail certain expenses . As a result , a cooperative player who shirks his or her duty to provide an incentive to other players will emerge , and he or she will be more advantageous than an incentive-provider . In fact , the problem of excluding such cooperative incentive-non-providers , or second-order free-riders , is a well-known academic challenge . In order to meet this challenge , we devise and analyze a meta-incentive game that integrates positive incentives ( rewards ) and negative incentives ( punishments ) with second-order incentives , which are incentives for other players’ incentives . In this paper , we solve the replicator dynamics for a simple version of the game and analytically categorize the game types into four groups . We show that second-order incentives for first-order reward are necessary for cooperative regimes . This solution is general and strong in the sense that the game does not need any centralized institution or proactive system for incentives .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2015
|
The Effect of Incentives and Meta-incentives on the Evolution of Cooperation
|
Using molecular dynamics simulations , we show that the prion protein ( PrP ) exhibits a dual behavior , with two possible transition routes , upon protonation of H187 around pH 4 . 5 , which mimics specific conditions encountered in endosomes . Our results suggest a picture in which the protonated imidazole ring of H187 experiences an electrostatic repulsion with the nearby guanidinium group of R136 , to which the system responds by pushing either H187 or R136 sidechains away from their native cavities . The regions to which H187 and R136 are linked , namely the C-terminal part of H2 and the loop connecting S1 to H1 , respectively , are affected in a different manner depending on which pathway is taken . Specific in vivo or in vitro conditions , such as the presence of molecular chaperones or a particular experimental setup , may favor one transition pathway over the other , which can result in very different monomers . This has some possible connections with the observation of various fibril morphologies and the outcome of prion strains . In addition , the finding that the interaction of H187 with R136 is a weak point in mammalian PrP is supported by the absence of the residue pair in non-mammalian species that are known to be resistant to prion diseases .
The misfolding of the prion protein ( PrP ) , which is a key aspect of transmissible spongiform encephalopathies ( TSE ) , has been the subject of intense research during the past decades . Nonetheless , little is known about the underlying molecular mechanism . One serious hurdle remains the determination of the structure of the resulting misfolded isoform ( ) [1] . As a consequence , various models have been suggested with substantially different packing arrangements and monomer structures , and a consensus about the structure of is far from being reached [1] . A particular subject of controversy is about the actual region of PrP that undergoes a deep refolding during the PrP conversion . According to the so-called “spiral” [2] and “” [3] , [4] models , extended are formed in the N-terminal region and at the beginning of the C-terminal domain up to H1 ( H1 is kept intact in the former and is refolded in the latter model ) . However , it has been recently shown that the H2H3 core is also highly fibrillogenic by itself [5] , [6] . Finally , it has also been suggested that could be entirely refolded in an in-register extended [7] . Many in vitro [5] , [8]–[11] and computational [2] , [12]–[16] studies have tackled this issue using acidic conditions . They have consistently shown that low pH destabilizes PrP and favors its misfolding . This represents biologically relevant conditions insofar as endosomal organelles , whose typical pH is about 5 but can be as low as 4 . 3 [17] , have been highlighted as possible locations for growth [18]–[20] . Importantly , mammalian PrP contain one slightly buried residue , H187 , that titrates right in the range of endosomal pH [11] , [13] . Several lines of evidence indicate that its protonation [13] , or more generally the addition of a positive charge at site 187 [11] , destabilizes the protein fold . Whereas many theoretical studies have been performed on the globular C-terminal domain ( residues 121–231 using the numbering of the human sequence ) of mouse PrP ( mPrP , Fig . 1-A ) , it is worth noting that the cellular form of PrP ( ) also contains a long unstructured N-terminal tail ( residues 23–120 ) [21]–[29] , a glycosylphosphatidyl-inositol ( GPI ) anchor [30]–[32] and can be mono or diglycosylated [27] , [33] . Nevertheless , previous MD simulations have suggested that the structure and dynamics of the globular domain of is rather independent of the anchoring to the membrane and the glycosylation [34] . In addition , our previous study of the misfolding propensity of mPrP using extensive REMD simulations [16] has revealed that various monomers can be formed from the C-terminal domain alone , which is also consistent with the results of Ref . [5] , [6] . Here , we have performed microsecond MD simulations of the structured C-terminal domain of mPrP at pH 4 . 5 , which corresponds approximately to the lowest pH value observed in endosomes [17] . To this end , we assigned the protonation state of all titrable residues with the program PROPKA [35] ( see also Materials and Methods section ) . The only buried residue for which the protonation state cannot be uniquely assigned is H187 . The quantitative evaluation of its is challenging , because the protonation/deprotonation of a buried residue usually affects the protein structure drastically [36] , [37] . Nevertheless , several semi-quantitative estimates of the of H187 have been obtained [13] , [38] and they all indicate that mPrP coexists in both , neutral and protonated-H187 forms at pH 4 . 5 . Thus we have performed two sets of acidic pH simulations , with H187 in either its neutral or protonated form . It is worth noting that other residues in mPrP also titrate at pH 4 . 5 . However , they are all located at the protein surface , so that their electrostatic effect on the global structure of the protein is much less important than that of H187 . Thus , we have considered only one protonation state for these residues ( see Materials and Methods section ) . Our micorsecond simulations show that the mechanism of mPrP destabilization upon protonation of H187 involves R136 as a key partner ( Fig . 1-B , C ) . There is an electrostatic repulsion between the imidazole ring of the protonated H187 ( ) and the guanidinium group of R136 ( ) , to which the system responds by pushing away either or . Because R136 and H187 belong to two very different structural regions of the protein , namely the loop connecting S1 to H1 ( ) and the C-terminal part of H2 ( H2 ( Cter ) , Fig . 1-A ) , the effect on the structure is different depending on which of the two transition routes is taken . It is possible that specific in vivo or in vitro conditions may favor one route over the other , which could lead to completely different structures . Our findings thus seems to provide some rational to the various conclusions reached by different authors regarding the actual region of the protein that is refolded upon misfolding .
Fig . 2 shows the effect of protonating H187 on the backbone of mPrP . The structure is very stable and remains close to the NMR structure when H187 is neutral , whereas simulations with the protonated H187 exhibit important backbone fluctuations and reorganizations . As depicted in Fig . 2-C , these enhanced fluctuations are mainly located in two specific regions of the protein , namely H2 ( Cter ) , which hosts H187 , and . Fig . 2-E shows that the protonation of H187 induces a drastic change in the free energy surface . The projection of the free energy on the of H2 ( Cter ) and shows a single minimum when H187 is neutral , which corresponds to the native structure of PrP , and a complicated multiple minima landscape when H187 is protonated . The new free energy basins are located Å away from the native basin , thus corresponding to substantial conformational changes . The two example snapshots provided in Fig . 2-B , D show that this reorganization is accompanied by a significant modification of the secondary structure of the protein . We will provide a more detailed analysis of the secondary structure changes later in the following sections . For the time being , it is interesting to rationalize how the perturbation that is introduced at one side of the protein ( the protonation of H187 located in H2 ( Cter ) ) is transmitted through the macromolecule and affects strongly the structure at the opposite side ( ) . In order to understand the mechanism by which the protonation of H187 induces the reorganization of the protein structure , it is necessary to have a closer look to the environment of H187 in PrP . It is particularly interesting to focus on nearby charged residues because they are expected to play a major role in the reorganization of the protein when H187 gets a positive charge upon protonation . In the NMR structure of mouse PrP , the closest charged residues are R136 , R156 , K194 , E196 and D202 ( Fig . S5-A ) . R136 is somewhat isolated in terms of proximity with charged residues other than H187 ( when protonated ) , whereas K194 , E196 , R156 , and D202 form a network of salt-bridge interactions . These four latter residues have been pointed out has possible key residues in the misfolding of PrP [13] , [39] . As shown in Fig . S5-B , our simulation provide a consistent picture with that of Ref . [13] , because the protonation of H187 leads to the disruption of the salt bridge between E196 and R156 and the transient formation of a new salt bridge between the protonated E196 and H187 , while a tight salt bridge is maintained between R156 and D202 ( K194 is highly solvated , independently of the protonation state of H187 , and never interact strongly with E196 ) . Nevertheless , the fact that R136 does not have any close alternative partner makes it more sensitive to the positive electric field created by the protonated H187 , as we shall see in the next section . The observation of structural rearrangements in , which is located far from H187 , has motivated us to perform a thorough analysis of the mobility of each residue in this region . It turns out that R136 is a key partner of H187 in the destabilization of mammalian PrP upon protonation of H187 . In the NMR structure of mPrP ( Fig . 1 ) , and are about 8 Å apart and loop ( residues 154–158 ) is located in between . is stabilized by a series of dipole-charge interactions with four peptide bonds while is H-bonded to one carbonyl group and establishes van der Waals contacts with the ring of P158 ( Fig . 1-B ) . Because of the proximity of and in the native structure of mPrP , the protonation of the former should induce an electrostatic repulsion between the two groups . A discussion of the corresponding energetics is provided in Text S1 . Fig . 3 shows the effect of the protonation of H187 on the position of ( or ) and . When H187 is neutral , and are mostly located in their respective native cavities , whereas they cover a much wider portion of conformational space upon protonation of H187 . We define four conformational states according to the position of ( or ) and inside or outside their respective native cavities . To do so we consider the bivariate histogram of the distances between ( or ) and from their respective cavities ( Fig . 3-C ) . The conformational state in which both groups stay close to their original location will be termed , and we define states and according to the departure of or , respectively . Interestingly , the state is almost not populated . The picture that is the most consistent with these data is that PrP exhibits a dual response to the protonation of H187 , by pushing away either or ( but not both at the same time ) , thus decreasing the electrostatic repulsion between them . Because H187 and R136 are attached to H2 ( Cter ) and , respectively ( Fig . 1-A , B ) , the local reorganization of either or affects the global structure of these two regions ( Fig . 2 ) . We stress that , once H187 is protonated , the dynamics of the system proceeds smoothly through a series of locally thermalized states giving rise , in a reproducible way , to either the or state . Fig . S6 and S7 show that and remain in their native pockets during at least 100 ns before one of the two moves out . A similar electrostatic repulsion can be expected for the H187R mutation , for which the positive charge of the introduced arginine has been suggested to destabilize the overall fold of human PrP [11] , [40] , [41] . An interesting aspect of this finding is that none of the non-mammalian PrP exhibit this specific spatial arrangement ( Fig . S4 ) . In other words , these non-mammalian proteins do not have this pH-sensitive “weak point” in there structure and this probably explains the fact that non-mammalian species do not exhibit TSEs . Due to the buried character of H187 and the fact that its protonation induces a substantial modification of the protein structure , the quantitative evaluation of its ( and the corresponding contributions of other residues ) during the misfolding is challenging [36] , [37] . Nevertheless , PROPKA calculations [35] provide physically sound estimates that can help to rationalize the underlying physics . Such calculations for representative snapshots of our simulations are provided in Fig . S8 . The of H187 is systematically shifted up as soon as the protein starts to misfold , independently of the pathway ( vs ) that is taken . This is in agreement with the fact that the proximity with the positive charge of in the native structure of mPrP induces a down-shift of the of H187 ( this is supported by the fact that our PROPKA calculations report R136 as a key residue in the electrostatic environment of H187 , see the corresponding PROPKA output file for a representative structure in Dataset S1 ) . As soon as moves out of its cavity ( state ) the electrostatic repulsion between and decreases and the protonated form of H187 becomes much more stable ( shifted up ) . When the protein adopts a state , is much more solvated by water and the of H187 approaches the corresponding value in water ( ) . The positioning of ( or ) and has a strong influence on the length of the S1 , S2 , as shown in Table 1 . Typically , both and states correspond to structures with a short native , while the state is characterized by the preference of an elongated . This is illustrated by the simulation depicted in Fig . 4 . At the beginning of the simulation , the protein is in its native conformation . As depicted in the insets of Fig . 4 , the native location of at is a key aspect of the protein fold because it forms a sort of “clip” that forces the backbone to remain packed against the rest of the protein ( Fig . 1-A ) in a specific conformation . The permanent departure of out of its cavity at ns induces an important release of backbone constraint and the system is consequently more prone to reorganize in this region . Then the system relaxes during about 400 ns , and and come close together . The number of hydrogen bonds between the two strands increases concomitantly and the elongates ( Fig . 4-A , B ) . As shown in Fig . 5 , both and states are characterized by an unraveling of H2 ( Cter ) . However , the underlying mechanisms ( and the corresponding transition pathways ) differ substantially . The portion of the helix that undergoes an unraveling is represented by a dashed purple arrow in Fig . 1-A ( see also the example snapshot depicted in Fig . 2-D ) . The departure of ( conformation ) out of its cavity obviously destabilizes H2 because the helix looses a key tertiary contact with loop ( Fig . 1-A ) . The unraveling of H2 ( Cter ) in the state has its roots in the polar interactions of with the nearby residues . A closer look to the shape of the cavity ( Fig . 1 ) reveals that it is a narrow groove at the bottom of which lies the carbonyl group of T183 . The contact analysis shown in Fig . 6 reveals that the neutral is H-bonded to R156 only , consistent with the NMR structure of mPrP [21] , whereas new contacts are formed with the CO group of T183 when H187 is protonated . A key aspect of these extra contacts is that they involve not only the group of H187 , but also the group . They reflect dipole-charge interactions between the extra positive charge of and the dipole moments of the 156–157 and 182–183 peptide bonds . In other words , the imidazole ring can take two conformations around the bond and still maintain a significant interaction with one of the two nearby backbone CO groups , which results in four stable conformations inside the pocket . The formation of new contacts between and T183 ( CO ) has two effects that explain the loss of helical character in H2 ( Cter ) . First of all , it weakens the tertiary contact between H2 ( Cter ) and . Second , the native intra-helix H-bond between T183 ( CO ) and H187 ( NH ) is lost . The tighter the interaction between and T183 ( CO ) the weaker the local stability of H2 . In this paper we have shown that the protonation of H187 in mPrP at pH 4 . 5 , which corresponds approximately to the lowest pH observed in endosomes [17] , leads to extensive conformational changes on the microsecond time scale . The picture that emerges from our simulations is that the protonation of H187 leads to an electrostatic repulsion between the positive charges of and , which results in conformational transitions in the regions to which H187 and R136 are linked , namely H2 ( Cter ) and respectively . Our findings hence highlight two possible routes for PrP misfolding with either the unraveling of H2 ( Cter ) alone ( route ) or the unraveling of H2 ( Cter ) with simultaneous elongation of S1 , S2 ( route ) . This dual behavior seems to reconcile the various observations and proposals that have been made regarding the actual PrP region that undergoes a deep refolding upon conversion to [2]–[5] . It is indeed possible that a particular computational or experimental setup favors one of the or substates at the beginning of the misfolding process . Such conformational shift could be assisted in vivo by molecular chaperones such as polyanionic molecules [1] , [42] . This variability in misfolding pathways may also be connected to the fact that prion exhibits a variety of strains , because it is believed that changes in conformations of encodes for strain properties [30] , [43] , [44] . Finally , it is interesting to note that the pattern is not present in those non-mammalian species who are known to resist to TSEs . This is a possible explanation for the observed resistance to TSEs in these species .
All simulations were started from the NMR structure of mPrP published by Riek et al . ( PDB code 1AG2 ) . We aimed at modeling mPrP with a neutral or protonated H187 at pH 4 . 5 . The protonation state of titrable residues apart from H187 was first estimated from PROPKA [35] calculations . The protonation state of most of them can be determined without ambiguity . All buried or semi-buried residues other than H187 are all aspartic or glutamic acids whose side chains are hydrogen-bonded to other groups in the protein . This has the effect to shift up their above the typical values of that they adopt in water , i . e . significantly above the pH we want to model . Hence they are expected to be protonated . The solvent-exposed histidines are expected to exhibit a of so they can be considered protonated at a pH of 4 . 5 . The remaining solvent-exposed aspartic or glutamic acids are more ambiguous because their is close to the pH we want to model . Nevertheless , their solvent-exposed character makes them much less important for the global fold of the protein . We chose their protonation state according to the estimated with PROPKA [35] . The relevance of this choice was verified a posteriori by observing that the fold of the protein is very well conserved over microsecond simulations with a neutral H187 . Two topologies ( one with H187 neutral and one with H187 protonated ) were built with the GROMACS 4 . 0 . 7 [45]–[47] suite of programs . For each of them , the protein was immersed in a rhombic dodecahedral water box . The size of the box was chosen so that the distance between the protein and the edge of the box was Å . The system was neutralized by adding 2 or 3 chloride counterions ( depending on the protonation state of H187 ) . The resulting system contained about 30000 atoms . The AMBER99SB force field [48] was used to describe the protein and the TIP3P model [49] was employed for the water molecules . The force field was included in GROMACS thanks to the ports provided by Sorin and coworkers [50] , [51] . The particle mesh Ewald method [52] together with a Fourier grid spacing of 1 Å and a cutoff of 12 Å was used to treat long-range electrostatic interactions . A cutoff of 12 Å was used for van der Waals interactions . The water box was first relaxed by means of NpT simulations with restraints applied to the positions of the heavy atoms of the protein . Then the system was optimized in a series of energy minimization runs in which the restraints on the protein were progressively removed . Finally , we run eight simulations with a time step of 2 fs . Three and five of them were conducted with a neutral or protonated H187 , respectively . Each simulation was initiated with a set of velocities taken at random from a Maxwell-Boltzmann distribution corresponding to a temperature of 10 K . Then the system was heated up to 300 K in 300 ps using two Berendsen thermostats [53] ( one for the protein and one for the solvent ) with a relaxation time of 0 . 1 ps each . The simulation was prolonged for 100 ps and the Berendsen barostat with a relaxation time of 2 ps was switched on during 100 ps . Finally , we switched to production phase using a Nose-Hoover [54] , [55] thermostat and a Parrinello-Rahman barostat [56] with relaxation times of 0 . 5 and 10 . 0 ps , respectively . The total simulation lengths were 1 . 9 , 1 . 3 and 1 . 6 for simulations with a neutral H187 , and 1 . 9 , 1 . 5 , 1 . 6 , 1 . 2 and 1 . 2 for simulations with a protonated H187 . The plot of each simulation is provided in Figure S1 . All the representations were done with the program VMD [57] . Secondary structure assignments were done using the STRIDE algorithm [58] .
|
Transmissible spongiform encephalopathies , which include the “mad cow” disease and the Creutzfeldt-Jakob disease , are related to the abnormal folding of a host protein termed the prion protein ( PrP ) . Many aspects of the underlying molecular mechanism still remain elusive . Among the hypotheses that have been put forward in the past few years , it has been suggested that PrP could be destabilized by the protonation of a specific residue , H187 , when the protein passes through acidic cell organelles . We have modeled PrP at the atomistic level , with the neutral and protonated forms of H187 . Our simulations show that the destabilization process can follow two alternative pathways that could lead to different final structures . This discovery may shed some light on one of the most puzzling aspect of prion diseases , the fact that they exhibit various strains encoded in the structure of misfolded PrP . In addition , the atomistic details provided by our model highlight a key interactions partner in the destabilization process , R136 . The residue pair is not present in non-mammalian species that do not develop prion diseases .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"biophysics",
"biology",
"computational",
"biology"
] |
2013
|
Two Misfolding Routes for the Prion Protein around pH 4.5
|
LeuT-like fold Na-dependent secondary active transporters form a large family of integral membrane proteins that transport various substrates against their concentration gradient across lipid membranes , using the free energy stored in the downhill concentration gradient of sodium ions . These transporters play an active role in synaptic transmission , the delivery of key nutrients , and the maintenance of osmotic pressure inside the cell . It is generally believed that binding of an ion and/or a substrate drives the conformational dynamics of the transporter . However , the exact mechanism for converting ion binding into useful work has yet to be established . Using a multi-dimensional path sampling ( string-method ) followed by all-atom free energy simulations , we established the principal thermodynamic and kinetic components governing the ion-dependent conformational dynamics of a LeuT-like fold transporter , the sodium/benzyl-hydantoin symporter Mhp1 , for an entire conformational cycle . We found that inward-facing and outward-facing states of Mhp1 display nearly the same free energies with an ion absent from the Na2 site conserved across the LeuT-like fold transporters . The barrier separating an apo-state from inward-facing or outward-facing states of the transporter is very low , suggesting stochastic gating in the absence of ion/substrate bound . In contrast , the binding of a Na2 ion shifts the free energy stabilizing the outward-facing state and promoting substrate binding . Our results indicate that ion binding to the Na2 site may also play a key role in the intracellular thin gate dynamics modulation by altering its interactions with the transmembrane helix 5 ( TM5 ) . The Potential of Mean Force ( PMF ) computations for a substrate entrance displays two energy minima that correspond to the locations of the main binding site S1 and proposed allosteric S2 binding site . However , it was found that substrate's binds to the site S1 ∼5 kcal/mol more favorable than that to the site S2 for all studied bound combinations of ions and a substrate .
Ion-coupled secondary-active transporters are integral membrane proteins involved in the cellular uptake and release of various substrates across cell membranes . These transporters transport the main substrate uphill against its concentration gradient by coupling with the favorable downhill transport of ion ( s ) . Examples of their substrates include neurotransmitters ( serotonin , dopamine , and epinephrine ) , sugars , amino acids , and nucleobases [1]–[3] . The malfunction of secondary transporters is implicated in various neurological , skin , cardiovascular , and renal diseases in humans [3] . As a result , ion-coupled secondary transporters are important targets for drugs in the treatment of psychotic states , schizophrenia , clinical depression , diabetes , and obesity [2] , [3] . For example , several sodium-glucose co-transporter 2 ( SGLT2 ) inhibitors are being developed as a new class of drugs to treat type 2 diabetes [4] , [5] , while transporters from the neurotransmitter:sodium symporter ( NSS ) family are common targets for anti-depressants ( tricyclic antidepressants and selective serotonin reuptake inhibitors ) and drug of abuse [6]–[9] . The generally accepted mechanism for secondary-active transport is the alternating access model [10] in which the transporter changes between outward-facing and inward-facing conformations , allowing the main substrate and coupled ion ( s ) to bind to one side and be released from the other side . The progress in structural studies of secondary transporters led to identification of several structure folds including a so-called “LeuT-like” fold . The LeuT-like fold secondary transporters share common structural features , including two-fold symmetric inverted repeats that involve 5+5 essential helices and a break in the TM helices that forms the substrate binding pocket ( s ) [1]–[3] . Recently , the sodium/hydantoin symporter Mhp1 [11] , [12] , the sodium/leucine symporter LeuT [13] , [14] , and the sodium/betaine symporter BetP [15] , [16] were crystallized in both outward-facing and inward-facing conformational states , providing detailed molecular insight into the atomic structures of the key protein states involved in the transport of a substrate . Structurally , Mhp1 has the same “LeuT-like fold” as LeuT , vSGLT , BetP and several other transporters despite belonging to a different gene family [12] and thus represents an ideal target for understanding of basic principles that governs mechanism of secondary transport in LeuT-like fold transporters . Mhp1 belongs to the nucleobase-cation-symport-1 gene family ( NCS1 ) . Similarly to LeuT , Mhp1 is Na+ dependent . Mhp1 has a transport stoichiometry of 1 Na+: 1 substrate , differing from the 2 Na+:1 substrate ratio in LeuT . Despite the different stoichiometry , the sodium-binding site in Mhp1 , corresponding to the Na2 site in LeuT [14] , is conserved in LeuT-like fold Na-dependent secondary transporters [3] , [17] , [18] . Thus , a detailed molecular-level understanding of the transport mechanism of Mhp1 may offer a chance to generalize the coupling mechanism for this large group of LeuT-like fold Na-dependent secondary transporters . The crystal structures for Mhp1 were captured in the following states: outward-facing open state with a bound sodium ion ( O_i ) , outward-facing occluded state with a bound substrate and a sodium ion ( O_si ) , and apo inward-facing open ( I_apo ) [11] , [12] . Several tentative mechanisms were proposed recently by Shimamura et al . [11] with the use of a dynamical-importance sampling study for the transitions and Adelman et al . [19] extending a weighted ensemble path-sampling study with coarse-grained model . Although these studies offered a potential scheme for the conformational dynamics in Mhp1 , the studies also have serious limitations . Most importantly , the transported Na+ and the benzyl hydantoin substrate , despite playing an essential role in the transport cycle [17] , [20]–[24] , were not included explicitly in the path sampling computations . Therefore one of the key questions in the mechanism of secondary transport remained untouched . In particular , how does the binding of a sodium ion stabilize different conformational states of the system ? To aid the understanding of the role of the sodium ion in conformational transitions , we performed atomistic simulations for the whole transport cycle of O_i→O_si→I_apo→O_i with explicit membrane , water environment , and the transported ion and substrate . To elucidate a reaction path for this transition , we used the swarm-of-trajectories method of Pan et al . [25] , [26] . The resulting minimum-energy reaction path was used then for detailed exploration of the energetics of the protein conformational transition as a function of ion/substrate binding with free energy simulations . Specifically , we report multiple 2-dimensional-potential of mean force ( 2D- PMF ) profiles [27]–[29] along the gating reaction coordinate and discuss role of ion/ligand binding in stabilization of different conformational states of the system . These profiles help to elucidate the thermodynamic contributions governing the ion/ligand translocation and transporter conformational changes [30] .
One of the key challenges in characterizing the free energy landscapes of conformational transitions in proteins is a definition of a set of variables ( reaction coordinates ) connecting the multiple conformational states of the system . Often , such choices are based on a preconceived notion about tentative mechanism of the process . Several models that explain the mechanics for the transporter sliding from state to state have been proposed . The two most popular models are the rocking bundle model [31] and the flexing helices model [13] , [14] . In the rocking bundle model [31] , the rocking of a 4-helix “bundle” formed by TMs 1 , 2 , 6 , and 7 against the “scaffold” formed by the rest of the essential TMs ( TMs 3 , 4 , 5 , 7 , 8 , 9 , and 10 ) allows the transporter to alternatively open to either side of the membrane . In the flexing helices model [13] , [14] , flections in the two helices with a helix-break-helix motif in the middle ( TM1 and TM6 ) of the TM section are proposed to be the major trigger for the conformational changes that allow alternating access . These two models are not necessarily mutually exclusive . The mechanics described in these models may work together to achieve the functions of Na-dependent secondary-active transporters [13] , [16] , [32] , [33] . In a number of available crystal structures , the features of both models seem to be present , albeit to a different degree . For example , the flexing helices model is highly featured in LeuT [13] while the rocking bundle model is associated with apparent symmetry between different states of Mhp1 transporters [11] , [13] , and a mixture of both modes of transport was proposed to be essential for BetP [16] . Regardless of the gating model , natural reaction coordinate for ion-dependent conformational dynamics relevant to the process can be reduced to the distance between the groups of residues forming the conserved Na+ site ( Na2 of LeuT ) from the two TMs ( TM1 and TM8 ) . This conserved Na+ site sits between the interface of the “bundle” and “scaffold” of the rocking bundle model [31] . This site is also directly beneath the break in the TM1 helix that was shown to be critical in the flexing helix model [13] . Thus , following Shimamura et al . [11] suggestion , we use the Na2 site distance , defined as the distance between the center of mass ( COM ) of the Na+-coordinating residues in TM1 ( Ala38 , Ile41 ) and TM8 ( Ala309 , Ser312 , and Thr313 ) , as an order parameter to indicate the conformation of the transporter protein . Na2 ion binding has been strongly associated with the gating of the so-called “thick gate” [14]: ∼20 Å thick of compactly packed protein blocking the accessing path to the intracellular side for the substrate when it is closed . For clarity , we use r ( thick_gate ) to describe the order parameter . A r ( thick_gate ) of ∼5 . 5 Å indicates outward-facing state , whereas a r ( thick_gate ) of approximately 10 Å corresponds to inward-facing state ( Figure 1 ) . In addition to the “thick” gate , an extracellular thin gate and an intracellular thin gate have been proposed for Mhp1 to explain the alternating access mechanism in a “modified” rocking bundle model [11] . In this model , Mhp1 changes orientation through the rocking of the “bundle” helices formed by TMs 1 , 2 , 6 , and 7 relative to a reduced “scaffold” , or the so-called “hash motif” ( helices of TMs 3 , 4 , 8 , and 9 ) ( Figure S1 ) . When the protein faces outward , TM10 could serve as an extracellular thin gate and bend to block the EC vestibule . In contrast , when the protein faces inward , the proposed IC thin gate TM5 bends and opens the IC vestibule . To account for the movement of the EC thin gate , an additional reaction coordinate r ( EC_thin_gate ) is defined by the COM distance of the mobile TM9/TM10 loop residues ( Asn360 , Thr361 , and Phe362 ) and the stationary TM1 residues ( Ile47 , Ala48 , and Ala49 ) following ref . [11] . The dynamics of the IC thin gate has been shown to nearly synchronize with those of the thick gate [11] and thus is redundant for describing the reaction coordinate . The order parameters ( Sz ( Na+ ) and Sz ( substrate ) describing binding/release of Na+ and the benzyl hydantoin substrate are defined as vertical ( Cartesian z-direction of the simulation system ) positions relative to their respective binding sites . Thus , Sz around 0 Å corresponds to a location of the binding site , while a large positive and negative Sz value indicates that the ion/substrate is at the EC and IC side respectively . The exact definitions of the order parameters are provided in Table S1 . Typical values of order parameters for various conformational states of Mhp1 are illustrated in Figure 1B . We obtained a relaxed transition path for the entire transport cycle using the string method with swarm-of-trajectories [25] , [26] ( see methods ) . This relaxed path is represented by 134 intermediate images that connect the three conformational states . RMSD analysis ( Figure S2 ) of the path shows that the modified rocking bundle model [11] can dynamically represent the entire cycle , consistent with an earlier report obtained from coarse-grained molecular dynamics [19] . Throughout the transport cycle , backbone RMSD changes within the “bundle” or the “hash motif” ( Figure S1 ) were less than 1 . 2 Å , whereas backbone RMSD changes for the entire transporter were much higher , up to 3 Å ( Figure S2 ) . The structures corresponding to finally relaxed transport path were used for free energy simulations using umbrella sampling and WHAM . The causality in the mechanism of release of the ions and substrate from the occluded state of LeuT fold secondary transporters has been an intriguing topic [1] , [17] , [22] , [32] , [34] . It has been proposed that the binding of a substrate to the main substrate binding site labeled “S1” perturbs the Na+-binding site [32] , promoting the release of Na+ . Our computations [17] indicated that slow wetting from the intracellular side may also facilitate the release of Na+ . Regardless of the driving forces , a large amount of evidence [17] , [22] , [24] , [32] , [34]–[36] indicates that the release of Na+ precedes the release of the main substrate . Our relaxed transition path from the string method strongly supports this notion . In Figure 2 we illustrate the path that describes transition from O_si to I_apo , represented with 70 intermediate images ( Image 12–81 ) connecting the initial O_si ( Image 11 ) and final I_apo ( Image 82 ) states . Na+ starts moving towards the intracellular site first ( ∼Image 25 ) , while the substrate remained bound even when ion has escaped from its binding site to ∼8 Å beneath the Na+ site of the occluded O_si state ( ∼Image 32 ) . Next we examined the free energy landscape underlying Na+ release from the transporter by calculating the 2D PMF for reaction coordinates that describe vertical ( across the lipid bilayer ) displacement of an ion relative to its binding site ( Sz ( Na+ ) ) and the state of the thick gate ( r ( thick_gate ) ) ( Figure 3A ) . The computed PMF shows that the initial stage of the Na+ release and the inward-facing opening of the thick gate , is a concerted process . 2D PMF for an ion release process ( Figure 3A ) shows a meta-stable Na+ binding site , denotes Na2' , near [5 . 5 Å , −3 . 0 Å] . The relative difference in stability between the main binding site and the meta-stable site is ∼5 kBT . In the meta-stable site , Na+ is still coordinated by Ile38 of TM1 and Ser312 and Thr313 of TM8 but breaks away from A41 of TM1 and Ala309 of TM8 to coordinate to Asn168 of TM5 ( Figure 3A , center panel , and Figure S3 ) . Thus , Asn168 of TM5 may provide an attractive force that chaperones Na+ escape from Na2 , similar to the role proposed for Asp189 in vSGLT [24] , [32] and Glu192 in LeuT [37] . On the other hand , the interaction of TM5 residues to the Na+ bound to the Na2 site suggests that Na2 and Na2' may play an important role in regulating the dynamics of TM5 . Which in turn may serve as the proposed intracellular thin gate of Mhp1 , consistent with an earlier study on LeuT by Shi et al . [23] . Figure S3 ( right panel ) illustrates how TM5 and TM8 move and open up the intracellular pathway for the I_apo state , when Na+ is released providing further support for this mechanism . The PMF maps collected in Figure 3A ( left panel ) show essentially a flat energy surface with Na+ fully dissociated from the transporter ( Sz ( Na+ ) at −32 Å to −28 Å ) . There is only a small difference ( <2 kBT ) in the PMF values between the outward-facing states ( r ( thick_gate ) at ∼5 . 5 Å ) and the inward-facing states ( r ( thick_gate ) at ∼10 Å ) . This result suggests that energy of a thermal bath is sufficient for stochastic shuttling of empty transporter between outward-facing and inward-facing states . In contrast , the binding of Na+ helps to stabilize the outward-facing state of the transporter , indicated by a favorable minima at a r ( thick_gate ) of ∼5 . 5 Å relative to a shallow well at ∼10 Å ( inward-facing states of the system ) . To highlight the coupling between ion binding and energetics of gating process we computed 1D PMF slice from 2D PMF map for the r ( thick_gate ) dynamics ( Figure 3A , right panel ) . These 1D PMF profiles provide a measure for the relative stability of the outward-facing and inward-facing states . When a Na+ is bound , the outward-facing states ( r ( thick_gate ) ∼5 . 5 Å ) are ∼16 kBT more favorable than the inward-facing states ( r ( thick_gate ) ∼10 Å ) . In contrast , when the Na+ is released , the PMF difference between the outward-facing and inward-facing states is within 2–3 kBT . To show that the stabilization of the outward-facing conformations in Figure 3A is mainly caused by the binding of Na+ but not the substrate , we computed 2D PMF maps ( Figure 3B , left panel ) for the transition from the inward-facing open ( I_apo ) state to the outward-facing state with a Na+ bound ( O_i ) , for which a substrate is not involved . Similar to Figure 3A , the 2D PMF also shows that a bound Na+ leads to significant stabilization of outward-facing conformations . In addition , without a bound Na+ , the PMF profile was essentially flat for the transition between the inward-facing ( r ( thick_gate ) ∼10 Å ) to the outward-facing ( r ( thick_gate ) ∼5 . 5 Å ) states with a barriers of ∼4 kBT ( Figure 3B , upper right panel ) . The small barrier between the inward-facing and outward-facing states when the Na+ is unbound suggests that the transition between these states is likely and can be driven by a thermal energy of the bath . This mode of gating is not uncommon in secondary transporters . For example , in an experimental study of LeuT gating using single-molecule fluorescence resonance energy transfer methods , outward-facing and inward-facing states were shown to be in equilibrium without Na+ binding [22] . Reyes et al . proposed relatively unobstructed ( stochastic ) gating for the ion/substrate-free GltPh transporter from the EAAT family [38] . The binding of an ion and a substrate merely shifts the stability of the system towards one of the states , whereas the empty protein can shuttle up and down the membrane [38] . The transition from the outward-facing Na+-bound O_i state to the outward-facing occluded state with binding of both Na+ and the substrate ( O_si ) features the binding of the substrate from the extracellular bulk to the primary S1 substrate binding site and the occlusion of the S1 site through the closure of the EC thin gate [11] . We quantified the free energy landscape of this transition by computing the 2D PMF map ( Figure 4A ) with two reaction coordinates describing dynamics of the EC thin gate ( r ( EC_thin_gate ) ) and substrate binding ( Sz ( substrate ) ) . The PMF map indicates a global minimum for the substrate at a Sz ( substrate ) of approximately −2 to −1 Å . This minimum corresponds to the primary substrate binding site ( S1 ) of the occluded Mhp1 ( O_si ) , in which the substrate forms hydrogen bonds with Asn318 ( TM8 ) and Gln121 ( TM3 ) and aromatic ring-ring interactions with Trp117 ( TM3 ) and Trp220 ( TM6 ) ( Figure 4B , left panel ) . At this minimum , the EC thin gate is relatively flexible as indicated by the small barriers featured in the PMF map ( <5 kBT ) for the gating distance transition from 12 Å ( relatively closed ) to 18 Å ( widely open ) . A second minimum ( S2' ) is found at the Sz ( substrate ) position of ∼7 Å , approximately 8–9 Å above the S1 substrate binding site . In addition to residues from TMs 1 and 3 ( Gln42 , Ile47 , Phe120 , and Gln121 ) , this site also involves residues from TM10 ( Leu363 ) , consistent with the putative S2 sites proposed for LeuT [22] , [39] and DAT [40] . This suggests that S2 may be involved in a substrate entrance providing a low-affinity binding site . However , we note that this second free energy minimum ( S2 ) for substrate is less favorable than in S1 , with relative difference in stabilization of about 10 kBT . From Figure 4A , the optimal sequence for the transition from the O_i state to the O_si state is as follows . The substrate binds to the substrate binding site ( S1 ) from the EC bulk , potentially through the putative S2' site during the transition . The EC thin gate then closes to occlude the substrate from the EC bulk . However , the EC thin gate displays significant conformational flexibility for this state as reflected by the flat PMF landscape for the opening/closure of the EC thin gate . Overall , the binding of a substrate to the S1 site for an O_i state transporter is an energetically favorable process . The PMF map shows a PMF drop of ∼16 kBT for the substrate binding to S1 relative to be in the bulk , suggesting mM affinity . The data collected is insufficient to provide a definitive answer whenever S2 plays any role in conformational dynamics of the transporter . Shi et al . [22] , [39] proposed a mechanism in which allosteric regulation mechanism of LeuT gating requires an existence of optimal conformational state ( for S2 binding ) at which substrate in S2 can lead to conformational switching and it is one of many possible combinations of ion/substrate loads . Therefore complete evaluation of conformational switching due to substrate binding to S1 and S2 would require full evaluation of multiple energy surfaces as function of substrate and ion loads for every stable minima of the system and is computational unfeasible at the moment . Our results highlight the role of Na+ in the transport cycle: the binding of Na+ promotes the outward-facing conformation , and the release of Na+ allows the transition to an inward-facing conformation . Therefore , the binding of Na+ is coupled to the binding of the substrate by preparing more Mhp1 protein in the outward-facing conformation , to which the substrate can readily bind . On the other hand , the binding of the substrate was also shown to promote Na+ binding [11] . Our free energy profiles for the transport of Na+ and substrate provide the molecular underpinning of this coupling mechanism . In particular , Figure 3B shows that binding of Na+ to an apo-state of Mhp1 in the absence of substrate is energetically unfavorable . The dehydration of a Na+ ion upon transfer from the extracellular milieu to a binding site ( Figure 3B ) in the O_i state is marginally unfavorable for the equi-molar IC/EC solutions used for the PMF computations . However , the small barrier for an ion to escape to the extra-cellular milieu suggests rapid ion exchanges with the bulk ( Figure 3B ) . At the same time , the binding of the substrate to the O_i state ( Figure 4A . ) is an energetically favorable process that aids the stabilization of a protein in the O_si state . The presence of a bound substrate obstructs the unbinding and escape of Na+ to the extracellular milieu . Figure 5 suggests that the bound substrate serves as a plug to restrict the release of Na+ to the extracellular bulk [11] . As a result , once locked in an occluded state ( O_si ) , Na+ is driven to unbind into the intracellular solution . The simulations shows that the Mhp1 transport cycle complies well with the postulated steps within an alternating access mechanism [2] , [10] i . e . The analysis of multiple PMFs allows for understanding of energy flow and coupling between different stages in a transport cycle ( summarized in Figure 6 ) . Starting from the outward-facing state with Na+ bound ( O_i ) , the substrate binds to the primary substrate-binding site ( S1 ) , facilitating formation of the O_si state . The binding of Na+ and a substrate results in the closure of the extracellular ( EC ) thin gate and the blockage of Na+ unbinding to the EC solution . In the next step of the transition , Na+ unbinds to the intracellular ( IC ) side [17] , [22] , [24] , [32] , [34]–[36] , and the transporter protein opens its thick gate towards the IC side in a concerted movement . The substrate is then released to the IC solution . It is important to note that releases of Na+ ( Figure 3A ) and the substrate ( Figure S4 ) are energetically unfavorable . With the release of the Na+ and the substrate , the Mhp1 transporter is in the inward-facing apo state ( I_apo ) . To reset the transport cycle , the protein is required to change its conformation back to the outward-facing state . Little is known about the driving force for this step [22] , [38] , [41] . Our 2D PMF computations ( Figure 3B and Figure 6 ) show that for an empty transporter ( both ion and a substrate are released ) the energetic barriers separating each of the conformational states are marginal . The transporter can shuttle back and forth until the binding even of a Na+ to Mhp1 promotes the stabilization of the outward-facing state O_i . PMF shows that I_apo and O_apo states separated by a very small energy barrier . This result is consistent with the mechanistic principles proposed earlier by Gouaux et al . ( Figure 3 of Ref . [18] ) . One unresolved feature of the above mechanism is with the identification of key driving forces responsible for Na+ binding and subsequent release from the Mhp1 protein . In addition to Asn168 , which may facilitate the release of Na+ , we propose that local concentration gradients and a partial surface hydration might play an important role [22] in this process . At the O_si state , the local Na+ concentration in the Na+ binding state is very high relative to the bulk phase because of the small volume of the Na+ site providing both attractive pathway for slow hydration and a driving force for ion release . In review , the free energy landscapes collected from over 4 µs of all-atom MD simulations predict that Na+ binding drives the cycle by promoting outward-facing states of Mhp1 , thus preparing conformations for the subsequent substrate binding . In contrast , the substrate mainly improves the apparent binding affinity of Na+ by restricting the release of Na+ back to the EC bulk . These computational results explain the experimental results on coupling between Na+ and benzyl-hydantoin binding to Mhp1 [12] . Our results also indicate that Asn168 of TM5 plays an important role in Na+ release to the IC bulk , reminiscent of the role assigned to Asp189 in vSGLT transporters [24] , [32] and Glu192 in LeuT [37] . This result supports the findings reported for LeuT and ApcT transporters [23] where Na2 ion was proposed to be essential for modulation of the IC thin gate . It was found that barriers separating an inward-facing apo and an outward-facing apo states are very low ( 2–4 kBT only ) . This result is consistent with experimental study of LeuT [22] and the proposed stochastic shuttling for the Na-dependent secondary transporter glutamate transporter Gltph [38] . Therefore , the low-barrier shuttling of empty proteins appears to be general feature in many secondary transporter systems .
Structures of the Mhp1 transporter [11] , [12] in the states of outward-facing with Na+ bound ( O_i , pdb entry 2jln ) , outward-facing occluded with both the substrate and Na+ bound ( O_si , pdb entry 2jlo ) , and inward-facing apo ( I_apo , pdb entry 2×79 ) were taken from the X-ray coordinates stored in the Protein Data Bank . The simulation system was built using the web-based CHARMM-GUI membrane builder [42] , containing one Mhp1 transporter , Na+ and the substrate when present , and 150 lipids molecules with a native-like 4∶1 mixture [11] of 1-palmitoyl-2-oleoyl phosphatidylethanolamine ( POPE ) and 1-palmitoyl-2-oleoylglycero-3-phosphoglycerol ( POPG ) solvated by 100 mM NaCl in an aqueous solution . The full simulation cells include ∼60 , 000 atoms in a hexagonal box with a dimension of approximately 43 Å in edge length and 94 Å in height ( Figure S5 ) . The MD simulations were carried out by CHARMM version c36a2 [43] using the CHARMM27 force fields with cross term map ( CMAP ) corrections for proteins and lipids [44] . The TIP3P model was used for water molecules [45] . The force field parameters for the benzyl-hydantoin ( BH ) substrate ( Dataset S1 ) were generated using the paramchem web-based interface [42] which applies the philosophy of the CGenFF force field [46] . A constant pressure/temperature ( NPT ) ensemble was used for all simulations with a pressure of 1 atm and a temperature of 298 . 15 K with the Nose-Hoover thermostat [47] , [48] . Long-range electrostatic interactions were calculated using particle mesh Ewald ( PME ) algorithm [49] with a 96 Å by 96 Å by 108 Å grid for a fast Fourier transform . A non-bonded interactions switching function over 12 to 16 Å was used in all MD simulations . The leap-frog algorithm was applied to integrate the equation of motion with a time step of 2 fs . Following a staged equilibration with a gradual decrease in harmonic constraints acting on heavy protein atoms , a further equilibration was run for 10 ns without any configurational constraints . These equilibrated systems were then used to perform targeted molecular dynamics simulations . The TMD module [50] in CHARMM [43] was applied for targeted molecular dynamics simulations . Three transitions were simulated: O_si to O_i , O_si to I_apo , and I_apo to O_i . To match the state of O_si , A substrate was placed in the extracellular vestibule ( ∼13 Å above the substrate binding site ) to the O_i state and a substrate and a Na+ were placed in the intracellular vestibule ( ∼26 Å below the substrate binding site ) to the I_apo state . As the EC and IC vestibules in those respective states are open to the EC and IC bulk , the substrate and Na+ can diffuse to the bulk easily , i . e . , they are in a relatively flat free energy surface . Thus , the exact placement of the missing substrate and Na+ to O_i and I_apo states shall not affect the optimal transition paths . For each transition , the initial state was slowly pulled to the targeted state by applying a harmonic restraint to reduce the RMSD between the two states . The selection of atoms for which the RMSD constraint is applied includes the protein heavy atoms , and the ligand Na+ and the 5′ carbon ( Dataset S1 ) of the hydantoin group in the substrate . The speed for RMSD constraint evolution was set at 0 . 00005 Å/step . The simulation was terminated when the system was within a RMSD difference of 0 . 02 Å to the target . Large-scale conformational changes in biomolecules , such as those involved in the transport cycle of Mhp1 , are complex processes taking place on timescales that can be well beyond the limit of brute force molecular dynamics simulations [26] . In this work , we applied the recently developed and emerging string method with swarm of trajectories [25] , [26] , [51]–[54] to obtain optimal paths connecting the structurally available conformational states of the Mhp1 transporter . The string method aims to find the minimum free energy path ( MFEP ) in the subspace of a large but finite set of coordinates , z , referred to as “collective variables” [52] . A path is ordered as a chain of M states or “images” , connecting two stable conformations . In this work , we explore the transition paths between the Mhp1 conformational states of O_i , O_si , and I_apo . These paths complete the transport cycle . To study the conformational transitions of the transporter and the translocation of the substrate and Na+ , the collective variables include the Cartesian coordinates of the transporter backbone atoms , Na+ , and 5′ carbon of 5-benzyl hydantoin ( C5 ) . We only included one atom ( C5 ) in the collective variables for the substrate in order to avoid an orientational bias to the substrate . The initial paths were obtained from targeted molecular dynamics [50] as described above . There are 9 , 70 , and 55 intermediate states , or images , for the path connecting O_i to O_si , O_si to I_apo , and I_apo to O_i respectively . The numbers of intermediate states were chosen so that the average RMSD of the collective variables between adjacent images is less than 0 . 2 Å . For each image , the simulation system has 59775 atoms , including the Mhp1 protein , the substrate , the Na+ , lipid molecules , water , and Na+ and Cl− counter ions . Therefore , the effect of lipid bilayers and water were taken into account explicitly . The iteration of the string method generally followed the procedures by Gan et al . [25] using NAMD [55] and VMD [56] . Each iteration consists of 4 steps: generation of the swarm of trajectories , evolution of the image , run of constraint MD , and re-parameterization . First , for each image , 100 2-ps-long MD simulations were carried out . Second , the coordinates of the collective variables for the 100 trajectories were averaged . Third , 50 ps of MD simulations were then carried out with a strong harmonic constraint ( 40 kcal/mol Å ) on the collective variables to evolve the collective variables to the average drift and relax the rest of the system other than the collective variables . Finally , the images are re-parametrized to ensure that they are evenly distributed in terms of collective variables along the new path , which , in our case , means the RMSD difference of the atoms in the set of collective variables between adjacent images are roughly equal . This final step ensures that the images are not trapped in local minima . Following Ovchinnikov et al . [54] , convergence of the string to the optimal path is evaluated each iteration by monitoring the average RMSD each image has moved from its initial conformation ( solid red line in Figure S6 ) . In addition , we also monitored the average RMSD each image has moved from the same image 4 iterations before ( dotted blue line , Figure S5 ) . Convergence is assumed when both lines reach a plateau . The transition paths are converged for O_i to O_si , O_si to I_apo , and I_apo to O_i after 30 , 48 , and 22 iterations . The three paths complete a transport cycle for Mhp1 and provide 137 energetically-relaxed images for the entire transport cycle . Structures from the path were chosen as appropriate starting structures for umbrella sampling simulations . Umbrella sampling simulations were carried out using the NAMD program [55] . Harmonic constraints were applied to the order parameters using the collective variable module ( colvars ) . Multiple windows were applied to cover the regions of interest . The window size for order parameter r ( thick_gate ) was 0 . 25 Å , while the order parameter r ( EC_thin_gate ) , Sz ( Na+ ) , and Sz ( substrate ) were mapped with a window size of 0 . 5 Å ( Table S1 ) . The force constants were chosen to be 5 kcal/mol to ensure overlapping of the sampling of windows . Each window was sampled for 560 ps of MD simulation , and the last 500 ps of data were used for weighted histogram analysis [28] . Thus , each 2D-PMF was produced from a total simulation time of 0 . 8–2 µs . WHAM program [57] was used to obtain the 2D PMF profiles from the umbrella sampling data . The bin size was set at 0 . 25 Å , and the convergence tolerance was set at 0 . 001 kcal/mol . The 1D-PMF's were integrated from interested regions of the 2D-PMF's following a method used by Allen et al . [27] . The statistical uncertainties were estimated by blocking the data into three blocks . Proper starting conformation for each window subject to umbrella sampling is essential for efficient computation of the PMF's . In the Mhp1 transport cycle , the conformational changes are too large for the allowance of a single starting conformation , because very long simulations are required to relax the protein structure to a specific window of the order parameters . For example , while the biased potential applied in umbrella sampling could force r ( thick_gate ) to a value around 8 . 0 , from either outward-facing ( r ( thick_gate ) ∼5 . 5 Å ) ) or inward-facing ( r ( thick_gate ) ∼10 Å ) structure , extremely long simulation time may be required to relax the rest of the protein . To avoid excessive long relaxation time , targeted molecular dynamics has been applied to obtain intermediate structures for use as starting structures for umbrella sampling [58] . In this study , we use the string method with swarm of trajectories to further relax the path from TMD simulations . To get a starting configuration for a specific window , we pick from the string-method path an intermediate configuration that has similar order parameter values for that specific window . When only one of two order parameters can be matched , priority was put on the one that usually needs more time to relax . For example , when no intermediate image has both order parameters close enough to a window centered at ( r ( thick_gate ) = 8 . 0 Å , Sz ( Na+ ) = −1 Å ) , we'd pick a intermediate image with r ( thick_gate ) around 8 Å , and let the harmonic constraint in umbrella sampling move Sz ( Na+ ) to its designated value ( Figure 6 ) . For instance , in our computation for the 2D-PMF shown in Figure 3A , we used image 32 of from the transition path obtained from swarm-of-trajectories for this window ( Figure S7 ) . The absence of Na+ contamination for constructed apo-states was confirmed for each window of the umbrella simulations by running minimal contact distance analysis ( Figure S8 ) .
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This study provides direct insights of how ion-dependent secondary transporters couples ion gradients to transport of a substrate . We conclude that the apo-form of a protein samples multiple conformational states separated by only small barriers . Binding of a single ion is sufficient to shift this conformational equilibrium towards one of the conformational states . Our work provides atomic detail for an ion-dependent gating that is at the heart of transport across membrane and present in many kingdoms of life .
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[
"Abstract",
"Introduction",
"Results/Discussions",
"Methods"
] |
[] |
2013
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The Molecular Mechanism of Ion-Dependent Gating in Secondary Transporters
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Phenotypic plasticity is a vital strategy for plants to deal with changing conditions by inducing phenotypes favourable in different environments . Understanding how natural selection acts on variation in phenotypic plasticity in plants is therefore a central question in ecology , but is often ignored in modelling studies . Here we present a new modelling approach that allows for the analysis of selection for variation in phenotypic plasticity as a response strategy . We assess selection for shade avoidance strategies of Arabidopsis thaliana in response to future neighbour shading signalled through a decrease in red:far-red ( R:FR ) ratio . For this , we used a spatially explicit 3D virtual plant model that simulates individual Arabidopsis plants competing for light in different planting densities . Plant structure and growth were determined by the organ-specific interactions with the light environment created by the vegetation structure itself . Shade avoidance plastic responses were defined by a plastic response curve relating petiole elongation and lamina growth to R:FR perceived locally . Different plasticity strategies were represented by different shapes of the response curve that expressed different levels of R:FR sensitivity . Our analyses show that the shape of the selected shade avoidance strategy varies with planting density . At higher planting densities , more sensitive response curves are selected for than at lower densities . In addition , the balance between lamina and petiole responses influences the sensitivity of the response curves selected for . Combining computational virtual plant modelling with a game theoretical analysis represents a new step towards analysing how natural selection could have acted upon variation in shade avoidance as a response strategy , which can be linked to genetic variation and underlying physiological processes .
In the course of evolution , plants have evolved traits often specific to a certain environment . When growth conditions change , the selection pressure on trait values change , which subsequently can change selection on genotypes . However , due to phenotypic plasticity one genotype can exhibit multiple phenotypes ( i . e . multiple trait values ) depending on environmental conditions [1–3] , which helps a plant to survive across different environments . The extent to which plasticity is adaptive depends on the environmental conditions , the reliability of the cues that signal the ( change in ) environmental conditions , and the costs related to phenotypic changes and plasticity itself [4–7] . Although considerable genetic variation in plasticity has been documented in various species [8–11] , it remains unclear how variation in plasticity is the result of direct evolutionary selection processes on a certain trait or a consequence of selection for other traits [12] . Evolutionary and ecological population models are widely used to explain genetic variation and species composition in different environments , and these models are often based on evolutionary game theoretical principles [13–17] . These models implicitly assume that variation in trait values is entirely due to genetic variation among genotypes . This implies that these models essentially predict selection for different genotypes in different environments when these environments select for different trait values . However , if plasticity would be considered in evolutionary game theoretical models as the ability of a genotype to change its trait value in response to environmental conditions , selection for different trait values in different environments would not necessarily lead to selection for different genotypes . In this paper we take the first step in exploring how variation in phenotypic plasticity could be the result of natural selection in different environments . Phenotypic plasticity is the result of plastic responses that are driven by physiological processes , and these responses are directly the result of environmental cues . To this end , the physiological processes underlying the plastic responses to environmental cues have to be quantitatively linked to trait values and to the performance of individual plants in various environments . Considering plasticity as a trait in itself and considering variation in plasticity across genotypes is required to analyse to which extent natural selection may have acted on variation in plastic responses . In this study we focus on plastic responses to light competition in vegetation stands of varying planting density and associated neighbour-plant proximity . Plants growing at high density ( close proximity to neighbour plants ) typically exhibit greater elongation rates of leaf-supporting structures ( i . e . internodes and/or petioles ) , reduced branching and greater leaf inclination angle than plants growing at low density [18–22] . One of the primary signals that induces these shade avoidance responses [23] is a reduction of the red to far-red ratio of light ( R:FR ) , as plants selectively absorb red and reflect far-red light [20] . The reduction in R:FR light perceived by an individual plant is therefore considered a cue for neighbour proximity [24] , reviewed in [23]; a low R:FR ratio signals that neighbours are close ( high density ) and a high R:FR ratio indicates that neighbours are farther away ( low density ) . In addition , R:FR light conditions are also affected by the 3D structure of the canopy [22 , 25 , 26] and by self-shading [27] . These alternative causes of changes in the R:FR ratio may decrease the reliability of R:FR as cue for neighbour proximity and future light availability . To be able to analyse to which extent natural selection may have acted on variation in shade avoidance responses to R:FR , it is required to consider the feedback between the R:FR cue and the plant phenotype . Changes in R:FR induce responses at the organ-level that cause changes in plant architectural phenotype , which in turn affects light capture for growth . The changed phenotype , in turn , changes the light environment and associated R:FR conditions , inducing new sets of responses and this continues throughout plant development . This feedback can be captured in so-called functional-structural plant models ( [28] , also called virtual plant models ) that can mechanistically simulate the interaction between plant 3D structure , growth , and the light distribution within the canopy [29 , 30] . While taking into account the phenotype-environment feedbacks created by the vegetation itself , R:FR induced organ-level plastic responses and variation within these responses can be realistically scaled up to whole-plant performance at vegetation level [31 , 32] . We utilize a recently developed and validated virtual plant model [27 , 31] that simulates Arabidopsis ( Arabidopsis thaliana ) rosette growth based on light absorption for photosynthesis and growth and induces phenotypic changes via a plastic response curve allowing plants to dynamically change their phenotype during growth . The current model simulates the consequences of specifically petiole and lamina plasticity [33] for whole-plant performance in different environmental conditions based on an organ level plastic response curve that describes the sensitivity of the relative petiole and lamina response to R:FR . The response curve is treated as a trait itself , and different shapes of the curve represent different plasticity strategies with the value α ( Fig 1 ) . Petiole plasticity entails petiole elongation in response to decreasing R:FR and can put the leaves in a higher strata of the canopy to increase light capture . Lamina plasticity entails lamina growth reduction in response to decreasing R:FR and can negatively affect light capture because it reduces the total lamina area . Therefore these two organ-specific plasticities are considered antagonistic . We combine this virtual plant modelling approach with evolutionary game theory ( Box 1 and Fig 2 ) to analyse the extent to which variation in plastic responses could be the result of natural selection in different competitive environments . Different planting densities represent different competitive environments . Specifically , we search for convergence stable evolutionary stable strategies ( cESS , [17 , 34] ) at five different planting densities . A cESS ( with strategy value α* ) is a strategy that is evolutionary and convergence stable , which means that i ) a resident population with trait α* cannot be invaded by a rare mutant with a trait value of α ( both locally and globally ) and ii ) a mutant that has a trait value closer to α* than the trait value of the resident , can invade the resident population , if the resident population has any other value than α* [34 , 35] . In theory , the cESS definition also often requires the resident population to reach carrying capacity before invasion of a rare strategy [36 , 37] . However , in this study we assume that the resident population is at carrying capacity in the density tested as planting density is the environmental factor of interest . To summarise , we ask the question to what extent natural selection may have acted on , or resulted in , variation in plastic responses by selecting different plastic shade avoidance response curves at different planting densities . We hypothesize that different plastic response curves will be selected at different planting densities if a given R:FR ratio signals a different level of future neighbour shading across these planting densities . In addition , we also explore the extent to which selection for plastic response curves depends on the cost trade-off associated with the petiole versus lamina responses to R:FR . The latter is expected to influence the selected plastic response curves because petiole and lamina responses have opposite effects on light capture and therefore influences plant performance .
To illustrate how plant growth in the virtual Arabidopsis model depends on planting density ( Fig 3 ) , we simulated the growth of plants within monomorphic vegetation stands consisting of plants that do not exhibit R:FR induced plasticity related to petiole elongation or lamina growth reduction ( plasticity strategy α = 0 in Eq 1 ) at different densities . During canopy development , interaction between leaves of neighbour plants ( see Methods and ref . [31] ) resulted in increased leaf angles and lamina growth resulted in increased total leaf area index ( Supporting Information S1 Fig ) . Together these processes decreased the average R:FR ratio perceived by the plants ( Fig 3A ) . In addition to the decreasing R:FR ratio , light availability per individual plant also decreased during canopy development due to the presence of neighbour plants . Therefore , individual plant growth , represented by total accumulated biomass , was , at the end of canopy development , lower at high than at low densities ( Fig 3B ) . Biomass allocation to different organs varied over time and varied with density because allocation of carbon to growing organs depended on the total available carbon due to light capture , and on the relative growth rates and the total number of growing organs at any time step ( for model description see Methods ) . Under weak competition for light ( low densities ) , the percentage of biomass allocated to the petioles decreased near the end of canopy development ( Fig 3C ) because sufficient carbon was available to reach potential growth of petioles and laminas and leftover carbon was additionally stored in laminas and the root . On the other hand , under strong competition for light ( e . g . at high densities of 1600 and 6400 plants m-2 ) there was relative low carbon available for growth of all organs . From this low available carbon , a higher fraction was invested in petioles and relative little in laminas and the root , by which the percentage of carbon allocated to the petiole increased ( although slightly at 6400 plants m-2 ) . To illustrate how the plasticity strategy ( shape of the plastic response curve ) affects the total plant biomass and the organ specific biomass allocation , monomorphic vegetation stands with different plasticity strategies were simulated ( according to the Average scenario ) . Total plant biomass decreased , in general , with increasing plasticity strategy ( Fig 4A ) , generally being highest for non-plastic individuals . This suggest that monomorphic populations ( all individuals having the same plasticity ) with low or no levels of plasticity generally perform better than populations with high levels of plasticity . There were exceptions to this trend , especially being that at the highest density ( 6400 plants m-2 ) biomass was higher at 0 . 2 plasticity level than for the 0 level ( non-plastic plants ) . This result suggests that at very high densities some level of plasticity may have a benefit for population level light capture and performance . During canopy development the plasticity strategy influenced the biomass allocation to petioles and laminas , which resulted in finally different percentages of biomass invested in petioles and laminas ( Fig 4B & 4C ) . Plants with a high plasticity strategy ( high α value ) allocated relatively more biomass to the petioles and less to the laminas because these plants induced petiole and lamina plasticity at a relatively high R:FR earlier during canopy development . The differences between plasticity strategies on organ biomass allocation are represented in the organ-rank specific sizes , which embody the rosette phenotype of the plant ( S2 Fig ) . The final percentage of biomass allocated to the petioles or laminas was different between planting densities because the dynamically changing R:FR influenced the plastic responses on top of the differences in light availability during growth and the organ specific growth rates , as illustrated previously ( Fig 3 ) . To determine how natural selection may have acted on variation in the plastic response curve , and how this selection may depend on the competitive environment , we performed an evolutionary game theoretical analysis in which we searched for cESS ( see introduction for the definition ) at five planting densities . The virtual plant model simulated the performance of a mutant within a resident population for different combinations of plasticity strategies for the mutant and the resident population ( see Fig 2C ) at five planting densities ( S3 Fig ) . These values were used to calculate the invasion exponents of the mutants , which were used to construct pairwise invasibility plots: positive and negative values of the invasion exponent relate to positive ( blue ) and negative ( red ) invasibility ( Figs 5A–5E , 6A–6E and 6K–6O ) . To aid the interpretation of these pairwise invasibility plots and help identifying possible cESS , the values of the performance of the mutant within a resident population were interpolated with a non-linear smoother after which the invasion exponent of the mutant was calculated ( Figs 5F–5J , 6F–6J and 6P–6T , see Methods for details ) . By definition , the identity line in the invasibility plots represents the case where the performance and strategy value of the mutant are identical to the performance and strategy value of the residents ( 1:1 line ) . A second isocline ( if present ) represents the plasticity values where the performance of the mutant equals that of the resident but without the mutant and resident having the same plasticity value . The point where the identity line and the isocline intersect corresponds to a singular strategy that could represent a cESS . In graphical terms the singular strategy is a cESS ( with value a* ) when moving up or down from the identity line no mutant has higher performance compared to the resident performance ( positive invasion exponent of the mutant ) ; and when starting with a resident population that is left of a* , a mutant closer to a* should have a higher performance than the resident population ( the same holds for residents right from a* ) . The extent to which the mutant can be closer to a* is determined by the second isocline . If the region between the isocline and the identity line does not include the horizontal line through a* , a singular strategy cannot invade all resident populations directly , but only through a series of stepwise mutations . Alternatively , the singular strategy could represent a branching point or evolutionary repeller ( see [34] for an accessible treatment ) . The pairwise invasibility plots based on the average petiole and lamina responses ( Fig 5 , see S4 Fig for the confidence intervals ) show possible cESS at all densities , except for 6400 m-2 . The cESS per density corresponds with different plasticity strategy values; higher plasticity values were selected at higher planting densities ( Fig 5 ) . This represents selection for plastic response curves with increased sensitivity for R:FR at higher densities . At the highest density ( Fig 5J ) a second isoclines is not present , by which a possible cESS could not be determined within our tested values . However , both the discrete and smoothed invasibility plots suggest that selection would result in plants with a high plasticity strategy ( high R:FR sensitivity ) probably beyond the tested values . At 1600 plants m-2 ( Fig 5I ) the pairwise invasibility plot has a complex shape around plasticity values of 0 . 5 and 0 . 6 . The calculated values with their confidence intervals ( Fig 5D and S4D Fig ) show that the invasion exponents of plants with plasticity 0 . 5 and 0 . 6 in resident populations of 0 . 6 and 0 . 5 respectively are around zero , which means that more simulations are required to determine the precise value of the invasion exponents at that part of the pairwise invasibility plot . Finally , we analysed the extent to which the above-mentioned cESS per density depended on the relation between petiole and lamina responses . In two additional scenarios , we either decreased ( Weak scenario ) or increased ( Strong scenario ) the lamina responses relative to petiole responses upon perception of R:FR ( parameter n in Eq 2 , see Methods ) . The balance between petiole and lamina responses , although both have the same plasticity strategy , relates to a trade-off regarding light capture during competition; increased light capture due to longer petioles versus decreased light capture due to smaller lamina area . Although not all pairwise invasibility plots identified a single cESS , the balance between petiole and lamina responses did clearly affect the mutant’s invasion exponents at different densities ( Figs 5 and 6 ) . When lamina responses were relatively small , there was selection towards plants with a slightly higher plasticity strategy value , evidently at the three lowest densities ( compare Weak scenario Fig 6A–6J with Average scenario Fig 5 ) . Higher plasticity strategy values relate to more sensitive plastic response curves . At 1600 plants m-2 ( Fig 6I ) there were three intersections between isoclines and the identity line , which suggests that the invasibility environment has a complex shape . However , similar as for the Average scenario , the confidence intervals of the invasibility exponents of plants with plasticity strategy value of 0 . 3 up to 0 . 6 included zero ( S4I Fig ) , which suggest that strong conclusions about the invasibility of these mutants within their corresponding resident populations cannot be made with confidence based on the data . Invasibility exponent values around zero suggest that not one single plasticity strategy with a narrow range value would eventually dominate the population , but that plants with a broader range of plasticity values could persist together . At 6400 plant m-2 ( Fig 6J ) there was no second isoclines , which means that within the tested values no conclusive cESS could be determined . However , the invasibilty plot suggests that a potential cESS lies higher than the 0 . 7 tested here . With relatively strong lamina responses there was selection for plants with a lower plasticity strategy compared to weak and average lamina responses ( Figs 5 and 6 ) . Although at 100 and 400 plants m-2 ( Fig 6P and 6Q ) there was no conclusive cESS , we expect that plants with a zero or negative plasticity strategy would be selected for . Negative plasticity strategy values would indicate that plants would invest less carbon in petioles and more carbon in laminas in response to lower R:FR . At 6400 plants m-2 , the second isocline crossed the identity line multiple times ( Fig 6T ) . This suggests again that the invasibility environment could be complex , and that not one single plasticity strategy would dominate a population at 6400 plants m-2 . More simulations of various mutant-resident combinations within our tested range could verify the exact shape of the complex invasibility environment . Additional replication simulations could also verify that the observed complex invasion environments are not due to stochasticity in the model outcomes .
We hypothesized that different plasticity strategies would be selected at different planting densities if a given R:FR value signals different levels of future neighbour shading across planting densities . Our results show that selection would lead to different response curves at different planting density ( Fig 5 ) . We argue that this may be partly due to the fact that the severity of future neighbour shading ( expressed in reduced photosynthetic active radiation ) that is signalled by a given R:FR value , differs between planting densities . The amount of photosynthetic active radiation received by laminas whose petioles perceived a R:FR around 1 . 0 decreased with density ( S5 Fig ) . In low density stands , a drop in R:FR during canopy development is mainly caused by self-shading , whereas in high density stands , R:FR reduction is more strongly determined by neighbour-shading [27] . This means that at different plant densities , a given R:FR ratio has different meanings regarding light availability and the level of impending light competition . R:FR is thus not a fully reliable cue for future shading over a range of densities . This prevents selection for one single plastic response curve over a range of densities . In low densities , when light competition is low and R:FR changes are mostly caused by the plant itself , a less sensitive response to a given R:FR is favourable to avoid relative long petioles and small laminas that have negative consequences for light capture and thus plant performance [19] . In high densities , a more sensitive response curve is favourable to create long petioles that can avoid neighbour shading and increase light interception for plant growth and performance . Our conclusion , that selection at different densities can result in variation in phenotypic plasticity if the signal is unreliable for future environmental conditions , agrees with previous studies that concluded that signals can have different meanings in different environments , such as R:FR values in open versus closed-canopy forests [10 , 38 , 39] , which could explain the observed variation in plasticity . Our model approach is different from these studies in that it focussed on a plastic response curve that represented the potential to induce relative trait changes instead of absolute trait values and value differences upon an environmental change . Our plastic response curve could be linked more readily to underlying genetics and physiological processes , as shown for Arabidopsis mutants deficient in transcriptional regulators or hormones [31] . Consequently , our modelling approach represents a step forward in linking selection processes to the genetic basis and physiological processes underlying phenotypic plastic responses . We explored the extent to which selection for plastic response curves at different densities would depend on the trade-off between petiole and lamina responses that respectively would relate to increased and decreased light capture . Changing the strength of lamina responses relative to petiole responses influenced selection for the response curves over the full range of planting densities . Plants with higher plasticity strategy values were selected for when petiole responses were associated with lower lamina responses ( Figs 5 and 6 , Weak scenario compared to the other scenarios ) . This indicates that when inducing plastic responses has lower negative consequences due to lower lamina growth reduction , selection can result in higher R:FR sensitivity . In contrast , plants with lower plasticity strategies were selected for when petiole elongation was associated with a stronger reduction of lamina growth in response to R:FR ( Figs 5 and 6 , Strong scenario compared to other scenarios ) . Although at the lowest densities no cESS was found within the tested strategies , the pairwise invasibility plots suggest selection in the direction of plants with zero or negative responses to R:FR . A negative value would indicate that plants would invest less carbon in petiole growth and more in lamina growth in response to lower R:FR . However , based on experimental work [33] we conclude that these inverse petiole and lamina responses to decreasing R:FR are not plausible in Arabidopsis . In general , we conclude that plants without plastic responses to R:FR would be selected for ( i . e . plants with low sensitivity for R:FR ) when performance consequences of showing plasticity are too negative . This is in accordance with other theories . For example , studies related to the evolution of phenotypic plasticity [6 , 40] state that selection would favour non-plastic responses if costs of inducing plastic responses are high . In addition , error management theory [41] would predict that when phenotypic responses have high costs , selection would favour less sensitive response curves responsible for the plastic response . Altogether , our results are a quantitative example of the influence of cost trade-offs related to plastic responses on the selection for specific plastic responses . The 3D virtual Arabidopsis model did not include other light signals than R:FR that can signal neighbour proximity and future shading [42] . For example the combination of low blue and low R:FR can indicate stronger shading and therefore can induce stronger shade avoidance responses than either light treatment alone [43] . Importantly , low blue and low R:FR signals can be created either by increased vegetation density of plants with roughly similar sizes or by tall trees in a closed-canopy forest . This suggests again that the reliability of an environmental cue to induce a response depends on the competitive environment . The location of signal detection on the plant can also affect reliability . For example , perception of low R:FR at the lamina tip is more reliable as cue for neighbour-proximity than R:FR perception at the petiole [27] . The regulation of multiple signals and their interactions are still poorly understood and need to be further studied to better understand selection for organ-specific plasticity under natural conditions . In addition , besides petiole and lamina plasticity , our simulations did not consider responses to R:FR other than leaf angle increase ( see Methods ) . Other responses such as specific leaf area increase [44] , flowering time acceleration [8] , root development reduction [45] and defence reduction [46] can also affect plant competition for light [32 , 47 , 48] and can thus influence selection for specific plastic response curves . We also did not consider any form of mechanical penalty on developing longer petioles , which would occur in natural systems and can affect plant performance in a density dependent manner . For example , the vulnerability to mechanical damage or hydraulic limitations for longer petioles can depend on density; in high density canopies , leaves can get mechanical support from surrounding leaves or protection against wind , by which plants have a lower risk of mechanical failure even if investment in supporting tissues is low . In low densities , this protection is low ( or absent ) and investment in longer petioles requires additional carbon allocation for petiole stability , which may affect final plant performance . If inducing phenotypic changes has higher fitness costs in low than in high planting density , we would expect an even larger effect of planting density on the selected response curves than what we found in the present study . Although we considered a wide range of planting densities ( regularly spaced ) , we did not consider that inter-plant distances within a natural vegetation is normally heterogeneous in space , which would make the light environment even more variable . Our result that different densities select for different plasticity strategies , suggests that when density is more variable in time and space transient evolutionary dynamics may prevail allowing different strategies to persist transiently [49] . Performing an analysis in which the distance between neighbour plants within the vegetation is heterogeneous or the density between successive generations is variable , could be the subsequent step towards identifying if and how many genotypes could persist over time . In theory , the definition of an cESS requires a population to reach carrying capacity before invasiveness of a rare mutant is tested [36 , 37] . But in our analyses planting density was the environmental factor in question , and could thus not be changed as part of the analyses . We thus implicitly assumed that the different densities reflected different carrying capacities as determined by the overall environment ( e . g . by resource availability ) . A consequence of the fact that different planting densities resulted in different cESS could be the following: if a given plastic response strategy is a cESS at a specific density , but the carrying capacity is at another density , then one may expect some kind of transience . Eventually it could be expected that different plastic response strategies will persist transiently if density changes strongly between years , or it could be expected that one single strategy would persist if the density stays constant around the carrying capacity of the population . It would be challenging and interesting to consider evolutionary and ecological dynamics while exploring the evolution of plastic responses in future studies . Our analyses indicate that different planting densities can select for different plastic response curves that represent different R:FR sensitivity for petiole and lamina responses . This is consistent with the considerable genetic variation in shade avoidance responses observed in Arabidopsis and in several other species [8–11] . This result seems in part to be explained by the reliability of the R:FR signal as a cue for future neighbour-shading that varies per density . In addition , selection for specific shapes of the plastic response curve is influenced by the trade-off between responses that have generally positive ( petiole elongation ) versus negative ( lamina growth reduction ) consequences for light capture and therefore plant performance . Combining virtual plant modelling and evolutionary game theory is a new step toward analysing how phenotypic plasticity , and the underlying sensitivity to an environmental signal , can affect the composition of genotypes over a range of environments . Promising next steps could be including responses to multiple light signals , considering environmental dependence of inducing phenotypic plasticity and regarding environments to be more variable in time and space .
Here we summarise the model description and specify the most important model components , assumptions and choices . More details can be found in ref [31] . Simulated Arabidopsis plants emerged from seeds and grew for 46 days into an adult rosette plant with multiple leaves ( petiole and lamina ) produced in a spiral pattern , and a single root . The leaves captured light for photosynthesis and were also sinks for carbon . The root only functioned as carbon sink and had no effect on aboveground growth . Organ initiation ( e . g . time between leaf emergence ) and geometric representation such as orientation of the leaves and the shape of the leaves were simulated using empirical relationships . Plant growth was driven by total carbon assimilation and organ-specific carbon allocation and plastic responses induced by the R:FR environment . The simulated light source emitted photosynthetic active radiation , red and far-red light and in each model time-step ( representing 24 hours ) these stochastic emitted light rays were reflected , transmitted and absorbed by the petioles and laminas individually according to their wavelength-specific spectral properties . The light source emitted an R:FR ratio of 2 . 3 , photosynthetic active radiation with an intensity of 220 μmol m-2 s-1 , and total daily light intensity was calculated based on 9 hours light per day , representing growth chamber conditions under which validation experiments had been carried out [31] . Plants were placed in regular places grids with different inter-plant distances to create different planting densities: 100 , 400 , 711 , 1600 and 6400 plants m-2 , which bracketed the densities in the validation experiment [31] . Border effects were minimized by using the plot replication functionality of GroIMP; canopies were replicated 20 times in the x and y direction and light conditions were calculated and averaged for these 400 canopies . The local light environment perceived by the individual plants ( at the organ level ) was created by the specific 3D structures of all plants within the canopy itself . Every individual organ absorbed light which enabled the model to calculate the light partitioning over all individual plants within the canopy . This way , we did not have to make assumptions about light partitioning over individuals with different trait values , as has been done in most other game theoretical light competition models [52–54] . Thus , our model simulates competition for light as an emergent property and not an input parameter . Total accumulated biomass stored in root , laminas and petioles after 46 days of growth was used as a measure of plant performance . The rationale is that seasonal biomass scales with seasonal seed production , which for an annual plant like Arabidopsis equates to life time reproduction [55] . Generally for light-demanding species like Arabidopsis , under light competition this correlation is very strong [21 , 56 , 57] . Plants could not die during canopy development , they only stopped growing when light capture was insufficient . The laminas individually absorbed photosynthetic active radiation that was converted into growth substrates via photosynthesis , assuming a negative exponential light response curve [58] . Total growth substrates per individual plant were summed into a central pool and subsequently partitioned over all growing organs through the relative sink strength principle [59] . The relative sink strength of an organ was expressed as a fraction of total plant sink strength , and determined the demand for substrates for each organ in relation to its age . Organ sink strength was defined as its potential growth rate and calculated using the beta growth function [60] . The beta growth function calculated the growth rate at organ age based on measured maximum organ size and duration of organ growth . Leaves senesced after 40 days of age . Thus primarily , the organs individually grew in time and 3D space based on the allocated substrates they received based on their relative sink strength . This means that when petioles receive more substrates due to an increase in relative sink strength , automatically other sinks such as leaves and roots will receive less . Lamina and petiole growth was influenced by shade avoidance responses that were induced by changes in the R:FR ratio . In this study we focus on the petiole elongation and downregulation of lamina growth [33] . Leaves also showed leaf angle responses due to neighbour proximity and R:FR conditions , see ref [27 , 31] , but this was not changed in between simulations . Leaves increased their angle with 16 degrees per time-step when the distance between neighbouring leaves was smaller than 2 mm ( mimicking touching of leaves [22] ) or when R:FR perception at the lamina was below a threshold of 0 . 5 . These settings were chosen based on wild-type Arabidopsis responses and allowed plants to induce leaf angle increase depending on planting density . Petiole elongation and lamina growth downregulation responses occurred every time-step based on the response curve that illustrates the relationship between relative organ growth and R:FR perception at the organ itself ( Fig 1 ) . The response curve was found by fitting an empirical relationship through experimentally obtained petiole elongation data [31]: F=min ( 2 , ( R:FRRFRcontrol ) −α ) ( 1 ) F is the relative organ growth factor . R:FR and RFRcontrol represent the actual experienced and the control R:FR ratio ( RFRcontrol is 2 . 3 , related to growth conditions in the validation experiment [31] ) . Parameter α ( dimensionless ) determines the curvature of the response curve and is referred to as the ‘plasticity strategy’ ( See Fig 1 for variation in the curvature related to α values ) . Note that F will be 1 when the plasticity strategy α is 0 , which will not induce petiole or lamina plastic responses . The higher the value of α the more sensitive the genotype is to R:FR decrease . We set a maximum of 2 to F , to prevent organs to change their own size more than twice within one day , since this has never been experimentally observed . Variation in the shape of the response curve represents variation in the physiological regulation of the response , as observed among response curves of Arabidopsis mutants [31] . Petiole elongation was simulated by multiplying petiole length with F , taking the R:FR perceived by the petiole itself as input [27] . This petiole elongation was calculated every model time-step after simulating petiole growth based on carbon allocation through the relative sink strength principle . Consequently , the petiole increased its demand ( sink strength ) for carbon substrates for the next time-step to account for the length increase . Lamina growth downregulation was simulated by decreasing the carbon demand ( sink strength ) ( Eq 2 ) : D= ( DpFn ) ( 2 ) where D is the actual lamina carbon demand ( mg day-1 ) as affected by R:FR perception , Dp the potential lamina carbon demand ( mg day-1 ) as calculated by the beta growth function ( see above ) , F the relative growth factor based on R:FR perceived by the lamina ( calculated with Eq 1 ) , and n a dimensionless coefficient that alters the strength of the lamina growth downregulation relative to the petiole elongation response . The default setting of the model had n = 2 , also referred to as the Average scenario , see Model scenarios ) . An increase of n indicates that the demand for lamina growth decreases by which the lamina receives less carbon , while therefore more carbon is available for petiole growth . The n value only differed between scenarios . Note that the decreased carbon demand for a given lamina has direct consequences for the absolute carbon allocation to all growing organs; decreased carbon allocation to a lamina can be related to increased carbon allocation to a petiole . Plastic responses could only occur during the growth phase of the specific organ that is set by the beta-growth function . In general , evolutionary game theoretical principles assume that over evolutionary time the strategy of a population can change when a rare individual with a different strategy than the population can invade the standing population . A rare mutant with a different strategy than the standing population ( also called ‘resident’ population ) can invade the standing population when it has a higher performance than the average performance of individuals of the standing resident population , and in theory this invasion will lead to replacement of the population , provided that the change caused to the resident phenotype by the mutant is sufficiently small . With the virtual plant model we captured this by simulating different canopies in which the middle plant of the canopy has the same strategy or a different strategy ( also called mutant ) than the surrounding resident population ( Fig 2 ) . Within this study the different strategies were represented by the shape of the plastic response curve with α ranging from 0 to 0 . 7 ( See Eq 1 and Fig 1 ) . A full matrix of combinations of mutant within a resident population has been simulated , resulting in 64 different canopies ( Fig 2C ) . In total , we replicated these 64 canopies 20 times at five densities and for three scenarios ( see below Model scenarios ) , resulting in a total of 19 . 200 simulations . The simulated performances of mutants within resident populations were used to calculate the invasion exponents of the mutants , calculated by log ( mean ( mutant performance ) /mean ( resident performance ) ) . These invasion exponents resulted in pairwise invasibility plots ( Fig 2D ) . To smooth out stochastic variation simulated by the virtual model , and to be able to get a more detailed estimation of a possible cESS , the simulated performances of the mutants within the resident populations were interpolated with a non-linear smoother after which the invasion components were calculated ( i . e . a general additive model [61] using the ‘mgcv’ package in R https://cran . r-project . org/web/packages/mgcv/mgcv . pdf , see S1 Script for details ) . To explore if selection at different planting densities would result in different cESS , we simulated a full matrix of mutant-resident canopies with plasticity values ranging from 0 to 0 . 7 at five densities . In addition , we tested how the estimated cESS at different densities alter when changing the strength of lamina responses relative to petiole responses to R:FR . This was done through varying the parameter n ( in Eq 2 ) , which determined the sink strength of the lamina and therefore the potential lamina growth . In the Average scenario we used the model settings as describe above in which average lamina responses were relatively equal to petiole responses ( n in Eq 2 is set to 2 ) . In the Weak scenario , we simulated a weak downregulation of the sink strength of the lamina by setting the value of n in Eq 2 to 1 , and in the Strong scenario we simulated a strong downregulation of the lamina sink strength by setting n to 4 . Comparing the three scenarios gives insight in the relative importance of the responses that have antagonistic consequences for light capture and therefore plant performance; petiole elongation has generally beneficial consequences by placing the leaf in a higher strata of the canopy versus lamina growth downregulation has generally negative consequences because the size of the lamina that is responsible for capturing light will decrease .
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Plants are able to respond to changes in the environment . Particularly , plants can show different structural traits ( e . g . stem height and leaf size ) in different planting densities . These trait changes are the result of so-called plastic responses that can be induced by changes in the light spectrum . Although a great part of the physiological processes underlying these plastic responses have been identified , it remains unclear how these plastic responses , and variations therein , can be the result of selection . In this paper we analyse selection on different plastic responses within different dynamic competitive environments . We use a 3D virtual plant model that simulates realistic plant growth based on light absorption , photosynthesis and specific light signals that induce changes in leaf growth . The 3D model simulated various competitive vegetation stands consisting of plants with different plastic responses at different planting densities . We conclude that selection in different densities results in different plastic responses . We advocate that our modelling approach allows for analyses related to selection on plastic responses itself , instead of on specific trait values in different environments , and is therefore an important new step forward in understanding the role of plastic responses for plant performance .
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2019
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Variation in plastic responses to light results from selection in different competitive environments—A game theoretical approach using virtual plants
|
Axonal degeneration is a hallmark of many neuropathies , neurodegenerative diseases , and injuries . Here , using a Drosophila injury model , we have identified a highly conserved E3 ubiquitin ligase , Highwire ( Hiw ) , as an important regulator of axonal and synaptic degeneration . Mutations in hiw strongly inhibit Wallerian degeneration in multiple neuron types and developmental stages . This new phenotype is mediated by a new downstream target of Hiw: the NAD+ biosynthetic enzyme nicotinamide mononucleotide adenyltransferase ( Nmnat ) , which acts in parallel to a previously known target of Hiw , the Wallenda dileucine zipper kinase ( Wnd/DLK ) MAPKKK . Hiw promotes a rapid disappearance of Nmnat protein in the distal stump after injury . An increased level of Nmnat protein in hiw mutants is both required and sufficient to inhibit degeneration . Ectopically expressed mouse Nmnat2 is also subject to regulation by Hiw in distal axons and synapses . These findings implicate an important role for endogenous Nmnat and its regulation , via a conserved mechanism , in the initiation of axonal degeneration . Through independent regulation of Wnd/DLK , whose function is required for proximal axons to regenerate , Hiw plays a central role in coordinating both regenerative and degenerative responses to axonal injury .
Axon degeneration can be induced by a variety of insults , including injury . When an axon is transected from the cell body , the distal axon “stump” degenerates through a regulated self-destruction process called Wallerian degeneration [1] . This process appears to be actively regulated in axons; however , the endogenous cellular machinery that regulates and executes this degeneration process is poorly understood . Previous studies have implicated a role for the ubiquitin proteasome system ( UPS ) in Wallerian degeneration , since inhibition of UPS leads to a delay in the early stages of degeneration [2] , [3] . One explanation for this result is that the UPS mediates bulk protein degradation via the combined action of many ubiquitin ligases . However an alternative model is that one or several specific E3 ligases target the destruction of key inhibitors of the degeneration process . Here , using an in vivo assay for Wallerian degeneration in Drosophila , we identify an essential role for a specific E3 ubiquitin ligase in promoting Wallerian degeneration . The ligase , known as Highwire ( Hiw ) in Drosophila , Phr1 in mice , is well known from studies in multiple model organisms for its conserved functions in regulating axonal and synaptic morphology during development [4]–[12] . We found that mutations in hiw strongly inhibit the initiation of Wallerian degeneration in multiple neuronal types and developmental stages . Until recently [13] , [14] , such a strong loss-of-function phenotype has not been reported for this process . Mutations in hiw also inhibit synaptic retraction caused by cytoskeletal mutations [15] . However the finding that Hiw promotes axonal degeneration was originally perplexing , since a known target of Hiw , the Wallenda ( Wnd ) MAP kinase kinase kinase ( also known as dileucine zipper kinase [DLK] ) [16] , [17] , was found to promote Wallerian degeneration in mouse DRG and Drosophila olfactory neurons [18] . In hiw mutants Wnd levels are increased [9] , [16] , [17] , however degeneration is inhibited . A partial explanation for these opposing results is that Wnd plays a protective role in some neuronal types [19] , [20] . However this alone could not account for the essential role of Hiw in Wallerian degeneration of all neuron types . These findings pointed to the existence of additional targets for Hiw . Recent studies in vertebrate cultured neurons have suggested the NAD+ synthase enzyme nicotinamide mononucleotide adenyltransferase 2 ( Nmnat2 ) as an attractive target of post-translational regulation in axons [21] . Nmnat2 is transported in axons , where it has a short protein half-life , and neurons depleted for Nmnat2 undergo axonal degeneration [21] . Moreover , many gain-of-function studies suggest that increasing the activity of an Nmnat enzyme in axons can effectively delay Wallerian degeneration [22] , [23] . The most classic example of this comes from studies of the Wallerian degeneration Slow ( WldS ) gain-of-function mutation in the Nmnat1 locus , which causes a greater than 10-fold delay in the degeneration of injured axons [24] . However , despite the plethora of studies examining the effect of overexpressing Nmnat enzymes [23] , very little is known about the role of the endogenous Nmnat enzymes in axons and how their activity may be regulated . In contrast to the three isoforms in vertebrates , the Drosophila genome contains a single nmnat gene , for which two splice forms are annotated . nmnat is an essential gene , whose depletion in neurons causes neurodegeneration [25]–[27] . Here we find that Hiw and ubiquitination negatively regulate the levels of axonal Nmnat in vivo . Moreover endogenous Nmnat is required , in parallel to Wnd , for mutations in hiw to inhibit degeneration . By down-regulating the levels of Nmnat protein , Hiw promotes the initiation of Wallerian degeneration in axons and synapses . Moreover , through co-regulation of the Wnd/DLK kinase , whose function is required for proximal axons to initiate new axonal growth [28]–[32] , Hiw coordinates both regenerative and degenerative responses to axonal injury .
We used a previously described nerve crush assay [20] , [30] to study the degeneration of motoneuron and sensory neuron axons within segmental nerves in third instar Drosophila larvae . To quantify the degeneration of motoneuron axons , we used the m12-Gal4 driver to label only a subset of motoneurons with UAS-mCD8-GFP ( Figure 1A , 1B , and Materials and Methods ) . In wild-type ( WT ) animals , these axons are completely fragmented within 24 h after injury ( Figure 1A ) [20] . Hiw is a large , highly conserved protein thought to function as an E3 ubiquitin ligase [17] , [33] . Previous studies have suggested that Hiw regulates the ability of axons to regenerate after injury [28] , [30] . Here we investigated whether Hiw plays a role in degeneration after injury . In both hiw null ( hiwΔN ) and hypomorph ( hiwND8 ) mutant animals , axonal degeneration was strongly inhibited . Even 48 h after injury ( which is the latest time that can be visualized before pupation ) the distal stump of injured axons remained intact in hiw mutants ( Figure 1A and 1B ) . The protection from degeneration was also recapitulated in neurons that expressed the dominant negative mutation , hiw-ΔRING ( Figure 1B ) , but not in adjacent neurons that did not express Gal4 . These results strongly suggest that Hiw performs a cell-autonomous function in promoting axonal degeneration after injury . Similarly , we found that overexpression of the de-ubiquitinating enzyme UBP2 [34] delayed degeneration of Drosophila motoneuron axons and neuromuscular junctions ( NMJs ) ( Figure 1B and 1D ) . The hiw mutation also inhibited degeneration of the NMJ ( Figure 1C ) . In wild-type animals , pre-synaptic proteins , such as the MAP1B homologue Futsch , disappeared completely from all NMJ boutons within 24 h after injury while the axonal membrane , detected with anti-HRP antibodies , fragmented into individual spheres ( Figure 1C ) . In hiw mutants , all markers of NMJ structure remained intact ( Figures 1C , 1D , and S1 ) . Expression of hiw cDNA in motoneurons restored their ability to degenerate after injury ( Figure 1D ) . To test whether the distal stump of hiw mutants remained functional , NMJ synapses at muscle 6 were subjected to a standard electrophysiology recording paradigm either before or after injury ( Figure 1E–1H ) . At 24 h after injury , wild-type NMJs were completely silent: no evoked excitatory junction potentials ( EJPs ) were observed ( Figure 1H ) , and only one single spontaneous miniature event ( mEJP ) was observed in all ten recordings ( Figure 1F ) . In contrast , at 24 h after injury , recordings in hiw mutant NMJs showed robust spontaneous mEJPs and evoked EJPs , resembling uninjured hiw NMJs [8] . Hence axons and synapses are functionally intact and resilient to degeneration in hiw mutants . We then tested whether Hiw promotes axonal degeneration in other neuron types ( Figure 2 ) . The sensory neuron axons in larval segmental nerves were also injured in the nerve crush assay , and their distal axons also degenerated in a Hiw-dependent manner ( Figure 2A ) . We then tested the role of Hiw in degeneration of adult neurons , which can be studied over a longer window of time . In wild-type animals , the distal stumps of olfactory neuron axons in the antennal lobe degenerated within 1 d after their cell bodies were removed by antennal lobe transection [2] , [35] . In contrast , in hiw null mutants , olfactory neuron axons remained in the antennal lobe even 20 d after cell body removal ( Figure 2B and 2C ) , which is comparable with the extent of protection by the WldS gain-of-function mutation [2] , [35] . These dramatic phenotypes in multiple neuron types suggest that Hiw plays a fundamental role in the initiation of axonal degeneration after injury . To understand the mechanism for Hiw in Wallerian degeneration we first considered a previously characterized target of Hiw regulation , the Wnd/DLK kinase . A previous study in mouse DRG and Drosophila olfactory neurons found that degeneration is delayed in wnd ( dlk ) mutants [18] . However , in larval motorneurons , we found the opposite result , since mutations in hiw lead to increased levels of Wnd kinase in axons [16] , and overexpression of wnd in motoneuron axons can delay Wallerian degeneration [20] . Consistent with Wnd playing a protective role against degeneration downstream of Hiw , the protection from degeneration in hiw mutants was suppressed in hiw; wnd double mutants , although the suppression was only partial ( Figure 3 ) . In contrast , the synaptic overgrowth and overbranching phenotype in hiw mutants was completely suppressed in the hiw;wnd double mutants [16] . We also noticed that while hiw mutations inhibited degeneration in multiple neuron types , overexpression of wnd did not protect olfactory neuron and sensory neuron axons from degeneration [20] . Hence the degeneration phenotype for hiw mutants could not be accounted for by Wnd alone . This suggested the existence of additional downstream effectors of Hiw during axonal degeneration . A well-known and intensively studied negative regulator of Wallerian degeneration is Nmnat [23] . An increased activity of this enzyme , first discovered in the WldS mutation , can strongly inhibit degeneration after injury [36] . This gain-of-function phenotype for nmnat bears a striking resemblance to the hiw loss-of-function phenotype in its ability to delay the onset of Wallerian degeneration . There is only one nmnat gene in Drosophila and it has been shown to be required for neural integrity [25]–[27] . To disrupt expression of this essential gene post-embryonically , we used the Gal4/UAS system to express double-stranded RNA [37] targeting nmnat , ( UAS-nmnat-RNAi ) , in neurons . Immunostaining with an anti-Nmnat antibody [25] indicated that the knockdown of Nmnat was effective ( Figure S2A ) ; however , it was unlikely to be complete , since neuronal clones that are homozygous mutant for Nmnat undergo spontaneous degeneration in uninjured animals [25] , [26] . In contrast , RNAi-mediated knockdown of nmnat in larva motoneurons did not affect the development or stability of axons and synapses ( Figure S2B ) , and only modestly affected the time course of degeneration after injury ( Figure 4B ) . However knockdown of nmnat strongly suppressed the hiw protective phenotype , both in axons ( Figure 4A and 4B ) and NMJ synapses ( Figure 4C and 4D ) . Similarly , reduction of Nmnat also suppressed the protection from degeneration caused by overexpression of UBP2 ( Figure S3 ) . These results suggest that Nmnat function is an important component of Hiw's role in the degeneration process . Interestingly the NMJ synaptic overgrowth phenotype of the hiw mutants was not suppressed by RNAi knockdown of nmnat ( Figure 4C and 4E ) . This implies that Hiw regulates synaptic morphology independently of Nmnat function , or at least through a mechanism that is less sensitive to Nmnat function than degeneration . In contrast , Wnd is required for synaptic overgrowth in hiw mutants , and data presented below suggest that Nmnat and Wnd function independently . To further probe the relationship between Wnd and Nmnat , we conducted genetic epistasis analysis . Overexpression ( O/E ) of either wnd or nmnat cDNA can delay Wallerian degeneration in Drosophila motoneurons ( Figure 5A–5D ) , so we tested whether the phenotype of O/E nmnat required wnd , and vice versa , whether the phenotype of O/E wnd required nmnat . We found that disruption of wnd function had no effect upon the protection from degeneration by O/E nmnat ( Figure 5A and 5B ) . For the converse experiment , we tested whether knockdown of nmnat by expression of UAS-nmnant-RNAi affected the protection by O/E wnd ( Figure 5C and 5D ) . While this method for disrupting Nmnat suppressed the hiw degeneration phenotype ( Figure 4 ) , it had no effect upon the O/E wnd phenotype ( Figure 5C and 5D ) . These observations suggest that Nmnat and Wnd protect axons from degeneration through independent mechanisms . We then tested whether knockdown of nmnat and wnd by RNA interference had additive effects in suppressing the hiw degeneration phenotype ( Figure 5E and 5F ) . Since nmnat-RNAi rescues the hiw phenotype very strongly on its own at 24 h after injury , we assayed earlier time points , 12 and 18 h after injury , for additive effects with wnd-RNAi . Expression of wnd-RNAi alone in the hiw mutant background caused 42% of the NMJs to degenerate ( including complete degeneration and partial degeneration ) within 18 h of injury , while expression of nmnat-RNAi alone caused 59% of the hiw mutant NMJs to degenerate at this time point . Combined knockdown of both nmnat and wnd led to a nearly complete suppression of the hiw degeneration phenotype , with 92% of the NMJs degenerating ( Figure 5E and 5F ) . Together , these results suggest that Wnd and Nmnat function independently downstream of Hiw in the Wallerian degeneration process ( Figure 5G ) . Hiw and its homologues are known to function within an E3 ubiquitin ligase complex [17] , [33] , [38]–[41] . An attractive hypothesis is that Hiw promotes ubiquitination and protein turnover of endogenous Nmnat protein . Consistent with this hypothesis , we found that knockdown of nmnat suppressed the protection from degeneration caused by overexpression of the de-ubiquitinating enzyme UBP2 ( Figure S3 ) . We therefore asked whether mutation in hiw leads to an increase in the levels of Nmnat protein . Most strikingly , we noticed an appearance of Nmnat protein in the synapse and neurite-rich neuropil of hiw mutants , which was not detectable in a wild-type background ( Figure 6A and 6B ) . We also observed complex changes in the distribution of Nmnat in neuronal nuclei and glia ( Figure S2 ) . To test whether Hiw regulates Nmnat in neurons via a post-transcriptional mechanism , we drove expression of transgenic HA-tagged nmnat cDNA in neurons via an ectopic Gal4/UAS promoter . In hiw mutants , the total level of HA-Nmnat protein , as detected on Western blots , increased in both larval brains ( 3 . 1±0 . 6-fold ) and adult heads ( 5 . 2±1 . 1-fold ) ( Figure 6C ) . By immunocytochemistry , the HA-Nmnat protein ( which represents a splice form that lacks the nuclear localization sequence ) could readily be detected in motoneuron cell bodies ( Figure 6D and 6G ) and axons within segmental nerves ( Figure 6E and 6H ) , but is barely detectable at NMJ synapses ( Figure 6F and 6I ) . In hiw mutants , the levels of HA-Nmnat increased in all compartments , however the 5-fold increase quantified at NMJ synapses was most striking ( Figure 6G–6I ) . The increase in Nmnat protein levels remained in the hiw;wnd double mutant background ( Figure 6E–6I ) , adding further support to the model that Hiw regulates Nmnat protein independently of Wnd . The hiw mutation led to an increase in the levels of transgenic Nmnat , which was expressed via the ectopic Gal4/UAS promoter . We confirmed that the hiw mutation did not increase expression from the different Gal4 drivers used ( ppk-Gal4 , OK6-Gal4 , and BG380Gal4 , unpublished data ) . Hence the regulation of Nmnat by Hiw takes place post-transcriptionally . To test whether Nmnat is regulated by ubiquitination , we overexpressed the yeast ubiquitin protease UBP2 in neurons , which can counteract the function of ubiquitin ligases [34] , [42] . We found that co-expression of UBP2 in neurons with the HA-nmnat transgene caused an increase in the levels of HA-Nmnat protein ( Figure 7A and 7C ) , resembling the hiw mutant ( Figure 6 ) . This suggests that the levels of Drosophila Nmnat are controlled by ubiquitination . We next tested whether the action of the Hiw E3 ubiquitin ligase is sufficient to modify Nmnat protein level in axons and synapses . Co-overexpression of hiw cDNA ( O/E hiw ) with HA-nmnat caused a strong decrease in HA-Nmnat protein in motoneuron axons ( Figure 7B and 7C ) . Because Nmnat protein was difficult to detect at the NMJ ( Figure 6F ) , we also examined the nerve terminals of class IV sensory neurons , whose concentrated location in the ventral nerve cord was easier to visualize . O/E hiw caused a reduction in HA-Nmnat protein in sensory axon terminals ( Figure 7D and 7E ) . In contrast , co-expression of the dominant negative hiw-ΔRING mutation caused an increased level of HA-Nmnat in the sensory axon terminals ( Figure 7D and 7E ) . Further evidence that Hiw function is sufficient to down-regulate Nmnat comes from studies in S2R+ cells , which do not express Hiw endogenously . Co-expression of Hiw , but not of Hiw-ΔRING , led to down-regulation of HA-Nmnat protein ( Figures 7F and S4A ) . These findings suggest that Hiw plays a direct role in regulating the levels of Nmnat protein . Curiously , we were unable to obtain evidence that Hiw down-regulates Nmnat via the UPS . Inhibition of the proteasome by addition of MG132 , using several different concentrations and periods of time that affect known targets to the UPS ( Materials and Methods ) [43] , [44] , did not affect the down-regulation of Nmnat by Hiw in S2R+ cells ( Figure S4A ) . To inhibit the proteasome in vivo we co-expressed dominant-negative proteasome subunit mutations , DTS5 and DTS7 , which in previous studies had been shown to lead to allow targets of the UPS to accumulate [45]–[47] . This led to only minor ( 7% ) changes in the levels of HA-Nmnat in sensory neuron terminals ( Figure S4B ) . Surprisingly , inhibition of the proteasome had a much greater effect upon HA-Nmnat levels in a hiw null mutant than in a wild-type background ( Figure S4C ) . This observation does not favor a simple model that Hiw regulates Nmnat via the UPS . Instead , the data suggest that additional ubiquitin ligases may regulate Nmnat , and that the regulation of Nmnat may be more sensitive to the UPS when hiw is absent . While the above data indicate that ubiquitination is important for the regulation of Nmnat , the detailed mechanism by which Hiw regulates Nmnat remains to be determined . The mechanism may involve a direct interaction , since co-immunoprecipitation experiments indicate that Nmnat can robustly interact with the enzyme dead Hiw-ΔRING protein in S2R+ cells ( Figure 7G ) . A recent study using vertebrate cultured neurons suggested that the disappearance of Nmnat2 , which has a short half-life , from the distal stump of axons may serve as a trigger for the Wallerian degeneration process [21] . This leads to an attractive hypothesis that Hiw promotes the disappearance of Nmnat protein from the distal stump . Supporting this model , we observed that HA-Nmnat levels become significantly reduced in axons ( Figure S5A ) and synapses ( Figure 8 ) distal to the injury site . In contrast , HA-Nmnat levels increase in the proximal stump after injury ( Figure S5A ) , consistent with the model that a cytoplasmic form of this enzyme is transported in axons from the cell body [21] . Within 4 h after injury , the majority of HA-Nmnat in sensory axon terminals had disappeared ( Figure 8 ) . By comparison , a significant amount of green fluorescent protein ( GFP ) -Hiw remained at this time point ( Figure S5B ) . When hiw was mutant , the levels of HA-Nmnat in the distal stump did not decrease significantly below its starting level , even 24 h after injury ( Figure 8A and 8B ) . Expression of UBP2 had a similar effect upon HA-Nmnat in the distal stump after injury ( Figure 8A and 8B ) . These findings indicate that Hiw and the ubiquitination are required for the disappearance of Nmnat protein in the distal stump . Vertebrates utilize three distinct Nmnat enzymes , which localize to distinct subcellular locations . We tested whether Hiw was capable of influencing the levels of ectopically expressed mouse Nmnat1 , which localizes to nuclei , mouse Nmnat2 , which co-localizes with golgi and late endosome markers , or mouse Nmnat3 , which localizes to mitochondria [48]–[50] , by crossing UAS-mNmnat1::myc , UAS-mNmnat2::myc , and UAS-mNmnat3::myc transgenes [51] , [52] into the hiw mutant background . Intriguingly , mutations in hiw resulted in increased levels of mNmant2-myc protein within axons and synaptic terminals of class IV sensory neurons ( Figure 9 ) . This finding implies that mNmant2-myc protein can be transported to distal axons and synapses , and that mouse Nmnat2 shares a conserved protein feature with Drosophila Nmnat that allows it to be regulated by Hiw . In contrast , loss of hiw had no effect upon the levels of mNmnat1 or mNmnat3 . We interpret that the distinct subcellular localization of mNmnat2 may make this protein more susceptible to regulation by Hiw , and that that a conserved mechanism , involving Hiw homologues , may regulate Nmnat2 in vertebrate neurons .
Since the discovery of the dramatic inhibition of degeneration by the WldS mutation , many studies have focused upon the action of the NAD+ biosynthetic enzyme isoforms , Nmnat1 , Nmnat2 , and Nmnat3 , which in some circumstances can confer protection against axonal degeneration ( reviewed in [22] , [23] ) . Most of these studies involve gain-of-function overexpression experiments; it has been difficult to address the role of endogenous Nmnat enzymes in this process . Recent observations indicate that endogenous Nmnat activity plays an essential role in neuronal survival , and its depletion leads to neurodegeneration [21] , [25]–[27] . In addition , recent studies in vertebrate neurons suggest that the cytoplasmic isoform , Nmnat2 , has a short half-life in neurons [21] . An attractive model proposes that Nmnat2 is rapidly turned over in axons , and that its loss in the distal stump of an axon , which has become disconnected from its cell body , leads to the initiation of Wallerian degeneration [21] . Some aspects of this model are supported by our current in vivo characterization in Drosophila . We have identified Hiw , a highly conserved protein with features of an E3 ubiquitin ligase , as an important regulator of Wallerian degeneration . Hiw's role in this process involves the Nmnat protein , whose levels in axons and synapses are regulated post-transcriptionally by Hiw function . In hiw mutants , Wallerian degeneration is strongly inhibited , and the increased level of Nmnat protein in hiw mutants is both required and sufficient to inhibit degeneration . While the localization of endogenous Hiw in Drosophila is not known , homologues in mice and Caenorhabditis elegans have been detected in axons and at synapses [9] , [53] , so it is in the appropriate location to target the destruction of Nmnat in distal axons ( Figure 8C ) . However , it remains to be determined whether the down-regulation of Nmnat in the distal stump per se is the trigger for Wallerian degeneration . When HA-Nmnat was overexpressed , axons were protected from degeneration long after the rapid disappearance of detectable protein in the distal stump . It is possible that even very low levels of Nmnat protein are sufficient to protect from degeneration . It is also formally possible that the basal levels of Nmnat before injury , rather than the disappearance of Nmnat after injury , is an important determinant of degeneration . We also acknowledge that axonal degeneration likely involves additional steps downstream or in parallel to the regulation of Nmnat by Hiw . While overexpression of Hiw can induce a reduction in HA-Nmnat levels ( Figure 7 ) , we were unable to observe an enhanced rate of degeneration when Hiw was overexpressed . Studies almost a decade ago suggested a role for the UPS in the initiation of Wallerian degeneration [3] . It is tempting to propose that this role is manifested by the regulation of Nmnat by Hiw . However our observations caution against a simple interpretation that Hiw regulates Nmnat via the UPS , since Hiw can promote disappearance of Nmnat protein in cells in a manner unaffected by proteasome inhibitors ( Figure S4A ) . Moreover , in vivo , inhibition of the proteasome had only a minor effect upon Nmnat levels in a wild-type background ( Figure S4B and S4C ) . However in hiw mutants , Nmnat levels were very sensitive to the function of the proteasome ( Figure S4C ) . We interpret that additional ubiquitin ligases and the UPS may regulate Nmnat independently of Hiw . Regardless of the role of the proteasome , our observations suggest that ubiquitin plays an important role in Nmnat regulation . Overexpression of the yeast de-ubiquitinating protease UBP2 leads to increased levels of Nmnat protein and inhibition of Wallerian degeneration , in a manner that requires endogenous Nmnat ( Figure S3 ) . Future studies of the mechanism by which Hiw regulates Nmnat will therefore consider potential proteasome-independent roles of ubiquitination . Of note , in yeast UBP2 has been shown to preferentially disassemble polyubiquitin chains linked at Lys63 [54] , which have been found to perform non-proteolytic functions in DNA repair pathways [55] , kinase activation [56] , and receptor endocytosis [57] , [58] . We should also consider the possibility that Hiw regulates Nmnat indirectly: since we have thus far been unable to detect any ubiquitinated Nmnat species , it is possible that an intermediate , yet unknown , regulator of Nmnat may be the actual substrate of ubiquitination . Nevertheless , co-immunoprecipitation studies from S2R+ cells indicate that Hiw and Nmnat have the capacity to interact ( Figure 7G ) . The mechanism and cellular location of Nmnat's protective action is a highly debated subject . Observations in the literature point to both NAD+-dependent and NAD+-independent models for the strong protection by the WldS mutation [23] . The location of its protective action may be the mitochondria , since mitochondrially localized Nmnat can protect axons from degeneration [51] , [52] , [59] . However golgi/endosomal localized Nmnat2 can also be protective [21] , [27] , [60] , [61] . Our findings suggest that mutation of hiw leads to an increase in the pool of endogenous Nmnat that functionally impacts degeneration . While the site of endogenous Nmnat function during axonal degeneration remains to be identified , we found that the levels of ectopically expressed mouse Nmnat2 were specifically increased in the hiw mutant background . In contrast , the levels of nuclearly localized mNmnat1 or mitochondrially localized mNmnat3 were unaffected by Hiw . Since Nmnat2 has a short half-life in vertebrate neurons [21] , it is intriguing to propose that it is regulated by Hiw orthologs via an analogous mechanism . Since Nmnat2 does not appear to localize to mitochondria , does this favor a non-mitochondrial activity , such as function as a chaperone [62] , [63] , for the protective action ? It remains challenging to determine the exact location of protection , since the most apparent changes in Nmnat protein may not necessarily be the functionally relevant changes . A previously characterized target of Hiw regulation is the Wnd MAP kinase kinase kinase [16] , [17] . This axonal kinase is also capable of inhibiting Wallerian degeneration in motoneurons [20] . The protective action of Wnd requires a downstream signaling cascade and changes in gene expression mediated by the Fos transcription factor [20] . Loss of nmnat does not affect this signaling cascade ( unpublished data ) nor does it affect the protective action of Wnd ( Figure 5C and 5D ) . Conversely , loss of wnd does not affect the protection caused by overexpressing nmnat ( Figure 5A and 5B ) . Importantly , the regulation of Nmnat by Hiw does not appear to require Wnd function , and Wnd and Nmnat can protect axons independently of each other . These findings favor the model that Wnd and Nmnat are both regulated by Hiw and influence axonal degeneration through independent mechanisms . The Wnd kinase plays additional roles in neurons , which can be genetically separated from Nmnat function . These include regulation of synaptic growth: a dramatic synaptic overgrowth phenotype in hiw mutants is fully suppressed by mutation of wnd , but is not at all affected by knockdown of nmnat ( Figure 4E ) . Wnd/DLK also promotes axonal sprouting in response to axonal injury [30] , which is also unaffected by nmnat knockdown ( unpublished data ) . It is therefore clear that by regulating both Wnd and Nmnat , Hiw regulates multiple independent pathways in neurons . It is intriguing that the actions of both Wnd and Nmnat promote cellular responses to axonal injury . Axonal regeneration requires an initiation of a growth program within the axon , which depends upon the function of Wnd and its homologues [28]–[32] . Equally important is a clearance of the distal stump to make room for the regenerating axon [64]–[66] . Since both Wnd and Nmnat are transported in axons [21] , [30] , Figure 8C proposes a model in which Hiw function in the distal axon terminal could simultaneously promote destruction of Nmnat in the distal stump , and accumulation of Wnd in the proximal stump . The latter is observed after injury [30] , and is required to promote new axonal growth . The actual location in which Hiw regulates Nmnat remains to be determined . As an upstream regulator of both sprouting in the proximal stump and degeneration of the distal stump , Hiw may play a central role in regulating the ability of a neuron to regenerate its connection after injury . Importantly , the protective action of Nmnat may not be limited to Wallerian degeneration . The WldS mutation can protect neurons from degeneration in a wide variety of paradigms , from models of neurodegenerative disease , diabetic neuropathy , excitotoxity , and loss of myelination [22] , [23] . These findings suggest that action and regulation of Nmnat function is broadly important for neuronal function and maintenance . As a critical regulator of Nmnat , the Hiw ubiquitin ligase and its vertebrate homologues deserve further scrutiny for potential roles in human health and disease .
The following strains were used in this study: Canton-S ( wild-type ) , hiwND8 [8] , hiwΔN , UAS-hiw and UAS-hiw-ΔRING from [67] , OK6-Gal4 [68] , BG380-Gal4 [69] m12-Gal4 ( P ( GAL4 ) 5053A ) [70] , ppk-Gal4 [71] , Or47b-Gal4 [72] , UAS-UBP2 [41] , UAS-DTS5 , and UAS-DTS7 from [45] , wnd1 , wnd3 , and UAS-wnd from [16] . UAS-HA::nmnat [25] , UAS-WldS [2] , UAS-mNmnat1::myc , UAS-mNmnat2::myc , and UAS-mNmnat3::myc [51] , [52] , and UAS-Dcr2 were gifts from Grace Zhai , Liqun Luo , Marc Freeman , and Stephan Thor . UAS-wnd-RNAi ( Construct ID 13786 ) and UAS-nmnat-RNAi ( construct ID 32255 ) were acquired from the Vienna RNAi center [37] . The segmental nerves of third instar larvae were visualized through the cuticle under a standard dissection stereomicroscope . While larvae were anesthetized with CO2 gas , the segmental nerves were pinched tightly through the cuticle for 5 s with Dumostar number 5 forceps . After successful injury , the posterior half of the larva was paralyzed . Larvae were then transferred to a grape plate and kept alive for varying periods of time at 25°C . Also see [30] . Larvae were dissected in PBS and fixed in 4% paraformaldehyde or formaldehyde in PBS for 25 min for the following antibodies used: ms anti-Futsch ( 1∶100 ) , guinea pig ( gp ) anti-NMNAT [25] , ( gift from Hugo Bellen and Grace Zhai , 1∶1 , 000 ) , rat anti-HA ( Roche , 1∶100 ) , rat anti-elav ( 1∶50 ) , or fixed in Bouin's fixative for 15 min for the following antibodies: ms anti-Brp ( 1∶200 ) , Rb anti-GluRIII ( 1∶1 , 000 [73] ) , Rb anti-DVLGUT ( 1∶10 , 000 , [74] ) . Rat anti-elav ( 7E8A10 ) and ms anti-Brp ( NC82 ) were obtained from Developmental Studies Hybridoma Bank , University of Iowa . The conjugated secondary antibodies are used and diluted as follows: Cy3-Gt anti-HRP and Cy5-Gt anti-HRP ( from Jackson labs ) at 1∶200 , A488-Rb anti-GFP ( from Molecular Probes ) at 1∶1 , 000 . For secondary antibodies Cy3 and Alexa-488 conjugated Goat anti-rb/mouse/rat/gp ( from Invitrogen ) were used at 1∶1 , 000 . All antibodies were diluted in PBS-0 . 3%Triton with 5% normal goat serum . Confocal images were collected at room temperature on an Improvision spinning disk confocal system , consisting of a Yokagawa Nipkow CSU10 scanner , and a Hamamatsu C9100-50 EMCCD camera , mounted on a Zeiss Axio Observer with 25× ( 0 . 8 NA ) multi and 40× ( 1 . 3NA ) , 63× ( 1 . 5NA ) , and 100× ( 1 . 46 NA ) oil objectives . Similar settings were used to collect all compared genotypes and conditions . Volocity software ( Perkin Elmer ) was used for all measurements of average and total intensities . For measurement of Nmnat intensity in the neuropil , the neuropil area was selected based on co-staining for the synaptic marker Brp . Objects meeting intensity criteria of >0 . 8 standard deviations above the mean were selected within a 140-µm long region of the ventral nerve cord and then summed for total intensity . The average intensity of the HA-Nmnat staining in muscle 4 NMJs was measured within the synaptic area defined by HRP staining after subtraction of background intensity for each image . The average intensity of the HA-Nmnat staining in motoneuron axons and sensory nerve terminus was measured with a similar protocol . Likewise for neuronal nuclei , the average intensity for Nmnat staining was measured in neuronal nuclei defined by staining for the neuronal marker Elav . Numbers are shown normalized to the average intensity of the control for each figure . To quantify axonal degeneration , we scored ( while blind to genotype ) the fragmentation of m12-Gal4 , UAS-mCD8-GFP labeled axons within segmental nerves according to one of five categories between 0 and 100% ( with 100% meaning completely degenerated ) as described in [20] . All measurements indicate the average from >100 axons . To quantify the degeneration of the NMJ , NMJs were stained for the MAP1B homologue Futsch and axonal membrane marker HRP , and were scored into one of three categories: ( 1 ) complete degeneration , defined by a complete loss of Futsch staining from the NMJ and fragmentation of the axonal membrane , ( 2 ) partial degeneration , defined by a partial loss of Futsch staining from the NMJ and partial membrane fragmentation , and ( 3 ) no degeneration , in which there was no fragmentation of the membrane or Futsch , similar to uninjured control animals . All quantifications shown represent the average scores from multiple NMJs from >six animals quantified in duplicate by two independent observers who were blind to the genotype . Degeneration of ORN axons was quantified following the previously defined method [2] , [35] by calculating the percentage of brains for each genotype in which contralateral axon projections could still be detected . For all the statistical analysis , Student's t test was applied . Intracellular recordings were made from muscle 6 in segments A3 and A4 of third-instar male larvae . The larvae were visualized with oblique illumination on an Olympus BX51W1 fixed stage upright microscope with a 10× water immersion objective . Sharp electrodes ( 15–25 MΩ ) , made of borosilicate glass ( outer diameter 1 . 2 mm ) were filled with 3 M KCl . The signal was amplified with a Geneclamp 500B ( Molecular Devices ) , digitized with a Digidata 1322A interface ( Molecular Devices ) , and stored on a PC with pClamp 10 . 2 ( Molecular Devices ) . Recordings were performed in HL3 Stewart saline [75] that contained ( in mM ) 70 NaCl , 5 KCl , 20 MgCl2 , 10 HCO3 , 5 trehalose , 115 sucrose , 5 HEPES , 1 CaCl2 , , the pH was adjusted to 7 . 2 . For all genotypes the resting membrane potentials and input resistances were similar , with average resting potentials of −73±4 and input resistances of 6±0 . 2 MΩ . To elicit evoked EJPs , the nerve was drawn into a tight-fitting suction electrode and stimulated with brief ( 1 ms ) depolarizing pulses controlled with Digidata interface . The stimulus amplitude was set to 125% of the amplitude necessary to activate the higher threshold of the two excitatory axons that innervate the muscle . For injured wild-type larvae ( in which nerve stimulation did not produce evoked synaptic responses ) the stimulus amplitude was set to double the amplitude used for un-injured larvae . However evoked responses were not observed , even at the largest stimulus amplitude that the equipment could produce . For analysis of evoked responses , 100 events per cell recorded at 0 . 2 Hz were measured using the cursor feature in Clampfit 10 . 2 ( Molecular Devices ) and then averaged . For analysis of spontaneous miniature EJPs , at least 50 consecutive events were measured per cell using MiniAnal ( Synaptosoft ) . mEJP frequency was calculated from the first 30 s of recording time . S2R+ cells were cultured in Schneider's medium ( Gibco ) which contains 10% ( v/v ) FBS ( Gibco ) and 1% penicillin-streptomycin ( Invitrogen ) . For plasmid transfection , cells were transfected using FuGENE 6 ( Promega ) following the manufacturer's instructions . Copper sulfate solution ( 0 . 5 mM ) was added 6 h after transfection to induce plasmid expression . Cell lysates were collected after 24 h . Plasmids used for transfection were pMT-Gal4 [76] , pUAST-eGFP [77] , pUAST-GFP-Hiw [67] , pUAST-HiwΔRING [67] , and pUAST-HA-Nmnat [25] . To inhibit the UPS , cells were treated with MG132 ( InSolution , Calbiochem ) or DMSO as control using several different conditions: 25 µM for 6 h , 5 µM for 20 h , and 5 µm for 36 h . All of these conditions led to an increase in the levels of polyubiquitinated proteins , detected by Western blots probed with FK1 antibodies . The following antibodies were used for Western blotting: rb anti-Hiw ( ref , 1∶2 , 000 ) , rat anti-HA ( Flourochem , 1∶2 , 500 ) , ms anti-β-tubulin ( 1E7 ) and ms anti-β-catenin ( armadillo , N27A1 ) from Developmental Studies Hybridoma bank ( University of Iowa ) , ms anti-polyubiquitin , ( FK1 , Enzo Life Sciences , 1∶1 , 000 ) , and ms anti-ubiquitin ( P4D1 , Cell Signaling , 1∶1 , 000 ) . Westerns were probed with IRDye 800CW and 680RD conjugated secondary antibodies ( LiCor biosciences , 1∶10 , 000 ) and imaged for quantitative analysis via a LiCor Odyssey imaging system . S2R+ cells were transfected with either pUAST-HiwΔRING or pUAST-HiwΔRING and pUAST-HA-Nmnat . Cells from 6-cm dishes were harvested in 500-µl ice-cold lysis buffer ( 20 mM HEPES [pH 7 . 5] ) , 200 mM KCl , 0 . 05% Triton X-100 , 2 . 5 mM EDTA , 5 mM DTT , 5% glycerol and Complete proteinase inhibitor [Promega] ) . 1 . 5 mg Protein G conjugated Dynabeads ( Invitrogen ) were used to capture 10 µl mouse monoclonal anti-HA antibody ( HA-7 ascites fluid , Sigma ) at room temperature for 30 min , and were then incubated with cell lysates for 30 min at room temperature . The immunoprecipitates were then washed three times with ice-cold lysis buffer and subjected to Western blotting analysis .
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Axons degenerate after injury and during neurodegenerative diseases , but we are still searching for the cellular mechanism responsible for this degeneration . Here , using a nerve crush injury assay in the fruit fly Drosophila , we have identified a role for a conserved molecule named Highwire ( Hiw ) in the initiation of axonal degeneration . Hiw is an E3 ubiquitin ligase thought to regulate the levels of specific downstream proteins by targeting their destruction . We show that Hiw promotes axonal degeneration by regulating two independent downstream targets: the Wallenda ( Wnd ) kinase , and the NAD+ biosynthetic enzyme nicotinamide mononucleotide adenyltransferase ( Nmnat ) . Interestingly , Nmnat has previously been implicated in a protective role in neurons . Our findings indicate that Nmnat protein is down-regulated in axons by Hiw and that this regulation plays a critical role in the degeneration of axons and synapses . The other target , the Wnd kinase , was previously known for its role in promoting new axonal growth after injury . We propose that Hiw coordinates multiple responses to regenerate damaged neuronal circuits after injury: degeneration of the distal axon via Nmnat , and new growth of the proximal axon via Wnd .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"molecular",
"neuroscience",
"developmental",
"neuroscience",
"cellular",
"neuroscience",
"synaptic",
"plasticity",
"neuromuscular",
"junction",
"neurobiology",
"of",
"disease",
"and",
"regeneration",
"neuronal",
"morphology",
"axon",
"guidance",
"signaling",
"pathways",
"biology",
"neuroscience",
"neurophysiology"
] |
2012
|
The Highwire Ubiquitin Ligase Promotes Axonal Degeneration by Tuning Levels of Nmnat Protein
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In almost every field in genomics , large-scale biomedical datasets are used to report associations . Extracting associations that recur across multiple studies while controlling the false discovery rate is a fundamental challenge . Here , we propose a new method to allow joint analysis of multiple studies . Given a set of p-values obtained from each study , the goal is to identify associations that recur in at least k > 1 studies while controlling the false discovery rate . We propose several new algorithms that differ in how the study dependencies are modeled , and compare them and extant methods under various simulated scenarios . The top algorithm , SCREEN ( Scalable Cluster-based REplicability ENhancement ) , is our new algorithm that works in three stages: ( 1 ) clustering an estimated correlation network of the studies , ( 2 ) learning replicability ( e . g . , of genes ) within clusters , and ( 3 ) merging the results across the clusters . When we applied SCREEN to two real datasets it greatly outperformed the results obtained via standard meta-analysis . First , on a collection of 29 case-control gene expression cancer studies , we detected a large set of consistently up-regulated genes related to proliferation and cell cycle regulation . These genes are both consistently up-regulated across many cancer studies , and are well connected in known gene networks . Second , on a recent pan-cancer study that examined the expression profiles of patients with and without mutations in the HLA complex , we detected a large active module of up-regulated genes that are both related to immune responses and are well connected in known gene networks . This module covers thrice more genes as compared to the original study at a similar false discovery rate , demonstrating the high power of SCREEN . An implementation of SCREEN is available in the supplement .
Confidence in reported findings is a prerequisite for advancing any scientific field . Such confidence is achieved by showing replication of discoveries in new studies [1] . In recent years studies have shown low reproducibility of results in several domains , including economics [2] , psychology [3] , medicine [4] , and biology [5–7] . A new methodology called replicability analysis was recently suggested as a way to statistically pinpoint replicated discoveries across studies while controlling for the false discovery rate ( FDR ) [8] . This type of analysis is essential when trying to detect new hypotheses by integration of existing data from multiple high-throughput experiments . The practical importance of replicability analysis is twofold . First , it quantifies the reliability of reported results . Second , collated information from multiple studies can identify scientific results that are beyond the reach of each single study . Indeed , in Genome Wide Association Studies ( GWAS ) replicability analysis allowed detection of new results that were not identified in meta-analysis , demonstrating that the two approaches are complementary [9] . Meta-analyses are widely applied and have been extensively studied in statistics [10] and in computational biology [11 , 12] . However , in recent years the changes in the scale and scope of public high-throughput biomedical data have posed new methodological challenges . The first , and more obvious , is accounting for inflation in the number of false discoveries due to the multiplicity of outcomes , as hundreds of thousands and even millions of hypotheses are tested ( see Zeggini et al . [13] for example ) . The second challenge is directly assessing consistency of results , which is not addressed by the classic null hypothesis of meta-analysis that the effect size is 0 in all the studies . Third , there is a need to distinguish between true effects that are specific to a single study and true effects that represent general discoveries and thus are replicable . For example , Kraft et al . [14] suggested that the effect of common genetic variants on the phenotype may correlate with population biases in a specific GWAS . While these are real discoveries in the sense that similar estimated effects are expected to be observed if the experiment could be replicated on the same cohort , their scientific importance is limited because they are specific to that cohort . For this reason , the authors argue that it is important to identify the association in additional studies conducted using a similar , but not identical , study base . In recent years several frequentist approaches were suggested for the problem . Benjamini and Heller [15] introduced an inferential framework for replicability that is based on tests of partial conjunction null hypotheses . For meta-analysis of n studies of the same m outcomes and u = 1…n , the partial conjunction Hu/n ( g ) is that outcome g has a non-null effect in less than u studies . Thus H1/n ( g ) is the standard meta-analysis null hypothesis that outcome g has a null effect in all n studies . The authors introduced p-values for testing Hu/n ( g ) for each outcome . Benjamini , Heller and Yekutieli [8] applied the Benjamini-Hochberg FDR procedure [16] ( BH ) to the partial conjunction hypotheses p-values , and suggested setting u = 2 in order to assess replicability . Heller et al . [17] developed an approach for checking if a follow-up study corroborates the results reported in the original study . Song and Tseng [18] proposed a method to evaluate the proportion of non-null effects of a gene . However , they used the standard meta-analysis null hypothesis and their method cannot handle composite hypotheses , which are partial conjunctions Hu/n with u > 1 . Bayesian methods handle these shortcomings and offer a powerful framework for replicability analysis . For analyzing results from a single study , Efron introduced an empirical Bayes framework called the two-groups model [19] . It allows explicit analysis of the distribution of the statistic ( e . g . , p-values ) of the underlying null and non-null groups . This clustering-based structure is then used to quantify the FDR of a rejection rule , and to compute a single point statistic , which is referred to as local Bayes FDR , or simply fdr . Heller and Yekutieli [9] introduced a method called repfdr , which extends the two-groups model for testing the partial conjunction hypotheses to the multi-study case . Formally , the problem is as follows: given an n × m matrix Z , where Zi , j is the p-value ( or z-score ) of object ( e . g . , gene ) i in study j , our goal is to identify the objects that are k-replicable ( i . e . , significant in k or more studies ) while controlling the fdr . Repfdr estimates the posterior probabilities of the various configurations of outcome effect status ( null or non-null ) across studies , and computes the fdr for each partial conjunction null by summing the posterior probabilities for the relevant configurations . The authors showed that their approach controls the FDR and offers more power than the frequentist methods . However , repfdr is not scalable and can only handle a few datasets . In addition , it was particularly designed for GWAS datasets in which the number of tested objects ( e . g . , SNPs ) is very large ( i . e . , > 100k ) . In this study , we propose ways to overcome the limitations of repfdr . Our focus is on allowing efficient computation when m is large and n is limited , such as in gene expression datasets ( i . e . , n ∼ 20k ) . To reach this goal we make three simplifying assumptions: ( 1 ) we ignore the effect size , ( 2 ) we ignore the direction of the statistic , and ( 3 ) we assume that the studies originate from independent clusters . In addition , to handle larger values of m we compute an upper bound for the fdr , cutting the running time substantially . Our main algorithm is called SCREEN ( Scalable Cluster-based REplicability ENhancement ) . It first detects the study clusters , then uses Expectation-Maximization ( EM ) to model each cluster , and finally merges the clusters using dynamic programming . Other algorithms that we propose here include two variants of SCREEN that differ in the way the studies are clustered: SCREEN–ind assumes independence and treats each study as a single cluster , and repfdr-UB puts all studies in one cluster . We compared SCREEN to other algorithms using various simulated scenarios and showed that only SCREEN had consistently low empirical false discovery proportions , and very high detection power . We applied SCREEN to two cancer datasets , where each is a collection of case-control gene expression experiments . In both cases SCREEN greatly improved the results obtained by standard meta-analysis , and provided new biological insights . The first dataset is a collection of 29 case-control gene expression cancer studies from different tissues . Here , SCREEN detected a large set of genes that are consistently up-regulated , highly enriched for cell proliferation and cell cycle regulation functions , and are well connected in known gene networks , indicating their functional coherence . The second dataset is a recent pan-cancer study that examined the expression profiles of patients with and without mutations in the HLA complex across 11 cancer types [20] . SCREEN detected a large set of up-regulated genes that are related to immune responses . Importantly , SCREEN reported many more immune response genes than the original study thanks to our ability to quantify the fdr , and allowed detection of prominent genes and pathways that were not reported previously .
We start with a brief introduction to the single-study model . For a full description and background see [19] . Given a large set of N hypotheses tested in a large-scale study , the two-groups model provides a simple Bayesian framework for multiple testing: each of the N cases ( e . g . , genes in a gene expression study ) are either null or non-null with prior probability π0 and π1 = 1 − π0 , and with z-scores ( or p-values ) having density either f0 ( z ) or f1 ( z ) . When the assumptions of the statistical test are valid , we know that the f0 distribution is a standard normal ( or a uniform distribution for p-values ) , and we call it the theoretical null . The mixture density and probability distributions are: f ( z ) = π 0 f 0 ( z ) + π 1 f 1 ( z ) F ( z ) = π 0 F 0 ( z ) + π 1 F 1 ( z ) For a rejection area Ƶ y = ( - ∞ , y ) , using Bayes rule we get: F d r ( Ƶ y ) ≡ P r { n u l l | z ∈ Ƶ y } = π 0 F 0 ( y ) / F ( y ) We call Fdr the ( Bayes ) false discovery rate for Ƶ: this is the probability we would make a false discovery if we report Ƶ as non-null . If Ƶ is a single point z0 we define the local ( Bayes ) false discovery rate as: f d r ( z 0 ) ≡ P r { n u l l | z = z 0 } = π 0 f 0 ( z 0 ) / f ( z 0 ) Previous work have shown that: ( 1 ) the Bayes Fdr is tightly related to false discovery control in the frequentist sense , and ( 2 ) using a threshold on the local fdr for defining discoveries is equivalent to the optimal Bayes rule in terms of classification between nulls and non-nulls . Moreover , a threshold of 0 . 2 on the local fdr was suggested [19 , 21] . Note that in our Bayesian setting computing the local fdr of a gene is an estimation problem . Within the context of our study we incorporated and tested two established methods for two-groups estimation studies: locfdr [22] and estimation based on mixture of Gaussians , which we call normix , see Materials and methods for details . Consider an extension of the two group model to analysis of n genes over m > 1 studies . The data for gene i are a vector of m statistics Zi , ⋅ = ( Zi , 1 , ⋯ , Zi , m ) that are all either z-scores or p-values . For simplicity , from now on we assume that these data are z-scores . The unknown parameter for gene i = 1 , ⋯ , n is a binary configuration vector Hi , ⋅ = ( Hi , 1 , ⋯ , Hi , m ) , with Hi , j ∈ {0 , 1} . If Hi , j = 0 then gene i is a null realization in study j , and it is a non–null realization otherwise . We assume that in each study j the parameters of the two-groups model θ j : ( π 0 j , f 0 j , f 1 j , f j ) are fixed and focus on replicability analysis . Generally , unless mentioned otherwise , we assume that the genes are independent . However , note that estimation of θj can account for gene dependence within study j [23 , 24] . Finally , we also assume that the z-scores of a gene are independent given its configuration . That is , P ( Z i , · | H i , · ) = ∏ j = 1 m P ( Z i , j | H i , j ) = ∏ j = 1 m ( f 0 j ( Z i , j ) ) ( 1 − H i , j ) ( f 1 j ( Z i , j ) ) H i , j Next , we use h ∈ {0 , 1}m to denote an arbitrary configuration vector , and π ( h ) to denote a probability assigned to the parameter space . We assume that the researcher has a set of configurations H 1 ⊆ { 0 , 1 } m that represents the desired rejected genes . Here we will assume that H 1 corresponds to genes that are non-null in at least k studies: H 1 = { h : | h | ≥ k } , where | h | = ∑ j = 1 m h j . As a note , selection of k depends on the research question at hand . For example , Heller and Yekutieli used k = 2 to detect minimal replicability of SNPs in a GWAS [9] . Low k values can also be reasonable if the m studies represent different biological questions that are related , such as differential expression experiments from different cancer subtypes . On the other hand , if the m studies represent tightly related experiments such as biological replicates then larger k ( e . g . , m/2 ) seems more reasonable . The local false discovery rate ( fdr ) of a gene i can be formulated as: f d r ( Z i , · ) = P r ( H 1 ¯ | Z i , · ) = ∑ h : h ∉ H 1 P ( h | Z i , · ) = ∑ h : h ∉ H 1 P ( Z i , · | h ) P ( h ) P ( Z i , · ) For a given k and H 1 = { h : | h | ≥ k } we get: f d r k ( Z i , · ) = ∑ h : | h | < k P ( Z i , · | h ) P ( h ) P ( Z i , · ) We first address the case where studies are independent . Lemma . If the studies are independent ( in the parameter space ) then: f d r k ( Z i , · ) = ∑ h : | h | < k ∏ j = 1 m P ( Z i , j | h j ) P ( h j ) f j ( Z i , j ) Proof . First , note that under the independence assumption P ( h ) = ∏ j = 1 m P ( h j ) = ∏ j = 1 m π 0 j ( 1 - h j ) ( 1 - π 0 j ) h j . Second , as the z-scores are independent given the configuration vector h we get that: P ( Z i , · ) = ∑ h P ( Z i , · | h ) P ( h ) = ∑ h ∏ j = 1 m P ( Z i , j | h j ) π 0 j ( 1 - h j ) ( 1 - π 0 j ) h j = ∏ j = 1 m ( P ( Z i , j | h j = 0 ) π 0 j + P ( Z i , j | h j = 1 ) ( 1 - π 0 j ) ) Proposition: If the studies are independent then fdrk can be computed in O ( mnk ) . Proof . By the lemma , the fdr of a gene is based on the product of the two-group model densities in each study . Therefore: f d r k i n d e p ( Z i , · ) = ∑ h : | h | < k ∏ j = 1 m ( π 0 j f 0 j ( Z i , j ) ) 1 - h j ( ( 1 - π 0 j ) f 1 j ( Z i , j ) ) h j f j ( Z i , j ) We use dynamic programming to calculate fdrk ( zi ) as follows . Define: U [ i , j , k * ] = ∑ h : | h | = ( k * - 1 ) ∏ j = 1 m ( π 0 j f 0 j ( Z i , j ) ) 1 - h j ( ( 1 - π 0 j ) f 1 j ( Z i , j ) ) h j f j ( Z i , j ) These values can be calculated ( for each gene i ) by updating a table of m × ( k + 1 ) values . The base cases are: U [ i , j , 1 ] = ∏ j = 1 m π 0 j f 0 j ( Z i , j ) f j ( Z i , j ) The recursive formulas are: U [ i , j , k * ] = π 0 j f 0 j ( Z i , j ) f j ( Z i , j ) U [ i , j - 1 , k * ] + ( 1 - π 0 ) j f 1 j ( Z i , j ) f j ( Z i , j ) U [ i , j - 1 , k * - 1 ] Finally , to obtain the fdr of a gene we sum over the values in the last column: f d r k i n d e p ( Z i , · ) = ∑ k * = 1 k - 1 U [ i , m , k * ] The running time for analyzing each gene is O ( mk ) and the total running time is O ( nmk ) . The empirical Bayes method of [9] estimates the prior distribution π ( h ) directly from the data . This approach has two drawbacks . First , the EM algorithm explicitly keeps a value for each possible configuration , which makes the algorithm intractable when m is large . Second , the estimation for rare configurations might be inaccurate , unless n >>2m . As an alternative , we develop an algorithm that keeps in memory only a small set of high probability configurations . We then use these estimates to obtain an upper bound for the fdr of a gene . We first describe the EM in the full configuration space , and then modify it for the constrained case . That is , the EM is guaranteed to improve the solution and converge . The unrestricted EM formulation is based on repfdr [9 , 25] , as follows: The E-step: P ( H i , · = h | Z i , · , π ( t ) ( h ) ) = f ( Z i , · | h ) π ( t ) ( h ) ∑ h ′ f ( Z i , · | h ′ ) π ( t ) ( h ′ ) The M-step: π ( t + 1 ) ( h ) = 1 n ∑ i P ( H i , · = h | Z i , · , π ( t ) ( h ) ) = 1 n ∑ i f ( Z i , · | h ) π ( t ) ( h ) ∑ h ′ f ( Z i , · | h ′ ) π ( t ) ( h ′ ) This process guarantees convergence to a local optimum . Our goal is to limit the search space . Lemma . The EM algorithm above can be used to find a local optimum estimator under the constraint ∀ h ∉ H ′ π ( h ) = 0 , for any non-empty configuration set H ′ . Proof . Note that during the EM iterations , if at some time point t π ( t ) ( h ) = 0 then ∀t* > t π ( t* ) ( h ) = 0 . Therefore , setting the starting point of the EM such that ∀ h ∉ H ′ π ( 0 ) ( h ) = 0 satisfies the constraint and ensures convergence . In this section we present a restricted version of the EM above , which we call repfdr-UB . To describe it , we need additional notation . Given a configuration vector h ∈ {0 , 1}m , let h[l] be the vector containing the first l entries of h . Given a real valued vector v , let v ( i ) denote the i’th smallest element of v . Our algorithm is based on the simple observation that if π ( h[l] ) ≤ ϵ then any extension of h[l] cannot exceed ϵ . That is , π ( h[l + 1] ) ≤ ϵ regardless of the new value in study l + 1 . Our algorithm works as follows . The user specifies a limit to the number configurations kept in the memory—nH . For simplicity we assume that nH is a power of 2 . We first run the unrestricted EM algorithm on the first log2 ( nH ) − 1 studies . Each subsequent iteration adds a new study . In each iteration l we keep four parameters: ( 1 ) H ^ l—the set of the top nH probability configurations , ( 2 ) π ^ l—the vector of their assigned probabilities , ( 3 ) ξ ^ l—an estimation of ∑ h [ l ] ∈ H ^ l π l ( h [ l ] ) , and ( 4 ) ϵ ^ l—an estimation of the maximal probability among the excluded configurations . Initially , l = log2 ( nH ) − 1 and H ^ l contains all possible configurations of the first l studies . In addition ξ ^ l = 1 , and ϵ ^ l = 0 . In iteration l + 1 we run the restricted EM algorithm on all possible extensions of H ^ l . That is , the input configuration set for the EM is a result of adding either 1 or 0 at the l + 1 position of each configuration in H ^ l . The EM run produces initial estimations for our parameters , on which the following ordered updates are applied: π ^ l + 1 = ξ ^ l π ^ l + 1 ( 1 ) H ^ l + 1 = { h [ l + 1 ] ; π ^ l + 1 ( h [ l + 1 ] ) ≥ π ^ ( n H / 2 ) l + 1 } ( 2 ) ξ ^ l + 1 = ∑ h [ l + 1 ] ∈ H ^ l + 1 π l + 1 ( h [ l + 1 ] ) ( 3 ) ϵ ^ l + 1 = max ( ϵ ^ l , max h [ l + 1 ] ∉ H ^ l + 1 ( π ^ l + 1 ( h [ l + 1 ] ) ) ) ( 4 ) Note that in step Eq ( 2 ) above we keep the top nH/2 configurations in H ^ l + 1 . This set is then used as input to the EM run in the next iteration . We repeat the process above until l = m . The output of the algorithm is H ^ m , π ^ m , ξ ^ m , ϵ ^ m . In this section we apply the ideas from the previous sections to obtain an algorithm for calculating the fdr under the assumption that the studies originate from independent clusters . We call this algorithm SCREEN ( Scalable Cluster-based REplicability ENhancement , see S1 Text for an overview of the method ) . Briefly , our algorithm has three stages . First , we use the EM-process on each study pair to create a network of study correlations . We then cluster the network to obtain a set of study clusters that are likely to be independent , see Materials and methods for the full description of this step . Second , we run the EM on each cluster separately . Finally , we merge the results from the different clusters using dynamic programming . Note that this algorithm is a heuristic as it uses EM within each cluster . We analyzed two real datasets . The first , which we call Cancer DEG , is a collection of gene expression studies that compared cancer to non-cancer tissues . The second , called HLA , is from [20] , where Shukla et al . tested differential expression between cancer samples with and without somatic mutations in the HLA complex across 11 TCGA cancer subtypes .
We presented here several novel algorithms for detecting replicated associations using an empirical Bayes approach . Our main algorithm , called SCREEN , outperformed other approaches in many scenarios and had consistently low false discovery proportion and high true discovery rates in all simulations . SCREEN works in three stages . First , it clusters the studies based on their pairwise correlations , which are learned via EM . Second , it performs replicability analysis within each cluster using our restricted EM approach . This method goes beyond previous studies by restricting the possible number of study configurations that are kept in memory . As a result , the method can analyze large study clusters , by computing an upper bound for the fdr instead of an exact estimation . Finally , the results of the replicability analyses of the clusters are merged using dynamic programming . For a given k , the output of SCREEN is the fdrk value for each gene , which can be used to detect genes that are non-null in at least k studies . We have shown that SCREEN performs well on various simulated scenarios , as well as on real datasets . Specifically , we analyzed two collections of cancer-related gene expression studies . In both cases the discovered gene sets highlighted active gene modules with pertinent functions . Such modules are revealed by the projection of the discovered genes on an interaction network and focusing on well-connected subnetworks . Notably , standard meta-analysis does not reveal many of the genes and generates more fragmented and less coherent subnetworks . For example , in the cancer DEG datasets , some of these genes are central in the network and are known master regulators of cell cycle ( e . g . , CDK1 ) . In summary , we demonstrated replicability analysis as a standard tool for analyzing a large collection of studies , and provided novel algorithms that are accurate and scalable . While the current version of SCREEN does not model the direction of the statistic directly ( i . e . , up- or down-regulation ) , we addressed this point empirically in our examples and showed that most genes were consistent in their direction . For the sake of functional analysis we required a gene to have the same direction in at least 75% of the studies . This threshold reflects a reasonable selection between the number of reported genes and the consistency requirement ( see S8 Fig ) . Of course , users can change this threshold to require a higher ( or lower ) consistency in direction in specific applications . Also , note that detection of “mixed sign” genes is an important feature of our analysis: the causality of such genes is questioned as they likely represent downstream effects . The strategy of SCREEN can be extended to a more complex definition of replicability across study clusters . For example , a researcher may seek genes that are replicable across one or more study clusters , where a gene is replicable in a cluster only if it is non-null in at least some predefined percentage of the studies in that cluster . See S1 Text for a discussion on this topic . A variety of methods for integration of different studies have been developed in GWAS but their goals are different from SCREEN’s . These methods typically assume a standard meta-analysis null hypothesis that the effect size is zero across all studies [31] . As such , they do not address the question of replication directly even if a clustering of the studies is considered [32] . Moreover , some of the Bayesian approaches that were developed for GWAS utilize a subjective prior and do not estimate it form the data [33] . Finally , as we have shown , SCREEN outperforms repfdr , which was originally developed for GWAS data [9] . Our study has some limitations that can be addressed in future research . First , we assumed that genes are independent . This assumption is usually made by state of the art methods , but is often incorrect . In our case , it was used to obtain tractable algorithms ( both the dynamic programming and the EM ) . Second , while our algorithms report fdrk values of genes , we currently do not estimate their variance . Third , selection of k was done manually on real datasets based on the specific biological question and the number of reported genes ( e . g . , Fig 3A ) . Fourth , our restricted EM approach to analyze study clusters is a heuristic that only guarantees convergence into a local optimum . Thus , while our algorithm has a deterministic starting point for the EM , setting starting points at random may change the obtained upper bounds for the fdr . Indeed , when we tried using random starting points , the set of reported genes in the cancer DEG dataset changed ( by up to 50 genes , but still leaving over 80 genes from the original results ) , and no change was observed in the HLA dataset . Another important aspect of the heuristic is the number of allowed gene configurations . As an example , SCREEN with nH = 10000 configurations on the cancer DEG dataset finds more than 100 additional genes for k = 20 , whereas the fdr of the genes reported in our original analysis ( Fig 4 ) is kept low , illustrating that the results are consistent ( see S9 Fig ) . Fifth , while our simple Exp-count approach to estimate the expected number of non-null realizations of a gene performed reasonably well in some simulated scenarios , it is only partially justified theoretically ( see S1 Text ) . Finally , our methods rely on fixed estimates of the two-groups model of each study . Future methods could go a step further and estimate all parameters ( i . e . , both the study parameters and the gene configuration probabilities ) in a single flow .
We tried two implementations of two-groups estimation algorithms . The first locfdr [22] , provides two options to learn the empirical null: maximum likelihood and central matching . By default , we used the maximum likelihood estimator . However , in practice this algorithm might converge to a solution in which π ^ 0 > 1 . Whenever this occured , we tried the central matching approach instead . If the new estimator also had π ^ 0 > 1 we used the theoretical null . The second approach was based on two previous methods: Znormix [34] and fdrtool [35] . Znormix uses EM to learn a mixture of Gaussians , whereas fdrtool assumes that the null distribution is a half normal distribution . Here , we applied an EM approach to the absolute values of the z-scores . We extended these methods by learning a mixture of a half normal with σ ≥ 1 for the null distribution , and a normal distribution with μ > 0 for the non-nulls . We call this approach normix . In practice , we discovered that our EM algorithm is sensitive to high values in the estimation of f1 . In addition , the methods above do not exploit additional information that could be obtained from the two-groups model: an estimation for the power of a study [19] . That is , this is a measure of how separated the two groups are . In our analyses , we took a very stringent approach: in each study we multiply f1 ( z ) by the estimated power of that study . The effect is a shrinkage in the f1 ( z ) values that is proportional to the estimated quality of the study . SCREEN relies on a known partition of the studies into clusters . In this section we use an empirical Bayes approach to obtain the clusters . Our analysis has two main parts: learning a network , and clustering . First , we create a correlation network among the studies . For each study i , let ai = P ( hi = 1 ) be the marginal non-null probability in that study . For studies i , j let ai , j = P ( hi = 1 ∧ hj = 1 ) be the shared non-null probability of the two studies . We estimate these parameters as follows: ai and aj are taken from the two-groups model of each study , and ai , j is estimated by running our EM approach on the data of these two studies . The correlation of the studies is then estimated by: r i , j = a i , j - a i a j a i ( 1 - a i ) a j ( 1 - a j ) We obtain a robust estimation of ri , j by taking the mean of 100 bootstrap runs of the procedure above . That is , in each run we reestimate ai , j by running the EM on a bootstrap sample of the genes ( n/2 genes out of n , sampled with replacement ) . Next , we cluster the network using the infomap algorithm [36] . Here , communities are detected using random walks in the underlying graph . As the input for this algorithm is an unweighted network , we used a threshold of 0 . 1 for the absolute correlation of study pairs to determine edge presence . This threshold is relatively low for general clustering tasks as it does not guarantee high homogeneity within clusters . However , it guarantees that the clusters discovered by SCREEN will be well-separated . In practice , our clustering approach found the correct clustering of studies in all simulations performed . In order to evaluate our fdr approaches we compared them to several extant methods for multi-study analysis . Here we outline them briefly . Network analysis and visualization was done in Cytoscape [39] . The GeneMANIA Cytoscape app [40 , 41] was used to create the gene networks of the selected gene sets . GO enrichment analysis was performed using Expander [42] . The datasets and an implementation of SCREEN are available in S1 Data .
|
When analyzing results from multiple studies , extracting replicated associations is the first step towards making new discoveries . The standard approach for this task is to use meta-analysis methods , which usually make an underlying null hypothesis that a gene has no effect in all studies . On the other hand , in replicability analysis we explicitly require that the gene will manifest a recurring pattern of effects . In this study we develop new algorithms for replicability analysis that are both scalable ( i . e . , can handle many studies ) and allow controlling the false discovery rate . We show that our main algorithm called SCREEN ( Scalable Cluster-based REplicability ENhancement ) outperforms the other methods in simulated scenarios . Moreover , when applied to real datasets , SCREEN greatly extended the results of the meta-analysis , and can even facilitate detection of new biological results .
|
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2017
|
Extracting replicable associations across multiple studies: Empirical Bayes algorithms for controlling the false discovery rate
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4 one-step , real-time , reverse transcription loop-mediated isothermal amplification ( RT-LAMP ) assays were developed for the detection of dengue virus ( DENV ) serotypes by considering 2 , 056 full genome DENV sequences . DENV1 and DENV2 RT-LAMP assays were validated with 31 blood and 11 serum samples from Tanzania , Senegal , Sudan and Mauritania . DENV3 and DENV4 RT-LAMP assays were validated with 25 serum samples from Cambodia 4 final reaction primer mixes were obtained by using a combination of Principal Component Analysis of the full DENV genome sequences , and LAMP primer design based on sequence alignments using the LAVA software . These mixes contained 14 ( DENV1 ) , 12 ( DENV2 ) , 8 ( DENV3 ) and 3 ( DENV4 ) LAMP primer sets . The assays were evaluated with an External Quality Assessment panel from Quality Control for Molecular Diagnostics . The assays were serotype-specific and did not cross-detect with other flaviviruses . The limits of detection , with 95% probability , were 22 ( DENV1 ) , 542 ( DENV2 ) , 197 ( DENV3 ) and 641 ( DENV4 ) RNA molecules , and 100% reproducibility in the assays was obtained with up to 102 ( DENV1 ) and 103 RNA molecules ( DENV2 , DENV3 and DENV4 ) . Validation of the DENV2 assay with blood samples from Tanzania resulted in 23 samples detected by RT-LAMP , demonstrating that the assay is 100% specific and 95 . 8% sensitive ( positive predictive value of 100% and a negative predictive value of 85 . 7% ) . All serum samples from Senegal , Sudan and Mauritania were detected and 3 untyped as DENV1 . The sensitivity of RT-LAMP for DENV4 samples from Cambodia did not quite match qRT-PCR . We have shown a novel approach to design LAMP primers that makes use of fast growing sequence databases . The DENV1 and DENV2 assays were validated with viral RNA extracted clinical samples , showing very good performance parameters .
Dengue is a worldwide public health concern annually affecting more than 100 million people in tropical and subtropical areas [1 , 2] . It is caused by dengue virus ( DENV ) , the most common vector-borne viral pathogen of humans , transmitted by mosquitoes of the Aedes genus ( primarily A . aegypti and to a lesser extent A . albopictus ) , as previously reviewed [3] . DENV infection in humans results in a broad spectrum of disease manifestations , ranging from self-limiting , acute febrile illness ( dengue fever ) to more severe forms of the disease ( dengue haemorrhagic fever and dengue shock syndrome ) , which may lead to death [4] . In 2013 , the annual global incidence was estimated close to 390 million DENV infections , which was more than three times the dengue burden estimate of the World Health Organization [2] . DENV is an enveloped virus ( genus Flavivirus , family Flaviviridae ) with a genome that consists of a single-stranded , positive-sense RNA molecule of about 11 kb in length . The DENV genome encodes three structural proteins ( C , capsid; prM , pre-membrane , and E , envelope ) at the N terminus and seven non-structural ( NS ) proteins ( NS1 , NS2a , NS2b , NS3 , NS4a , NS4b and NS5 ) [5 , 6] . This virus is classified into four phylogenetically related and loosely antigenically distinct serotypes ( DENV1 , DENV2 , DENV3 and DENV4 ) , each of which contains phylogenetically different genotypes [7–9] . DENV outbreaks between 2006 and 2013 , in India , Vietnam , Solomon Islands , Myanmar , China , Singapore , Malaysia and Portugal [10–14] , highlight the necessity of rapid virus detection to identify DENV as the cause of an outbreak , in order to manage and control virus spread in infrastructure poor urban , peri-urban and rural settings . Notably , routine detection of DENV in children who are often asymptomatic carriers could improve outbreak control [15] . A first vaccine has recently been licensed for the prevention of dengue , which aims to reduce the number of hospitalizations per year , being approved for people aged between 9 to 45 years [16] . Traditional virus isolation is time-consuming , requires experienced staff , costly facilities and equipment and needs more than seven days to complete the assay [17 , 18] . IgM- and IgG-capture enzyme-linked immunosorbent assay ( ELISA ) are most widely used but some degree of cross-reactivity against other flaviviruses is usually observed and this method is not useful when antibody titers are not sufficiently high ( febrile viremic phase ) [19] . Molecular amplification techniques to detect DENV RNA ( RT-PCR , quantitative RT-PCR—qRT-PCR ) , which have emerged as a new standard , have a quick turnaround time and can distinguish DENV serotypes [20–26] . However , these techniques require sophisticated equipment and experienced staff , making them unpractical for laboratories with limited resources . Loop-mediated isothermal amplification ( LAMP ) has the potential to substitute PCR-based methods because of its simplicity , rapidity , specificity , sensitivity and cost-effectiveness , as no special equipment is needed ( just a heating block or water bath capable to maintain a constant temperature between 60°C to 65°C ) [27–29] . Reactions can be visualised by monitoring either the turbidity in a photometer or the fluorescence in a fluorimeter , by visual inspection under UV lamp when using an intercalating dye or by colour change [8 , 28–36] . Previously reported reverse transcription LAMP ( RT-LAMP ) assays for DENV target the 3’ untranslated region ( UTR ) [8 , 30 , 32 , 34 , 37] , whilst other detect a fragment of the C-prM region [33] , a conserved region of the NS1 [36] , or regions of NS2A ( DENV1 ) , NS4A ( DENV3 ) , NS4A ( DENV2 ) and the 3’ UTR ( DENV4 ) [38] . In all cases information about the primer design is limited as only one sequence per serotype or reference sequences were considered or it is not clearly detailed how the sequence alignment was carried out or how many sequences were included in the design . An initial screen of all published DENV RT-LAMP detection amplicons quickly revealed that all of them fail to cover the documented sequence variation . To improve DENV RT-LAMP design we used the LAMP Assay Versatile Analysis ( LAVA ) algorithm [39] which solves the limitations of existing LAMP primer-designing programs by allowing designs based on large multiple sequence alignments . Our LAMP design is based on 2 , 056 whole-genome DENV sequences covering DENV strains from 2004 to 2014 and yielded 4 one-step , real-time RT-LAMP assays to specifically detect each DENV serotype .
Ethical approval for retrospective use of the anonymized samples in diagnostic development research was available: Tanzania samples ( Ethikkommission Basel in Switzerland , Institutional Review Board of the Ifakara Health Institute and National Institute for Medical Research Review Board in Tanzania ) , IPD and IPC samples ( Ministry of Health of Senegal and National Ethics Committee for Health Research of Cambodia , respectively ) . Virus material: DENV isolates were provided and tested at the Institut Pasteur in Paris ( Table 1 ) . TriReagent extracts from flavivirus culture supernatants were provided by M . Weidmann . Inactivated strains ATCC VR-344 ( DENV1 ) , ATCC VR-345 ( DENV2 ) , ATCC VR-1256 ( DENV3 ) and ATCC-1257 ( DENV4 ) were provided by ENIVD / Robert Koch Institute . An inactivated Zika virus strain ( ZIKV , H/PF/2013 ) was provided by Prof . Xavier de Lamballerie ( Unité des Virus Emergents , Marseille , France ) . An External Quality Assessment ( EQA ) 2015 panel was provided by QCMD ( Quality Control for Molecular Diagnostics , Glasgow , UK ) including ten unknown samples ( 15–01 to 15–10 ) . Patient samples: We used RNA extracts of 31 blood samples collected during a fever study in Tanzania , 2013 ( Table 2 ) provided by the Swiss Tropical and Public Health Institute in Basel , Switzerland . These samples included 24 DENV qRT-PCR positive , 2 DENV positive ( not characterized by qRT-PCR ) and 5 negative samples . In addition , a negative sample from MAST Diagnostica GmbH ( Reinfeld , Germany ) was included . RNA extracts of 11 DENV qRT-PCR serum samples from Senegal , Sudan and Mauritania collected in November-December 2014 by the Institut Pasteur in Dakar ( IPD ) , Senegal ( Table 3 ) were tested by qRT-PCR and LAMP in Dakar . Additionally serum samples from Cambodia collected through the National Dengue Surveillance System [40] were tested . RNA was extracted and air-dried using pre-dried RNAstable 1 . 5 mL microfuge tubes ( Biomatrica , USA ) from 13 DENV3 and 12 DENV4 samples , collected by the Institut Pasteur du Cambodge ( IPC ) in 2004–2006 and between 2008 and 2014 , respectively . Samples were shipped at ambient temperature . Moreover , samples were tested by qRT-PCR before shipment and after receipt and reconstitution in molecular grade water . Overall the qRT-PCR CT deviation was in a range of 0 . 8 CT . Five μL RNA of each sample were used per reaction . RNA extractions were carried out using the RNeasy mini ( DENV strains from Robert Koch Institute , QCMD samples ) ( QIAGEN , Crawley , West Sussex , UK ) and the QIAamp Viral RNA mini ( DENV samples from IPD and IPC and ZIKV strain from Unité des Virus Emergents ) ( QIAGEN , Courtaboeuf , France ) kits . TriReagent extracts were processed according to the manufacturer’s extraction protocol ( Sigma-Aldrich , Dorset , UK ) . RNA extraction of the clinical samples from Tanzania was initially performed from 50 μL whole blood using a trial version of a nucleic acid isolation system equivalent to the protocol established for the MagSi-gDNA blood kit ( MagnaMedics , Geleen , The Netherlands ) . RNA was eluted in 190 μL elution buffer , and 5 μL per sample were used for each RT-LAMP reaction . Additionally , an improved trial version of the MagnaMedics system for nucleic acid isolation , starting from 100 μL whole blood and eluting the RNA in 100 μL elution buffer , using 5 μL per sample for each RT-LAMP reaction , was used . RNA was extracted from the clinical samples from Senegal using the QIAamp Viral RNA mini kit . A DENV RNA standard was transcribed from the DENV 3’ UTR region , ligated into pCRII and evaluated as previously described [41] . DEN FP and DEN P were as described with the probe now tagged 5’-FAM / BBQ-3’ but an adapted reverse primer DEN RP2 ( 5’-CTGHRGAGACAGCAGGATCTCTG-3’ ) as described [42] . DENV qRT-PCR was performed using the Light Cycler 480 Master Hydrolysis Probes ( Roche , Mannheim , Germany ) in a 20-μL reaction volume containing 1x LightCycler 480 RNA Master Hydrolysis Probes , 3 . 25 mM activator Mn ( OAc ) 2 , 500 nM primers DEN FP and DEN RP2 , 200 nM probe DEN P , and 1 μL RNA template on the LightCycler 2 . 0 ( Roche ) , as follows: reverse transcription for 3 min at 63°C , activation for 30 s at 95°C , followed by 45 cycles consisting of amplification for 5 s at 95°C and 15 s at 60°C and a final cooling step of 40 s at 40°C . Analysis of the reactions was conducted using LightCycler software version 4 . 1 . 1 . 21 ( Roche ) . The Institut Pasteur in Dakar performed a qRT-PCR [43] , using the ABI7500 Fast Real-time PCR System ( Applied Biosystems , Foster City , CA ) . An RT-PCR assay , which simultaneously detects the 4 DENV serotypes , followed by a nested PCR , that specifically detects each DENV serotype , were used [20] . A two-step approach was used . First , all available sequences of DENV1 to 4 were downloaded from the NCBI database . Searches were limited to the samples collected between 2004 and 2014 . All sequences were then aligned ( for each serotype ) using GramAlign v3 . 0 [44] , and diversity was assessed using the glPCA module of R/adegenet v1 . 4 . 1 [45] . Finally , based on the Principal Component Analysis ( PCA ) and phylogenetic tree ( Neighbor-Joining tree using the R/ape 3 . 2 package ) , the sequences were manually split into different clusters in order to maximise the region of sequence identity . LAMP DNA signatures for each cluster ( and all combinations to minimise the number of primer sets ) were designed using a modified version [https://github . com/pseudogene/lava-dna] of LAVA [39] applying the loose parameters set for DENV1-3 and the standard parameter set for DENV4 . Full scripts and methods are available on GitHub at https://github . com/pseudogene/lamp-denv . All the designed sets of primers were first checked for primer dimerisation with the VisualOMP version 7 . 8 . 42 . 0 ( DNA Software , Ann Arbor , MI ) . In addition , primer combinations for each of the DENV assays were tested for primer dimerisation by comparing amplification signals in positive and negative controls . RT-LAMP reactions were run at 64°C using either an ESE-Quant TubeScanner ( QIAGEN Lake Constance GmbH , Stockach , Germany ) or Genie II ( Optigene , Horsham , UK ) , in a final reaction volume of 25 μL . The Genie II device displays the annealing curve for specificity after the reaction has finished , by melting curve analysis from 98°C to 80°C ( 0 . 05°C/s ) . Four RT-LAMP assays were developed , one for each DENV serotype ( S1 File ) . Each reaction consisted of 1x RM Trehalose , 6 mM MgSO4 , 5% polyethylene glycol ( PEG ) , 1 μL fluorochrome dye ( FD ) , 8 U Bst 2 . 0 DNA Polymerase ( New England BioLabs , Hitchin , Herts , UK ) , 10 U Transcriptor Reverse Transcriptase ( Roche ) and 1 μL template ( DENV RNA or H2O as negative control ) . For each primer set per RT-LAMP assay , the final concentrations was as follows: 50 nM F3 , 50 nM B3 , 400 nM FIP , 400 nM BIP , 200 nM FLOOP , 200 nM BLOOP . Before adding the Bst 2 . 0 DNA Polymerase , Transcriptor Reverse Transcriptase and template , mixes were incubated at 95°C for 5 min to melt any primer multi-mers and cooled immediately on ice for 5 min . Reaction times vary for each RT-LAMP protocol , running for 45 min ( DENV1 ) , 90 min ( DENV2 ) , 75 min ( DENV3 ) and 50 min ( DENV4 ) . RM Trehalose , MgSO4 , PEG and FD were supplied by MAST Diagnostica GmbH . Sensitivity analysis was performed in the ESE-Quant TubeScanner ( QIAGEN ) . Ten-fold dilutions of viral DENV RNA samples ( ATCC VR-344 ( DENV1 ) , ATCC VR-345 ( DENV2 ) , ATCC VR-1256 ( DENV3 ) and ATCC VR-1257 ( DENV4 ) ) , quantified by qRT-PCR , were used to analyse the sensitivity of the developed RT-LAMP assays ( range from 104−105 to 10 molecules/μL ) and 1 μL per dilution was added to the RT-LAMP reaction . The complete RNA standard was tested in eight separate runs . The values obtained were subjected to probit analysis ( Statgraphics plus v5 . 1 , Statistical Graphics Corp . , Princeton , NJ ) and the limit of detection at 95% probability for each RT-LAMP assay was obtained . Cross-specificity tests for the four RT-LAMP assays were carried out at the Institut Pasteur ( Paris ) using the QuantStudio 12K Flex Real-Time PCR System , and results were analysed with the software QuantStudio 12K Flex v1 . 2 . 2 . ( Applied Biosystems , Carlsbad , CA ) . Each of the RT-LAMP assays was tested using 1 μL RNA extracted from the DENV strains described in Table 1 . Cross detection of other flaviviruses , ZIKV , Yellow fever virus ( YFV ) , West Nile virus ( WNV ) and Ntaya virus ( NTAV ) , was analysed using the Genie II ( Optigene ) at the University of Stirling . The RT-LAMP assays were also tested against several DNA pathogens ( Salmonella Typhi , S . Paratyphi , Streptococcus pneumoniae and Plasmodium falciparum ) . DNA samples were provided by MAST Diagnostica GmbH . The performance of the RT-LAMP assays ( sensitivity and specificity ) was additionally evaluated using the 2015 DENV EQA panel provided by QCMD . Results obtained from QCMD refer to 8 core and 2 educational samples . Core samples are those needed to assess the performance from the regulatory point of view and educational samples are additional samples related to questions such as limit of detection or specificity . We used 31 blood samples from a fever study in Tanzania , 2013 ( Table 2 ) . Twenty-six samples had been confirmed as DENV2 positive by the Swiss Tropical and Public Health Institute ( Basel , Switzerland ) ( 2 of them were not tested by qRT-PCR ) . Aliquots of these blood samples were sent to MAST Diagnostica GmbH and stored at -20°C until RNA extraction was performed using the Magnamedics kit trial version . RNA samples were stored at -80°C . RT-LAMP reactions were run in the TubeScanner TS2 ( QIAGEN ) , using 5 μL RNA of each sample per reaction . The samples at IPD were analysed by both qRT-PCR [43] , and the DENV1 and DENV2 RT-LAMP assays ( in triplicates ) in an ABI7500 Fast Real-time PCR system ( Applied Biosystems ) , using 5 μL RNA of each sample per reaction . Sensitivity , specificity , positive predictive value ( PPV ) and negative predictive value ( NPV ) were obtained for the DENV2 RT-LAMP developed when compared against the results obtained by qRT-PCR .
The RNA standard was tested 3 times and similar crossing point ( CP ) values were obtained for the different dilutions from 107 to 103 RNA molecules detected ( S1 Fig ) , showing an efficiency ( E = 10−1/slope—1 ) of 0 . 99 ± 0 . 04 ( mean ± standard deviation , SD ) . Quantification of DENV1-4 RNA extracted from inactivated isolates ATCC VR-344 ( DENV1 ) , ATCC VR-345 ( DENV2 ) , ATCC VR-1256 ( DENV3 ) and ATCC VR-1257 ( DENV4 ) ( Table 1 ) ranged from 6 . 9x104–9 . 4x104 ( DENV1 ) , 4x105–5 . 3x105 ( DENV2 ) , 1 . 5x105 - 3x105 ( DENV3 ) , and 1 . 8x105–2 . 7x105 ( DENV4 ) RNA molecules/μL . In total 1 , 145 , 477 , 376 and 58 genomic sequences were retrieved from the NCBI database for DENV1 , DENV2 , DENV3 and DENV4 , respectively . Each serotype dataset was split into 4 to 21 clusters ( Fig 1A and S2–S4 Figs ) , allowing for the LAVA algorithm to design LAMP primer sets , and was executed for each group separately as well as for all possible combinations of the groups . Sets of primers that showed dimerisation when analysed with VisualOMP ( DNA Software , Ann Arbor , MI ) were discarded ( Fig 2A ) . Remaining sets where sequentially combined and tested by RT-LAMP to discard cases of primer dimerisation , visualised by the non-specific amplification signal ( intercalating dye ) in the no template control ( NTC ) ( Fig 2B ) . The final primer sets are described in Fig 1B and S1–S4 Tables and consist of 84 ( 14 amplicons , DENV1 ) , 72 ( 12 amplicons , DENV2 ) , 48 ( 8 amplicons , DENV3 ) and 18 ( 3 amplicons , DENV4 ) primers . When combining the amplicon primer sets for each RT-LAMP assay , amplification was not observed when using published standard LAMP primer concentrations for each primer set: 0 . 2 μM F3 , 0 . 2 μM B3 , 1 . 6 μM FIP , 1 . 6 μM BIP , 0 . 8 μM FLOOP and 0 . 8 μM BLOOP . To determine the concentration window of the complicated primer mix , a 2-fold dilution series of the above primer mix was used . Amplification yielding the best possible detection without amplification in the NTC was achieved at a dilution of 1:4 ( 50 nM F3 , 50 nM B3 , 400 nM FIP , 400 nM BIP , 200 nM FLOOP and 200 nM BLOOP , Fig 2C ) . Table 1 and Fig 3 show the results of the cross-specificity and cross-detection tests . All DENV cell culture RNA extracts were detected and no amplification was observed in the NTC . The RT-LAMP protocols for DENV2 , DENV3 and DENV4 were specific for each respective serotype . The RT-LAMP protocol for DENV1 detected all DENV1 RNA strains , but also scored positive in RNA extracts KDH0010A and VIMFH4 containing RNA extracts from DENV3 and DENV4 isolates , respectively ( Table 1 ) . Additional testing of samples KDH0010A and VIMFH4 by nested RT-PCR ( Fig 4A and 4B ) indicated contamination of the cell cultures samples with DENV1 confirming the RT-LAMP results . The RNA of other flaviviruses was not cross-detected ( Fig 3 and Table 1 ) . Specific amplification was also indicated by a specific single peak temperature in the melting curve analysis ( Fig 3B , 3D , 3F and 3H ) , with mean values ± SD of 85 . 4 ± 1 . 1°C ( DENV1 ) , 83 . 1 ± 1 . 0°C ( DENV2 ) , 84 . 3 ± 0 . 9°C ( DENV3 ) and 86 . 4 ± 0 . 3°C ( DENV4 ) . No amplification was observed when DNA from S . Typhi , S . Paratyphi , S . pneumoniae and P . falciparum was used as template in the different RT-LAMP assays ( Table 1 ) . The 2015 DENV EQA panel analysis confirmed that the RT-LAMP assays developed passed 8 core and the 2 educational samples of that panel . Concerning the core samples , 5 positive samples were scored 3/3 , and 1 positive sample was detected once ( the other 2 samples were negative ) . Results obtained from the educational samples indicated that 1 sample was detected in the 3 repetitions whilst the other sample was detected in 1/3 repetitions . DENV1-4 RNA samples , previously quantified by qRT-PCR , were used to analyse the sensitivity of the developed RT-LAMP assays . RT-LAMP protocols for DENV1 , DENV2 and DENV4 detected as few as 10 molecules per reaction , although this amount was only obtained in 3 , 5 and 2 of 8 repetitions , respectively , with the following mean times: 28 . 8 ± 6 . 3 min ( DENV1 ) , 78 . 2 ± 5 . 8 min ( DENV2 ) and 44 . 6 ± 3 . 3 min ( DENV4 ) . RT-LAMP for DENV3 detected as few as 102 molecules , but only in 4 of 8 reactions , at 44 . 9 ± 18 . 6 min . The lowest amount of molecules detected in the 8 reactions , showing 100% reproducibility , were 102 ( DENV1 , mean time of 25 . 3 ± 2 . 6 min ) , and 103 ( DENV2 , DENV3 and DENV4 , mean times of 69 . 2 ± 11 . 6 min , 37 . 2 ± 11 . 6 min and 26 . 8 ± 2 . 7 min , respectively ) ( Fig 5 ) . Considering 8 independent reactions per protocol developed , the probit analysis revealed that the limit of detection at 95% probability for each RT-LAMP was 22 RNA molecules ( DENV1 ) , 542 RNA molecules with a confidence interval from 92 to 3 . 2x1013 RNA molecules ( DENV2 ) , 197 RNA molecules ( DENV3 ) and 641 RNA molecules with a confidence interval from 172 to 1 . 2x105 RNA molecules ( DENV4 ) . Tables 2 and 3 show the results of the blood and serum samples analyses when using both qRT-PCR and RT-LAMP . Out of 26 DENV2-infected blood samples 24 scored positive in qRT-PCR with cycle threshold ( CT ) values ranging from 21 . 57–29 . 13 ( Table 2 , column 2 ) . In a first test DENV2 RT-LAMP detected 17/24 ( 70 . 8% positive samples ) with initial time to positive ( TT ) values between 37 and 89 min ( Table 2 , column 3 ) . RNA from 14 samples , including those with initial TT values over 60 min , negative in both RT-LAMP and qRT-PCR , and 6 DENV negative samples ( Table 2 ) , were extracted a second time using the optimized MagnaMedics extraction starting from 100 μL sample and yielding enhanced detection . Five samples with initial TT values from 81–89 min , now tested positive with TT values from 55–77 min . Six samples initially negative by RT-LAMP became positive with TT values of 61 . 7–72 . 2 min . Three samples , 1 of which had scored positive in qRT-PCR , remained negative in RT-LAMP . Most RNA samples extracted with the optimized method scored positive in all 3 replicates . One sample was detected 2/3 times , and 2 were detected only once . All negative samples included in these analyses scored negative . Calculation of the clinical sensitivity and specificity yielded 100% specificity ( CI: 0 . 63–1 . 00 ) , as no false positives were detected , and a sensitivity of 95 . 8% ( CI: 0 . 79–1 . 00 ) with 23/24 positive samples , a PPV of 1 . 00 ( CI: 0 . 85–1 . 00 ) and NPV of 0 . 86 ( CI: 0 . 42–1 . 00 ) . Table 3 summarises the results obtained with samples collected by the IPD and IPC . All 11 RNA samples from IPD used in this study were analysed in parallel by qRT-PCR and with DENV1 and DENV2 RT-LAMP assays . All scored positive in qRT-PCR ( CT 25 . 89–38 . 48 ) , 4 samples scored positive in the DENV1 RT-LAMP , and 7 scored positive in the DENV2 RT- LAMP ( TT values 20–45 min ) . Samples 267175 , 267197 and 267174 were serotyped as DENV1 with the developed RT-LAMP . Additionally , of 12 qRT-PCR positive DENV4 samples dried with RNAstable shipped by IPC , 10 tested positive by qRT-PCR after shipment , and 9 were detected by DENV4 LAMP . Of 13 DENV3 samples qRT-PCR positive before shipment , only 1 tested positive by qRT-PCR on arrival and only 3 by RT-LAMP .
Dengue is now prevalent in more than 100 countries of the tropics and subtropics and as DENV continues to spread , all four serotypes co-circulate widely [46–48] . The introduction of new DENV strains continues through travellers moving between dengue-endemic countries [11] and recently the capacity of individual mosquitoes to carry multiple DENV serotypes was described [49] , while elsewhere acute simultaneous infection with several DENV serotypes was observed [10] . DENV detection methods include virus culture , which is time consuming [17 , 18] as well as ELISA or immunofluorescence methods to detect IgM and IgG which suffer from cross-reactivity to other flaviviruses antibodies and which are only considered valid when antibody titers are sufficiently high [19] . The introduction of NS1 antigen detection has improved the situation and recent studies show a high sensitivity of NS1 detection [50] , with some concluding that the combination with IgM detection can outperform PCR [51] . However , its use for routine screening in dengue epidemics is questioned in terms of clinical necessity [52] . For molecular RNA detection , nested PCR [20] and real time PCR-assays [21–26] with high specificity and sensitivity are being used but need expensive and sophisticated thermocyclers and experienced staff . In recent years , isothermal amplification assays have been described , such as RT-LAMP [8 , 30 , 32–38] and RT-RPA [53 , 54] . These assays require less expensive equipment and can be delivered in dried pellet format , making handling easier and amenable to poor infrastructure settings . Worldwide monitoring and the use of Next Generation Sequencing methods have increased the number of complete DENV genomes sequenced and deposited in GenBank to 2 , 988 ( as of June 2016 ) . It is virtually impossible to use this amount of sequence information to manually align and design amplicons for molecular detection methods . There have been several attempts to create algorithms to derive signature sequences for PCR techniques from sequence datasets or alignments [55 , 56] . LAMP amplicons are inherently more challenging to design as they require a minimum of 4 and a maximum of 6 signature sequences . LAVA software was developed to facilitate the determination of signature sequences for LAMP primer design using a set of aligned sequences [39] . The original and modified version of LAVA take into consideration the limitations observed with other primer-design programs ( LAMP DESIGNER [http://www . optigene . co . uk/lamp-designer/] and PRIMER EXPLORER [https://primerexplorer . jp/e/] , such as preventing the use of extensive alignments or sequences longer than 2 , 000 nt . We used this approach to design serotype-specific primers aiming to match all possible DENV strains circulating worldwide , by considering 2 , 056 available GenBank DENV sequences ( 2004–2014 ) . This is the greatest difference compared to other previously published RT-LAMP assay designs in which primer design focused on the conserved 3’ UTR , NS1 or C-prM regions but detailed limited information about the DENV sequences used to develop the primers . As the LAMP primers were designed from different clusters of each DENV serotype obtained after PCA and phylogenetic analyses , the individual LAMP amplicons locate to several regions across the DENV genome conserved in these clusters ( Fig 1 ) . This allows an overall detection of DENV variability surpassing any other molecular amplification assay . The final amplicons were selected through a combination of in silico primer dimer formation assessment ( Visual OMP ) and in vitro assessment by checking amplicons selected in the first step for unspecific amplification in the NTC . A similar methodology has been used to design RT-LAMP primers to detect Chikungunya virus ( manuscript submitted to PLoS Neglected Tropical Diseases ) and we consider this approach would be suitable for the assay development of other infectious diseases . The final DENV1-4 specific RT-LAMP assays contained 84 , 72 , 48 and 18 oligonucleotides respectively . The challenge was to find a working concentration of these oligonucleotide mixes , which would allow for sensitive detection . A 2-fold dilution series approach for the individual final primer mix allowed to identify a working concentration window in the dynamic range of these assays . This however came at the cost of run time . In order to increase the reaction speed without losing sensitivity , several combinations of enzymes were tested . We tested the combination of AMV RT ( Promega , Southampton , UK ) and GspSSD DNA polymerase ( Optigene ) recommended by others who successfully developed rapid RT-LAMP assays with 10–15 minute run times [57] ( Manuguerra personal communication ) . We also tested Bst 3 . 0 DNA polymerase ( New England BioLabs ) , but found that none offered an advantage over the enzyme combination we used ( Transcriptor Reverse Transcriptase and Bst 2 . 0 ) . As a matter of fact , we saw an increased level of unspecific amplification with Bst 3 . 0 DNA polymerase ( data non-shown ) . Thus currently reaction times range from 45 ( DENV1 ) to 90 minutes ( DENV2 ) . This was not correlated with the number of oligonucleotides in the mixture but may reflect the efficiency of the individual primer sets in the mixture detecting the respective standard strains we used for the validation , and the low oligonucleotide concentration . Alternative approaches to evaluate the sensitivity of each RT-LAMP would consist of having either a pool of RNA samples representative for each amplicon included or specific primer sets for each particular DENV strain that would be compared with the primer mixtures included in the developed assays . We used an RNA standard evaluated by qRT-PCR to quantify viral RNA of DENV1-4 . These quantified RNA were then used to test the analytical sensitivity of the 4 individual specific RT-LAMP assays for the detection of each serotype . The analytical sensitivities of the DENV1-4 RT-LAMP assays , as estimated per probit analysis , ranged from 22 to 641 RNA molecules detected , and 100% reproducibility after 8 independent runs was achieved for 102−103 RNA molecules detected . Therefore , results were in the range observed for previously described RT-LAMP methods detecting all four serotypes in a single reaction [8 , 33 , 37] with sensitivities between 10 and 100 RNA molecules detected , and RT-LAMP assays distinguishing the serotypes in individual reactions [30 , 38] . For the latter assays the analytical sensitivities determined were 10 to 100 plaque-forming units ( PFU ) /mL and 10 RNA molecules detected respectively . Our RT-LAMP assay for DENV1 showed a limit of detection as per probit analysis of 102 PFU/mL with a confidence interval from 20 to 7 . 8x103 PFU/mL ( data non-shown ) . The assays developed were serotype-specific , and no cross-detection of other flaviviruses was observed . Surprisingly , 2 viral preparations tested—KDH0010A ( DENV3 ) and VIMFH4 ( DENV4 ) —were also found positive for DENV1 . Subsequent analysis by serotype-specific nested PCR [20] confirmed the presence of DENV1 RNA probably due to contamination during RNA extraction or virus culture , and indicating that the DENV RT-LAMP assays had picked up the contamination correctly . EQA panels have been developed in order to evaluate the performance and reliability of current diagnostic methods in laboratories worldwide , by using different samples ( both negative and positive samples , including different concentrations ) that provide information about their specificity and sensitivity [58 , 59] . The EQA panel used in this study , provided by QCMD , comprises strains for the 4 DENV serotypes , as well as negative samples . The analysis showed that our RT-LAMP assays passed all the samples included in the 2015 DENV EQA panel , consisting of 8 core and 2 educational samples . For evaluation with clinical material , RNA was extracted from whole blood samples collected in Tanzania , confirmed as DENV2 positive by qRT-PCR . A bead-based extraction protocol was improved and , in addition , instead of using 50 μL whole blood and eluting in 200 μL RNA , the extraction commenced from 100 μL whole blood and RNA was eluted into 100 μL . Due to this improved extraction protocol , time to positivity reduced from 81–89 min to 55–77 min . In some cases , there were disparate results between RT-LAMP and qRT-PCR . Sample 1232 , negative by RT-LAMP , had a CT value of 28 . 78 , and samples 1241 and 1473 , with CT values of 24 . 27 and 29 . 13 , showed current mean TT values of 70 and 73 . 9 min , respectively . These differences in results observed may not be related to the sensitivity levels of the individual assay and we suggest that the performance of isothermal amplification reactions could be compromised when not using fresh samples , as previously described [53] . All 11 serum samples collected by Institut Pasteur in Dakar ( 2014 ) , tested positive by qRT-PCR and the DENV1 and DENV2 RT-LAMP assays . While 3 of the samples could not be characterised with the qRT-PCR protocol , they were successfully amplified by the DENV1 RT-LAMP , providing evidence that determination of serotype is possible when handling samples that have not been serotyped yet . Based on the results obtained for the fever study in Tanzania , our DENV2 RT-LAMP scored a sensitivity of 95 . 8% ( CI: 0 . 79–1 . 00 ) and specificity of 100% ( CI: 0 . 63–1 . 00 ) in reference to the qRT-PCR used by the Swiss Tropical and Public Health Institute , indicating that all detected as positive by the LAMP assay were truly positive and no false positives were detected . We used predried tubes of RNAstable for shipment of DENV4 and DENV3 RNA extracts from Institut Pasteur du Cambodge . The efficiency of this type of shipment at ambient temperature was disappointing . Surprisingly DENV3 sample RNA extracts suffered most from this type of shipment and this could not be improved in altogether three shipment trials . The results for DENV4 samples indicate specific detection which does not quite match the qRT-PCR sensitivity . DENV3 samples were detectable but sensitivity could not be assessed . The determination of clinical sensitivity , specificity , PPV and NPV allows interpretation of diagnostic results for clinical decisions [60 , 61] . The scores obtained for specificity , sensitivity , PPV and NPV were in the range observed for previously published assays [8 , 30 , 33 , 36–38] . The scores obtained for PPV and NPV estimate the probability that the disease is present or absent depending of the result is positive or negative . Since the samples were collected in a fever study , the results obtained with the RT-LAMP ( PPV = 100% and NPV = 85 . 7% ) highlight a good performance of the method in determining true positive cases while excluding negative cases . PPV and NPV are very dependent of the number of positive and negative samples used , providing valuable information during naturally occurring infections in prospective trials . The values obtained in our study may not reflect this as only thirty samples were analysed and a larger number of both positive and negative samples would be needed to refine these results . To conclude , we have shown a novel approach to designing LAMP primers that makes use of fast growing sequence databases . During the study time the number of complete DENV genome entries grew by 932 genomes deposited . To be able to cover all of the diversity documented , our approach devised 4 complicated mixes of oligonucleotides for the detection of the individual DENV1-4 serotypes . The DENV1 and DENV2 assays were validated with viral RNA extracted clinical samples and showed very good performance parameters . Finally the combination of PCA analysis and molecular detection assays design should also be considered for other molecular assay formats since the available sequence dataset of several pathogens has increased beyond what can be handled by traditional design based on simple alignments .
|
The co-existence of several dengue virus ( DENV ) serotypes within the same location and/or individuals as well as a single mosquito being able to carry multiple DENV serotypes highlight the necessity of specific diagnostic tools capable of detect and serotype DENV strains circulating worldwide . In addition , these methodologies must be highly sensitive in order to detect the genome at low levels ( i . e . , before the onset of clinical symptoms ) and not cross-detect other flaviviruses . Isothermal amplification methods ( such as reverse transcription loop-mediated isothermal amplification , RT-LAMP ) are affordable for laboratories with limited resources and do not need expensive equipment . Because of the increasing number of publicly available full DENV genome sequences , traditional primer design tools are not able to handle such huge amount of information . Therefore , to be able to cover all the diversity documented , we developed 4 complicated oligonucleotide mixes for the individual detection of DENV1-4 serotypes by RT-LAMP . This approach combined Principal Component Analysis , phylogenetic analysis and LAVA algorithm . Our assays are specific and do not cross-react with other arboviruses and DNA pathogens included in this study , they are sensitive and have been validated with samples from Tanzania , Senegal , Sudan , Mauritania and Cambodia , showing very good performance parameters .
|
[
"Abstract",
"Introduction",
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"methods",
"Results",
"Discussion"
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2018
|
Development and validation of four one-step real-time RT-LAMP assays for specific detection of each dengue virus serotype
|
Although the Zika virus ( ZIKV ) epidemic ceased to be a public health emergency by the end of 2016 , studies to improve knowledge about this emerging disease are still needed , especially those investigating a causal relationship between ZIKV in pregnant women and microcephaly in neonates . However , there are still many challenges in describing the relationship between ZIKV and microcephaly . The few studies focusing on the epidemiological profile of ZIKV and its changes over time are largely limited to systematic reviews of case reports and dispersal mapping of ZIKV spread over time without quantitative methods to analyze patterns and their covariates . Since Brazil has been at the epicenter of the ZIKV epidemic , this study examines the geospatial association between ZIKV and microcephaly in Brazil . Our study is categorized as a retrospective , ecological study based on secondary databases . Data were obtained from January to December 2016 , from the following data sources: Brazilian System for Epidemiological Surveillance , Disease Notification System , System for Specialized Management Support , and Brazilian Institute of Geography and Statistics . Data were aggregated by municipality . Incidence rates were estimated per 100 , 000 inhabitants . Analyses consisted of mapping the aggregated incidence rates of ZIKV and microcephaly , followed by a Getis-Ord-Gi spatial cluster analysis and a Bivariate Local Moran’s I analysis . The incidence of ZIKV cases is changing the virus’s spatial pattern , shifting from Brazil’s Northeast region to the Midwest and North regions . The number of municipalities in clusters of microcephaly incidence is also shifting from the Northeast region to the Midwest and North , after a time lag is considered . Our findings suggest an increase in microcephaly incidence in the Midwest and North regions , associated with high levels of ZIKV infection months before . The greatest burden of microcephaly shifted from the Northeast to other Brazilian regions at the beginning of 2016 . Brazil’s Midwest region experienced an increase in microcephaly incidence associated with ZIKV incidence . This finding highlights an association between an increase in ZIKV infection with a rise in microcephaly cases after approximately three months .
On February 1 , 2016 , the World Health Organization ( WHO ) declared that the Zika virus ( ZIKV ) epidemic was an international public health emergency [1] . The increasing evidence of a causal relationship between ZIKV in pregnant women and an unpredicted rise in the incidence of microcephaly , later characterized as fetal congenital ZIKV syndrome [2–4] , prompted this designation . Findings suggest that ZIKV affects neurogenesis during human brain development , leading to neurological syndromes as observed in Guillain-Barré or microcephaly [4] . As of the latest ZIKV status report in March 2017 , 48 of 50 countries and territories in the Americas have confirmed autochthonous cases of ZIKV [5] . Half of these countries and territories ( 24 ) have confirmed cases of congenital ZIKV syndrome [5] . Brazil remains at the epicenter of the ZIKV epidemic with reports of 130 , 000 cases in 2016 [6] . In October 2015 , early warning signs of a link between ZIKV in pregnant women and microcephaly in neonates surfaced when the number of infants born with microcephaly in the Northeastern state of Pernambuco rose [7] . From 2015 to 2016 , 2 , 229 cases of microcephaly in infants were confirmed [8] , over a 10-fold increase from the yearly average of 157 cases between 2000 and 2014 [9] . Consequently , a body of literature has emerged supporting a causal association between ZIKV infection during pregnancy and infant microcephaly [2 , 10–12] . Existing literature focused on the Brazilian ZIKV epidemic consists heavily of clinical management guidelines and longitudinal and case-control studies of mothers diagnosed with ZIKV and their infants to assess risks of congenital ZIKV syndrome [13 , 14] . Despite the ongoing research , challenges related to preventing ZIKV and its consequences , such as microcephaly , are still staggering . First , the committed countries have a limited epidemiological surveillance capacity . Second , the time delay between the onset of the ZIKV epidemic and the microcephaly reports means public policy is still defining the epidemic and not yet able to prevent its consequences . The rise in microcephaly incidence was documented only after infants were born , mostly due to limited ZIKV testing during intrapartum infection , leading to delays in timely epidemiologic and geographic surveillance of both diseases . Using mapping techniques to study vector-borne disease epidemiology has proven crucial , as seen with previous research on dengue virus [15] and chikungunya [16] . These health geography studies can identify disease propagation patterns and high-risk areas , then model forecasts allowing inferences for the determinants of these outcomes [17] . To date , however , few studies have utilized geospatial techniques to investigate the ZIKV epidemic in Brazil , and the spatial-temporal association between ZIKV and microcephaly remains uninvestigated . The only available works [18 , 19] rely on systematic reviews of case reports or dispersal mapping of ZIKV spread over time without quantitative methods to analyze patterns and their covariates . By using a framework of health geography , we believe we can provide insights into disease spread patterns , high-risk areas , and forecast disease models that allow for inferences regarding the determinants of these outcomes [17] . This study examines the geospatial association between ZIKV and microcephaly January—December 2016 . Specifically , we aim to 1 ) spatially represent diffusion patterns for both ZIKV and microcephaly incidence; 2 ) identify hot and cold spots of high and low incidence clusters for both diseases and any changes in their distribution across time; and 3 ) measure the spatial-temporal association between ZIKV and microcephaly spread . We hypothesize that areas with higher ZIKV incidence will be positively associated with an increase in microcephaly incidence after a time lapse of at least 16 weeks [9] .
This ecological , retrospective study utilizes secondary data analysis of national health data systems during the ZIKV epidemic from January to December 2016 in Brazil . The largest country in Latin America in both size and population , Brazil spans approximately 3 . 2 million square miles with an estimated 190 . 7 million inhabitants [20] . An upper middle—income country and member of BRIC , Brazil ranks ninth among global economies [21] and has a high human development index level of 0 . 754 [22] . Brazil achieved universal health care coverage in the 1990s with the implementation of and reforms to the Unified Health System ( SUS ) [23] . Driven by national policies favoring decentralization and community-based models of health services delivery , the structure of the SUS is conducive to ecological studies of health outcomes [24] . The SUS maintains over 15 national-level health informatics and epidemiological databases to guide population health surveillance [24] . Data of infrastructure to outcome indicators are available , comprising information at individuals , municipalities or states levels . [6] . Marked inequality among Brazil’s regions , namely lower development levels and widespread poverty in the Northeast , results in disparities in health services coverage and population health indicators [25] , which are significant when addressing diseases with natural and built environmental determinants ( Fig 1 ) . The availability of publicly accessible government databases at the national level , coupled with the socio-geographic landscape of the country and manifestations of the ZIKV epidemic , make Brazil an optimal setting in which to investigate the spatial-temporal association between ZIKV infection and microcephaly spread . Data on confirmed cases of ZIKV were obtained from the Disease Notification System [26] . ZIKV infection was included in the Brazilian Ministry of Health compulsory notification disease list on February 17 , 2016 . From this date on , every health system unit in Brazil was obligated to report any confirmed or suspected case of ZIKV to the Ministry of Health [27] . ZIKVnotification is performed on a weekly basis , and deaths related to ZIKV must be reported within a maximum of 24 hours of death . Notification information is uploaded to the SINAN NET system ( acronym in Portuguese—Disease Notification Information System ) [28] . During our study , we included only confirmed cases of ZIKV . A suspected case was considered confirmed if one of the following characteristics was observed: positive of viral isolation test result , RNA viral detection by reaction of reverse transcriptase , or IgM serology . After confirmation of autochthonous circulation , the cases of Zika should be confirmed by clinical-epidemiological criteria . Despite that , suspected cases in pregnant women , neurological manifestations , and death still need to be confirmed using a serology test [27] . Data on confirmed cases of microcephaly were retrieved online from the System for Specialized Management Support [8] . Microcephaly was defined as an infant with 37 or more weeks of gestation with a head circumference equal to or less than 31 . 9 cm for male infants , or equal to or less than 31 . 5 cm for female infants , in concurrence with WHO standards [1] . For babies less than 37 weeks gestation at birth , the InterGrowth curve was used since the cephalic perimeter varies according to an infant’s gestational age [1] . Monthly case reports of microcephaly must be sent to the RESP-Microcephaly system ( acronym in Portuguese—Register of Events in Public Health for Microcephaly ) . Additionally , population data were obtained from the Brazilian Institute of Geography and Statistics ( Instituto Brasileiro de Geografia e Estatística ) [20] . Data from these three sources were merged for all 5570 Brazilian municipalities . All secondary data extracted correspond to 2016 . Raw values of confirmed cases of ZIKV and microcephaly were used to compute incidence rates . Incidence rates were expressed continuously per 1 , 000 inhabitants for ZIKV and 100 , 000 for microcephaly , at the municipal level . We opted to use different scales because the prevalence of ZIKV and microcephaly in the general population occur on different scales . Ideally , we would have used better exposure controls such as pregnant women and newborns , such as the results reported by De Oliveira et al [29] . Thus , we decided to present the results in indices by population , which would give us a robust metric . Data analyses were carried out in three steps . First , we conducted a descriptive analysis for the aggregated incidence rates of ZIKV and microcephaly for six 2-month time periods between January and December 2016 at the regional level . As such , the first bi-monthly period comprised January and February , the second period March and April , and so on . Next , we conducted a Getis-Ord-Gi [30] spatial cluster analysis to identify the presence of clustering according to incidence rates of both diseases throughout 2016 . The Getis-Ord-Gi analysis produced two types of spatial clusters: hotspots with high values of incidence of both diseases , and coldspots highlighting low incidence areas . Lastly , a Bivariate Local Moran’s I analysis was carried out to evaluate the temporal-spatial association between ZIKV and microcephaly incidence rates over time [31] . The Bivariate Local Moran’s I is a statistic that evaluates the spatial correlation between two variables [31] . It verifies whether the value of the first variable in a reference municipality is related to the average value of the second variable in neighboring municipalities . Therefore , if the two variables are measured in different time periods , and a long enough time lapse is taken into account , this technique can provide insights regarding whether previous incidence of ZIKV infection in any reference municipality is associated with microcephaly cases in the neighboring region . This analytical strategy relies on the assumption that ZIKV has a causal role in microcephaly when pregnant women are infected [4 , 13] . Although there is not a consensus of the exact time in pregnancy that a ZIKV infection will cause microcephaly , there is a high volume of evidence supporting the association [14 , 32] . Thus , a time lag between ZIKV infection and microcephaly incidence can be approximated to the gestational period ( in our case , 3 to 4 bi-monthly time periods ) [9] . Therefore , considering the importance of a time lag between ZIKV and the emergence of microcephaly cases we opted to test multiple scenarios . For each scenario , a minimum difference of one bi-monthly period was considered . Incidence of ZIKV infection during the first and second bi-monthly periods were compared to microcephaly incidence rates of the third to sixth bi-monthly periods . This time arrangement was applied to all 2016 bi-monthly periods for both diseases , for all possible combinations that respect a minimum time lag of two bi-monthly periods . We chose this time lag period considering previous findings by the Centers for Disease Control [9] . The categorization provided by Bivariate Local Moran’s I technique can identify clusters based on ZIKV incidence considering , simultaneously , the microcephaly levels months later . In this scenario , a High-High cluster , for example , would represent a group of municipalities with elevated rates of microcephaly surrounded by municipalities with high values of ZIKV incidence a given number of months beforehand . Incidence rate mapping and the Getis-Ord-Gi cluster analysis were performed in ARCGIS 10 . 3 [33] . The Bivariate Local Moran’s I analysis was conducted using the software GEODA [34] .
From January—December 2016 , Brazilian incidence rates of ZIKV per 100 , 000 inhabitants varied from 13 . 01 to 0 . 21 . During the year , ZIKV incidence substantially decreased in all regions . Though this reduction was observed in all regions , it was more pronounced in the Midwest and Northeast regions . A high number of Midwest region municipalities showed incidence rates above 20 cases per 100 , 000 inhabitants during the first and second bi-monthly periods . At these time points , the Midwest contained the greatest number of confirmed ZIKV cases , with mean incidence rates of 82 . 06 for the first bi-monthly period and an annual average of 21 . 36 cases per 100 , 000 inhabitants . ZIKV incidence decreased in the following bi-monthly periods of the year . During the third bi-monthly period , the greatest mean ZIKV incidence rate was seen in the Northeast ( 7 . 56 ) , which also had the greatest mean ( 2 . 81 ) for the fourth bi-monthly period . The fifth and sixth bi-monthly periods were marked by a continued reduction of high- and medium-incidence municipality clusters . By the fifth and sixth bi-monthly periods , mean ZIKV incidence rates had declined , with the highest for the fifth bi-monthly period in the Northeast ( 0 . 61 ) and the highest for the sixth bi-monthly period in Midwest ( 0 . 42 ) . For both the first and second bi-monthly periods , the Northeast had its highest mean microcephaly incidence rates of 0 . 66 and 0 . 71 , respectively . During the third and fourth bi-monthly periods , the density of microcephaly incidence clusters in the Northeast diminished . During the fifth and sixth bi-monthly periods , the Northeast had the highest mean microcephaly incidence rates of 0 . 20 and 0 . 18 , respectively , remaining the region most affected across the observed period ( Fig 2 ) . The geospatial distribution becomes more diffuse over time , with scattered groups of municipalities with high incidence in the Midwest , North , Northeast , and Southeast regions . Patterns of microcephaly geospatial distribution , distinct from that of ZIKV infection , tended to be concentrated in the Northeast during the first , second , and third bi-monthly periods ( Figs 3 and 4 ) . The results of the cluster analysis ( Getis-Ord-Gi ) highlighted ZIKV hotspots in the Midwest , Northeast , and Southeast regions during the first two bi-monthly periods; hotspots then shifted to the Northeast for the third and fourth bi-monthly periods . The fifth and sixth bi-monthly periods are marked by persisting hotspots in the Northeast , in addition to appearance of hotspots in the North , and the reemergence of those in the Midwest . In contrast to the varied locations of ZIKV incidence hotspots , those for microcephaly incidence varied less between the regions across all bi-monthly periods . From the third until sixth bi-monthly period , hotspots also appeared in the Midwest and North . The South and Southeast regions also both consistently remained coldspots of confirmed microcephaly across all bi-monthly periods ( Figs 3 and 4 ) . Bivariate Local Moran’s I analysis was performed , focusing on evidence of a possible spatial relation between the spread pattern of ZIKV and microcephaly ( Fig 5 ) . Considering the multiple time lag intervals adopted in the analysis , it was possible to identify an increasing wave in High ZIKV cluster areas becoming microcephaly High cluster areas across time . We are more interested in the High-Low/Low-High clusters in this representation . High-Low clusters ( light red ) represent areas with a high incidence of ZIKV surrounded by areas with low incidence of microcephaly , while Low-High areas are the inverse . From the results depicted in Fig 5 , we noticed High-Low clusters mostly in the Midwest region . These clusters highlight regions with high incidence of ZIKV and low values of microcephaly , considering the different time lags observed . Analyzing the simultaneous presence of High-High clusters in the Midwest region , the High-Low clusters ( light red ) have the potential to become High-High clusters . This finding highlights a relationship between an increase in ZIKV infection with a growth in microcephaly cases after two bi-monthly periods . Early in 2016 , the ZIKV epidemic was already decreasing in the Northeast region , but was followed up with an increase in microcephaly ( Low-High clusters ) . However , as we entered the 2016 epidemic year , the High clusters of ZIKV transitioned to the Midwest and North regions . The maps showing the association between the first bi-monthly period of ZIKV and the fifth and sixth bi-monthly periods of microcephaly demonstrate an increase of Low-High clusters in the Midwest and North regions , indicating that the microcephaly epidemic followed the distribution of the ZIKV infection . Additional analysis considering other bi-monthly periods as a starting point highlighted a similar growth pattern since a time lag of at least two months was observed ( Fig 5 ) .
From 2015–2016 , Brazil experienced an unprecedented epidemic of microcephaly that carried devastating social and economic costs . A better understanding of the association between ZIKV and microcephaly was necessary to prevent a pandemic . Despite the importance and relevance of health geography , there is a lack of literature employing geospatial methods to analyze ZIKV and microcephaly . This study is the first to conduct a spatial-temporal evaluation of the association between ZIKV and microcephaly . Through this new approach , it was possible to identify evidence of an increase in microcephaly incidence associated with ZIKV incidence in the Midwest region of Brazil . A potential link between ZIKV and microcephaly was first examined following reports of an abnormal rise in microcephaly incidence in Brazil’s Northeast region . This unexplained rise in microcephaly rates led public health authorities to begin epidemiological investigations . It was not until mid-2015 that suspicions regarding a link to ZIKV surfaced . This late identification of a possible cause carried implications for epidemiological surveillance . For most of 2015 , there was no attention given to the causal link between ZIKV and microcephaly and , as a result , no reliable registry of ZIKV incidence rates was maintained . ZIKV was only designated as a disease of compulsory notification on February 17 , 2016 [27] . Scientific evidence in support of the causal link later emerged in the beginning of 2016 [2 , 35 , 36] . In this context , it was not possible to analyze the spatial relationship between ZIKV and microcephaly in the early stages of the outbreak in the Northeast region . There were no data about ZIKV incidence before the 2016 microcephaly epidemic . After ZIKV was classified as a compulsory notification disease , more resources were invested toward more thorough and reliable reporting . Thus , data for all of 2016 is available at the municipal level . These improvements in disease surveillance facilitated research on the association between ZIKV and microcephaly spread patterns to identify those areas that are disproportionately affected and remain at an elevated risk . Our study contributes to these efforts and found a significant spatial pattern of association between both diseases . The spread of ZIKV showed higher rates of infections in the Midwest in early 2016 that diminished by the end of the year . Confirmed ZIKV patterns in the Northeast and Southeast are consistent with a previous study of the spatial distribution of dengue fever in Brazil from 2014 , one year before the Zika outbreak [37] . The Southeast , North , and Midwest regions experienced an increase in microcephaly incidence across 2016 . This trend is explained by the high incidence of ZIKV previously in those regions . Our findings reveal that ZIKV incidence is positively associated with an increase in microcephaly incidence in the same location . Spread patterns of ZIKV and microcephaly cases in Brazil in 2016 suggest that a high number of cases of ZIKV in the Midwest are associated with a high number of cases of microcephaly in the region after a certain time lag . Measuring this association assists in probabilistic forecasting; monitoring the incidence of ZIKV may help predict where there will be increased incidence of microcephaly . Our data support that the greatest burden of microcephaly could shift from the Northeast to other regions that reported a high volume of ZIKV during the beginning of 2016 . A similar finding was reported by De Oliveira et al [29] . Our findings are of importance to health care providers and managers in these regions who should anticipate a greater need for prenatal care and adjust protocols in light of new systematic public health data about both diseases . The establishment of a regular monitoring system informed by the methodologies defined in the present study is needed to further confirm if observed relationships are maintained over time . Best practices for pregnancy management during the ZIKV epidemic detail clinical manifestations of infection , endorse serological testing [38] depending on symptom presentation and timing of acute infection , and recommend routine ultrasounds before 24 weeks gestation [35] . Laboratory confirmation of ZIKV facilitates systematic efforts to estimate its prevalence and risk [39] . These practices need to be considered in the Midwest region for pregnancy management , as our findings suggest that this region will face an increase in microcephaly cases . Therefore , training actions for primary care professionals are recommended , as well as a revision of protocols related to pregnant women in areas at risk . The transmission of ZIKV and other arbovirus diseases through genus Aedes mosquitos places every region with a tropical climate in a position of risk . The European Centre for Disease Prevention and Control mapped and categorized patterns of ZIKV transmission globally [40] . Several countries in South and Central America , Africa , and portions of Oceania were categorized by the World Health Organization as regions with active circulation of ZIKV . Tracking the relationship and behavior of ZIKV and microcephaly geographically is essential to design and implement response strategies to avoid outbreaks of microcephaly and other neurological complications [41] . The observed reduction in ZIKV incidence in the country as a whole , in fact , requires additional explanation . The incidence pattern of ZIKV followed the same tendency of dengue and chikungunya in 2016; there were peaks in incidence during the first months of the year followed by a decrease [8] . This trend might be due to the rainy season in Brazil that lasted from November until the end of March for most of the country . The increase in the rainfall index contributes to the growth of Aedes mosquito breeding sites , producing a rise in diseases transmitted through this vector . Brazil has continental dimensions with different climatic characteristics between its regions , as well as historical regional inequalities related to access to basic sanitation services . Simultaneous access to water supply by general network , sanitary sewage by general or rainwater network , and direct or indirect collection of garbage are still unequal among the five Brazilian regions . Municipalities lacking adequate sanitation are subject to a higher risk of infestation by Aedes and are consequently exposed to a higher risk of dengue , chikungunya , and ZIKV [42] . Even with this hypothesis , there is not yet longitudinal data on ZIKV to attribute the decrease in incidence rates to seasonal events like the rainy season . Additional hypotheses are being tested to explain the decline in ZIKV cases , including a massive infection perspective leading to a lack of susceptible individuals [43] . As limitations of the present study , we can highlight the lack of laboratory confirmation for part of the cases considered . However , they meet the epidemiological case criteria . Another limitation was the impossibility of estimating the incidence rates of ZIKV infection only in women due to the absence of the gender variable in the database . Thus , the estimates refer to the overall rates including men and women . The presence of gender information , as well as other details , such as pregnancy status or week of pregnancy , could increase the surveillance capabilities of the present information . Additionally , this information carries the potential to better support the relationship among ZIKV and microcephaly . However , as the current evidence supports sexual transmission [38 , 44–47] and salivary transmission [48] , a high incidence in men increases the chances of infection in women . Thus , the overall incidence is a good estimator of the disease in the population . The fact that compulsory notification was only instituted in Brazilian health services in February 2016 may have led to an underreporting of both events ( ZIKV and microcephaly ) , but especially of the first . The detection of abnormal levels of microcephaly cases was the trigger event responsible for raising additional investigations . Only after several months was a clear relation between microcephaly and ZIKV established . Thus , ZIKV was not on the surveillance radar of Brazilian epidemiological authorities when the microcephaly cases peaked . Therefore , there is no solid information about ZIKV incidence before the first rise in microcephaly cases , limiting the possibility of additional investigations regarding the first outbreak of microcephaly . As a consequence , there was potential bias towards the null hypothesis in association estimates , i . e . , if all cases of ZIKV infection had been effectively reported , the associations found would have been even stronger . Our study helps clarify the spatial association of microcephaly incidence in neonates whose mothers were previously infected with ZIKV . However , doubts remain about a possible relationship between the time of infection in pregnancy and the severity of sequelae in the fetus , or whether the symptoms of microcephaly depend on virus titers in fluids but not at the time of infection [49] . We don’t know if co-infections like dengue and chikungunya play any role in the severity of microcephaly [50] . Little is known [51 , 52] about the consequences of co-infection events [50 , 53] . What are the mechanisms used to break placenta barriers ? What cells are involved in the pathogenesis of severe disease ? [54] It is imperative to establish Aedes aegypti control in the Americas and the rest of the world to prevent the spread of ZIKV to new areas [55] . Understanding these and other issues may contribute to plans to control new outbreaks of this or other variations of the virus .
|
The increasing evidence of a relationship between ZIKV in pregnant women and fetal congenital ZIKV syndrome with microcephaly has been reported in the literature over the last two years . Our findings suggest a spatial dependency between the diseases . Therefore , using the spatial pattern of ZIKV incidence to better understand risk areas for microcephaly may help the design of surveillance policies . Brazil had a large epidemic of ZIKV , leading to several important studies of the ZIKV outbreak and its association with microcephaly . This study used a geospatial analysis approach to examine the association between ZIKV and microcephaly in Brazilian regions . It was possible to highlight a spatial association between ZIKV and microcephaly considering a time lag between diseases . Brazilian regions with the highest incidences of microcephaly were the regions where the highest incidence of ZIKV occurred months before . This finding can help the organization and planning of health services to offer better screening actions dedicated to pregnant women in high-risk areas .
|
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2018
|
Zika virus infection and microcephaly: Evidence regarding geospatial associations
|
Genome-wide association studies ( GWAS ) have successfully identified loci associated with quantitative traits , such as blood lipids . Deep resequencing studies are being utilized to catalogue the allelic spectrum at GWAS loci . The goal of these studies is to identify causative variants and missing heritability , including heritability due to low frequency and rare alleles with large phenotypic impact . Whereas rare variant efforts have primarily focused on nonsynonymous coding variants , we hypothesized that noncoding variants in these loci are also functionally important . Using the HDL-C gene LIPG as an example , we explored the effect of regulatory variants identified through resequencing of subjects at HDL-C extremes on gene expression , protein levels , and phenotype . Resequencing a portion of the LIPG promoter and 5′ UTR in human subjects with extreme HDL-C , we identified several rare variants in individuals from both extremes . Luciferase reporter assays were used to measure the effect of these rare variants on LIPG expression . Variants conferring opposing effects on gene expression were enriched in opposite extremes of the phenotypic distribution . Minor alleles of a common regulatory haplotype and noncoding GWAS SNPs were associated with reduced plasma levels of the LIPG gene product endothelial lipase ( EL ) , consistent with its role in HDL-C catabolism . Additionally , we found that a common nonfunctional coding variant associated with HDL-C ( rs2000813 ) is in linkage disequilibrium with a 5′ UTR variant ( rs34474737 ) that decreases LIPG promoter activity . We attribute the gene regulatory role of rs34474737 to the observed association of the coding variant with plasma EL levels and HDL-C . Taken together , the findings show that both rare and common noncoding regulatory variants are important contributors to the allelic spectrum in complex trait loci .
Numerous studies have associated low levels of high density lipoprotein cholesterol ( HDL-C ) with an increased risk of developing coronary heart disease ( CHD ) [1] , [2] , [3] , [4] , [5] , [6] , [7] . HDL-C levels are approximately 50% heritable [8] . Genome-wide association studies ( GWAS ) for lipid traits have identified many genes previously associated with HDL metabolism and numerous novel loci [9] , [10] , [11] , [12] , [13] , [14] . However , the identification of the causal variants in these loci has proven difficult . Resequencing studies have not identified common coding variants that explain the associations . Such results may suggest that causal coding variants are rarer than anticipated [15] or lie in the gene regulatory regions . Furthermore , many of the variants identified by GWAS are embedded in gene deserts . Although a portion of these associated variants may tag less-common variants with strong phenotypic effects , some noncoding variants are likely to be causal themselves [16] . Nevertheless , combining the variation explained by all of the common variants identified to date leaves missing heritability [17] that may be explained , at least in part , by rare variants . Several HDL-C candidate genes , including those with known physiological relevance to HDL-C metabolism , have been characterized though targeted gene-resequencing approaches [18] . Through these studies , the exons of HDL-C candidate genes ( ABCAI , APOAI , LCAT ) [19] and other mechanistically implicated genes ( ANGPTL4 , LIPG ) [20] , [21] have been sequenced in individuals at the extremes of the HDL-C phenotypic distribution . Rare coding loss-of-function variants were shown to segregate with the phenotype in a manner consistent with the known physiological role of the gene product in increasing or decreasing HDL-C levels . Causality of the identified variants was shown through a combination of in vitro functional studies and computational methods . Because the occurrence of each rare variant was too low to test its association in our sequencing cohorts , individual variants in each phenotypic extreme were grouped together ( “collapsed” ) , and the total number of rare variants in the sequenced region was compared between cohorts . This method of rare variant association analysis , known as the cohort allelic sums test ( CAST ) [22] , [23] , has been instrumental in showing that rare loss-of-function variants modulate HDL-C levels in humans . However , few studies to date have utilized this approach to study rare regulatory variants , which do not always segregate with the phenotypic extremes of continuous traits as stringently as deleterious nonsynonymous variants . Additionally , the functional validation of identified variants in regulatory regions can be challenging , especially for unknown promoter or regulatory elements . In the last decade , several HDL-C candidate genes have been identified , including many with large regulatory regions implicated in association studies . These findings , combined with the fact that HDL-C exists as a continuously distributed trait , make HDL-C candidate genes well-suited for understanding how rare regulatory variants influence complex traits . One HDL-C candidate gene associated in GWAS is LIPG [9] , [10] , [11] , [12] , [13] , [24] , [25] , which encodes endothelial lipase ( EL ) , a conserved plasma phospholipase expressed from endothelial cells [26] , [27] . Compared to other plasma proteins , EL exhibits preferential HDL phospholipolysis activity in vitro [28] . Somatic overexpression of EL in mice causes a dose-dependent reduction in plasma HDL-C levels [29] , whereas targeted deletion of LIPG [30] or inhibition of EL using polyclonal antibodies [31] raises HDL-C levels in vivo . We recently identified rare loss-of-function coding variants in subjects with high HDL-C through a resequencing study of subjects at the extremes of the HDL-C phenotypic distribution [20] . Here , we expand our initial resequencing effort to include regulatory variations , thereby further characterizing the allelic spectrum of LIPG . Our findings show that both rare and common variations in regulatory regions of LIPG affect LIPG expression , plasma EL protein concentrations , and HDL-C levels .
We sequenced a portion of the promoter and the 5′ UTR ( 1755-bp immediately upstream of the transcription start site ) in 388 unrelated individuals . Of the sequenced individuals , 195 individuals had extremely high HDL-C levels ( ≥95th percentile; HHDL Sequencing Cohort ) and 193 had low HDL-C levels ( ≤25th percentile; LHDL Sequencing Cohort ) . A summary of the characteristics of the participants in the sequencing cohorts appears in Table 1 . Through this study , we identified a total of 22 rare and common LIPG regulatory variants in the region sequenced ( Figure 1 ) . 25 individuals from our sequencing cohorts harbored a rare variant ( minor allele frequency [MAF]<1% ) in the proximal promoter or 5′ UTR of LIPG . Of these 25 individuals , 16 were in the HHDL and 9 were in the LHDL Sequencing Cohort ( Table 2 ) . The main characteristics of each of these participants are summarized in Table S1 . Of the 17 individual rare LIPG regulatory mutations we identified , 10 were found only in individuals with high HDL-C , 5 occurred only in individuals with low HDL-C , and the remaining 2 occurred in individuals from both cohorts . We did not find a disproportionate frequency of rare regulatory variants between the HHDL and LHDL cohorts ( P = 0 . 2142 , Table 3 ) . We also searched for these variants in the 1000 Genomes Project database ( 451 participants; [32] ) and found that only the 2 variants present in both cohorts , −303 A>G and −324 A>G , occurred in individuals of the YRI ethnicity in this database ( MAF = 0 . 014 for −303 A>G , MAF = 0 . 024 for −324 A>G ) . Neither of these variants was present in 1000 Genomes Project participants of other ethnicities , nor were any of the other 15 variants present in any population from this study . To determine the functional significance of the identified variants in modulating LIPG promoter activity , variants were tested with a luciferase reporter assay in HUVECs , which endogenously express LIPG . A wild-type LIPG promoter construct corresponding to the sequenced portion of the LIPG promoter was constructed and tested against the promoter-less pGL3-basic construct . The WT LIPG promoter construct displayed approximately 31 . 9 times greater relative luciferase activity than the pGL3-basic construct ( Figure S1 ) . We tested promoter constructs corresponding to the rare LIPG variants . Four of the 10 rare variants found only in high HDL-C individuals displayed decreased promoter activity relative to the WT promoter construct ( Figure 2A ) . In contrast , 4 of the 5 rare variants found only in low HDL-C individuals displayed increased promoter activity ( Figure 2B ) . The remaining 6 variants identified in only in high HDL-C individuals and 1 variant identified only in low HDL-C individuals did not alter promoter activity relative to WT ( Figure S2A and S2B ) . One of the 2 rare regulatory variants found at both extremes ( −303 A>G ) caused increased promoter activity in vitro ( Figure S2C ) . Six individuals from the HHDL Sequencing Cohort had a rare regulatory variant decreasing LIPG expression in vitro , compared to no individuals from the LHDL Sequencing Cohort ( P = 0 . 0301 , Fisher's exact test , Table 3 ) . One individual from the HHDL Sequencing Cohort had a rare regulatory variant increasing promoter activity , compared with 7 individuals from the LHDL Sequencing Cohort ( P = 0 . 0364 , Table 3 ) . Next , we individually compared the number of individuals with functional rare regulatory variants identified in either sequencing cohort . We excluded the 2 regulatory mutations that were identified in individuals from both cohorts and reassessed the association of functional rare regulatory variants with the phenotypic extremes . Similar to the results obtained above , a significant excess of rare LIPG promoter variants causing decreased LIPG expression was found in individuals with high HDL-C ( P = 0 . 0301 , Table 3 ) , and an excess of rare variants causing increased promoter activity was found in individuals with low HDL-C ( P = 0 . 0297 , Table 3 ) . Notably , when we enriched for variants only present in either of the cohorts , no variants decreasing LIPG promoter activity in vitro were identified in individuals with low HDL-C . Likewise , no variants increasing promoter activity were present in individuals with high HDL-C . In addition to discovering novel , rare LIPG regulatory variants , our sequencing effort identified 5 common variants ( MAF≥5% ) , all of which were present in both high HDL-C and low HDL-C subjects ( Figure 1 and Table 4 ) . The minor alleles of 3 of the identified variants ( rs9959847 , −1495 T>C; rs4245232 , −1429 C>A; rs3829632 , −1309 A>G ) are in complete LD with each other and constitute a common haplotype . According to the International HapMap Project dataset [33] , this haplotype includes 3 additional SNPs upstream of the sequenced region ( rs4939583 , rs6507929 , rs4939875 ) and 2 intronic SNPs ( rs2000812 , rs3819166 ) ( Figure 1 , Table S2 , Figure S3 ) . We assessed the association of 2 of the identified common variants , −1309 A>G ( rs3829632 ) and −1358 ( T insertion ) , with HDL-C and other HDL traits in the Framingham Heart Study Offspring cohort ( FHS; 1089 subjects in this analysis , Table 5 ) . The −1309 A>G variant was used as a tag SNP for the haplotype . Although the −1358 ( T insertion ) variant had a borderline association with decreased HDL3 subfraction , the −1309 A>G variant ( and , thus , the entire haplotype ) was strongly associated with decreased HDL-C by approximately 2 mg/dL ( P<0 . 0002 ) . This latter variant was also associated with decreases in HDL3 , large HDL particles , apoA-I ( the major protein component of all HDL ) , and HDL size . Consistent with these findings , a recent GWAS of >100 , 000 individuals by the Global Lipids Genetics Consortium ( GLGC ) found that the minor alleles of several variants of this haplotype were strongly associated with a reduction in HDL-C ( Table S2 ) [13] . Neither the −1358 ( T insertion ) or −1309 A>G variants were associated with changes in any other lipid or lipoprotein measures in the FHS ( data not shown ) . Reporter constructs corresponding to the common LIPG regulatory variant rs34474737 ( 229 T>G ) and the −1358 T insertion variant , neither of which is known to be part of a haplotype extending beyond the LIPG promoter , were generated and used to test their impact on LIPG promoter activity in HUVECs ( Figure 3 ) . The rs34474737 variant caused a marked reduction in luciferase reporter activity ( P<0 . 01 vs . WT ) , whereas the −1358 ( T insertion ) variant , which was not strongly associated with modulation of HDL-C in FHS , did not significantly alter LIPG promoter activity . We hypothesized that the common LIPG regulatory variant rs34474737 , which decreases promoter activity in vitro , would cause decreased plasma levels of EL in human subjects . If true , this finding would provide a mechanism through which the identified variants could increase HDL-C levels in humans . We also assessed the role of 2 recently associated noncoding variants ( rs2156552 and rs4299883 ) and the common haplotype spanning the LIPG locus ( rs3829632 , −1309 A>G ) in the regulation of LIPG expression , by testing the effects of these variants on plasma EL . The EL concentrations were measured in participants of the SIRCA study who were genotyped for variants rs34474737 ( n = 761 ) , rs2156552 ( n = 570 ) , rs4299883 ( n = 755 ) , and rs3829632 ( n = 760 ) ( Table 6 ) . Minor alleles of the rs4299883 and rs2156552 variants were highly associated with decreased HDL-C in the GLGC GWAS ( P<10−44 and P<10−48 respectively ) [13] . We tested the association of these 2 variants with HDL-C and HDL subphenotypes in the FHS , and found that the minor alleles of these variants are associated with decreased HDL-C , HDL2 , HDL3 , and HDL particle sizes and apoA-I levels ( Table 5 ) . Consistent with these findings , the minor alleles of these variants were also associated with increased plasma EL ( P<0 . 002 and P<0 . 004 , respectively ) ( Table 6 ) . The minor allele of the −1309 A>G variant was moderately associated with increased plasma EL ( P<0 . 05 ) , consistent with its role in decreasing plasma HDL-C , as suggested by the GLGC and FHS association studies . The minor allele of the rs344747347 ( 229 T>G ) variant was highly associated with decreased plasma EL ( P<0 . 004 ) , consistent with the luciferase reporter assay results . Plasma EL concentrations were measured for individuals in SIRCA genotyped for the rs2000813 variant ( Thr111Ile; n = 761 ) . This common nonsynonymous variant does not alter EL lipolytic activity in vitro or in vivo [20] , but was associated with increased HDL-C in GLGC ( P = 1 . 92×10−14 ) . Plasma EL concentrations decreased with the minor allele of the Thr111Ile variant ( P<0 . 0008 , Table 6 ) . It may be that the Thr111Ile variant is in high LD with a regulatory variant that decreases EL expression , which would explain the decreased plasma EL of subjects with the Thr111Ile variant , as well as its association with HDL-C but normal lipolytic activity in GLGC . To test this possibility , using genotyping data for the common regulatory variants in SIRCA participants , we estimated their LD with Haploview software [34] . The rs34474737 ( 229 T>G ) and rs2000813 ( Thr111Ile ) variants were in high LD ( R2 = 0 . 8 ) ( Figure 4 ) .
Numerous methodologies have been described for statistically comparing the frequency differences of rare coding variants for a complex trait in cases and controls [22] . Some approaches assume that much of the heritability of complex traits arises from the combined presence of functionally important rare variants . These , which include CAST and combined multivariate and collapsing ( CMC ) method , collapse rare variants within a functional location ( e . g . , gene locus ) and compare the frequencies of the aggregate variants between cases and controls [36] , [37] . Other methods for evaluating rare , risk-conferring mutations include weighted sum methods that count both rare and common coding variants . These tests weight variants based on their frequency in controls [38] or are informed by computational prediction programs for assessing functionality [39] . Although these assessment methods demonstrate high statistical power , they are disadvantaged by their inclusion of both rare and common variants , as well as functional information that is largely inapplicable or unavailable for noncoding variants . Although the effects of rare missense variants are frequently deleterious with regard to protein structure and function , the effects of rare regulatory variants are less readily interpretable [23] , [36] . Such variants may cause increased or decreased gene expression , depending on their location; may act in a tissue-dependent manner , thereby weakening their association with complex traits; and may increase , decrease , or not affect transcription at all . Whereas nonfunctional coding variants can be predicted easily by synonymous or conservative amino acid substitutions , similar criteria cannot be applied to regulatory variants . We first used CAST to investigate the contribution of rare regulatory variants to HDL-C without computationally predicting their effects . The results showed no significant excess of rare regulatory variants in LIPG in either cohort . However , the strength of rare variant aggregation methods increases when the functional validity of the variants is known [22] , [23] , [40] , [41] . Therefore , we assessed the functional effects of each variant in a cell type that endogenously expresses LIPG ( HUVECs ) . These putative functional effects were used to reassess the association of functional variants in the 2 cohorts . Using a modification of CAST , we separately tested the associations of variants that increase or decrease LIPG promoter activity . The results showed that variants segregated with the phenotypic extremes in a manner that was almost completely consistent with the contribution of the gene to the phenotype . For example , given that EL inversely affects HDL-C levels , variants that decrease EL should cause increased HDL-C and should occur at a higher frequency in high HDL-C individuals , and vice versa . Including functional information in the association analysis permitted the near-perfect demonstration of this distribution . The only rare regulatory mutation inconsistent with the expected distribution was the −303 A>G variant , which increased LIPG promoter activity in vitro . This variant was found in 1 high and 2 low HDL-C individuals , which is the expected distribution , given its in vitro functionality . However , the high HDL-C individual with the −303 A>G variant also had another rare LIPG regulatory variant , −1487 A>G , which decreased promoter activity in vitro . Thus , the actual role of −303 A>G in contributing to high HDL-C levels must be considered in the context of the contribution from the additional rare variant in this individual . Previously , Hegele et al . presented an elegant approach of refining association tests by using exclusively presenting coding variants [42] . In the present study , this approach was modified for application to noncoding variants . We examined the association of variant types with the phenotypic extremes after eliminating variants occurring at both extremes . The results showed that promoter-activating or -damaging rare LIPG variants occurred only in individuals with high or low HDL-C , respectively . Thus , our analysis method effectively enriched for functional variants with the greatest potential effect at either extreme . A limitation of this approach is that the exclusivity of any rare variant depends on the selection criteria and sizes of the cohorts . Nevertheless , even without this selectivity filter , the expected enrichment of opposing regulatory variant types occurred at the opposite phenotypic extremes . The current literature contains additional rare variant association tests that evaluate the contribution of risk and protective rare variants to complex traits . One is a modified C-alpha score-test that measures the deviation of variance of each observed mutation from the expected variance with a binomial distribution . However , this method may not be valid for evaluating variants occurring only once in a test cohort , such as were identified in our study [43] . Another method , weighted sum test , calculates 2 one-sided statistics to quantify the association of variants in either phenotypic extreme . This test allows the incorporation of functional information of the identified variants and may be applicable to measuring the association of rare regulatory variants [44] . Yet neither of these methods is sufficiently robust to manage the large number of rare nonfunctional variants likely to be identified in resequencing studies of regulatory regions . In our study , nearly half of the rare variants identified in only one extreme failed to have any transcriptional effect . A recently reported modification of a previous methodology for studying common variants , the sequence kernel association test , may prove useful in studying the association of such rare variants without making any assumption of the functional direction or degree of effect of any individual variants [45] . Exploration of the LIPG noncoding regions revealed the contributions of common regulatory variants . For example , the 229 T>G ( rs34474737 ) variant in the 5′ UTR was found to decrease LIPG promoter activity in vitro and to raise plasma EL in humans . This variant was in LD with the common nonsynonymous variant Thr111Ile ( rs2000813 ) . Thr111Ile is a missense variant that does not damage EL function ( according to the PolyPhen prediction program ) and does not alter EL lipolytic activity in vitro or in vivo [20] . The association of Thr111lle with HDL is unclear , with some studies purporting a weak association with elevated HDL-C and others showing no association [46] , [47] , [48] , [49] , [50] , [51] , [52] . However , a recent GLGC GWAS metaanalysis of >100 , 000 individuals revealed significant association of this variant with increased plasma HDL-C ( P = 1 . 92×10−14 ) [13] , suggesting that Thr111Ile may be in LD with a regulatory variant . Based on the high LD between 229 T>G and Thr111Ile , as well as the association of plasma EL with minor alleles of the 229 T>G and Thr111Ile variants in SIRCA participants , we propose that the 229 T>G variant may cause the association of Thr111Ile with HDL-C by decreasing plasma EL . To our knowledge , this finding represents the first identification of a putative haplotype involving a causal regulatory variant and a functionally benign coding variant . The result also highlights the potential misattribution that can occur when nonsynonymous coding variants are considered to be highly suggestive of causal mutations , and regulatory variants are ignored . Interestingly , there are several reports of common nonsynonymous variants causing the association of noncoding variants in high LD with a phenotype . Kanda et al . reported that a common missense variant in high LD with a nearby promoter SNP at chromosome 10q26 independently explains the association of the locus with susceptibility to age-related macular degeneration [53] . A common functional missense variant in the B-cell scaffold protein BANK1 was shown to be in high LD with a common intronic variant that alters splicing , and both variants were strongly associated with systemic lupus erythematosus [54] . The functional heterogeneity of linked coding and noncoding SNPs highlights the complexity of haplotype structures , as well as the need to characterize the complete ( i . e . , coding and noncoding ) variation in candidate loci for complex traits . Indeed , resequencing studies to identify haplotypes in candidate genes for inflammation , lipid metabolism , and blood pressure regulation are susceptible to missing partial or whole haplotype blocks when only coding variation is considered [55] . Common regulatory variation in observed haplotypes for several complex traits may have profound functional significance . In our analysis of common regulatory variation in LIPG , we identified another haplotype with SNPs in the proximal promoter region . Three variants , −1495 T>C ( rs9958947 ) , −1429 C>A ( rs4245232 ) , and −1309 A>G ( rs3829632 ) , identified in the promoter region were in complete LD with each other . A study of HapMap data indicated that three SNPs upstream of the sequenced region ( rs4839583 , rs6507929 , and rs4939875 ) and SNPs in the second and fifth introns of LIPG ( rs2000812 and rs3819166 , respectively ) are also in high LD with these three promoter SNPs [33] . Because the region encompassed by this haplotype extends far upstream and within the LIPG gene ( approximately 34 . 1 kb from the most 5′ to most 3′ of the variant constituents of the haplotype ) , it is not possible to assess its full functional impact with a reporter driven by part of the LIPG promoter . Characterization of the effects of single variants of this haplotype on LIPG expression in vitro could lead to erroneous implications about their functional significance , because their aggregate ( and potentially synergistic ) effects on transcription would be ignored . Therefore , we evaluated the contribution of the combined haplotype by measuring its effect on HDL-C levels and plasma EL concentrations from human subjects . Minor alleles of the haplotype variants were associated with decreased HDL-C in the FHS and GLGC GWAS studies , and the minor allele of 1 variant was associated with increased plasma EL . Together , these findings implicate this haplotype in the reduction of human HDL-C . Association of minor alleles of the −1309 A>G , rs4939883 , and rs2156552 variants with decreased HDL-C ( P = 0 . 0002 , 2 . 28×10−7 , and 1 . 08×10−8 , respectively ) in the FHS was supported by a similar association with decreases in the HDL subphenotypes HDL2 and HDL3 . A recent GWAS of 17 nonconventional , NMR-assessed lipoprotein measures also identified association of the rs4938993 variant with apoA-I and large HDL particles under both fasting and nonfasting conditions [56] . Together , these results demonstrate the reproducibility of such measurements in association studies . Future lipid genetic association studies using nonstandard measurements may provide additional insights beyond aggregate lipoprotein measures . Finally , we evaluated 2 SNPs , rs2156552 and rs4299883 , which were recently reported in the GLGC GWAS metaanalysis to be highly associated with HDL-C . Both variants are 40–65 kb downstream of the LIPG gene and are in high LD with each other [20] , but not with Thr111Ile or Asn396Ser . In addition to being associated with decreased HDL-C , the minor alleles of these variants are associated with increased plasma EL in humans . We did not observe any LD with any of the common variants identified in our resequencing study . Further analysis of the regulatory region harboring these SNPs may help elucidate the mechanism by which these variants contribute to increased human LIPG expression . The molecular regulators of LIPG expression are largely unknown . Investigations of induced EL secretion from human endothelial cells upon cytokine treatment have suggested that LIPG is regulated in an NFκB-dependent manner [57] . Subsequent studies utilizing electrophoretic mobility shift assays , chromatin immunoprecipitation ( ChIP ) , and cotransfection experiments of luciferase reporter constructs determined that the LIPG promoter contains 2 NFκB binding sites , one of which ( position −1250 relative to the transcription start site ) exhibited strong NFκB binding in vitro [58] . In addition , ChIP combined with genome tiling arrays in HepG2 liver cell lines identified LIPG as a potential target of the SREBP1 transcription factor , a major regulator of cellular fatty acid synthesis and metabolism [59] . None of the promoter variants identified in this study disrupt the NFκB or SREBP1 binding sites . Further characterization of regulatory variants affecting LIPG expression may help elucidate key regulators of LIPG expression . In this study , we demonstrate that regulatory variants , both common and rare , causally contribute to an associated phenotype . Given the complexities of interpreting the functionality of noncoding variants , direct experimental evaluation may be required to assess their impact accurately . By expanding on previous statistical association methods , this study provides an example of how such an evaluation may be done . As future whole-genome sequencing efforts will undoubtedly uncover myriad causal regulatory mutations for several polygenic traits , the findings in this study should encourage the development of methodologies to assess the contribution of rare noncoding variants .
Written informed consent was obtained from all participants in the cohorts described . The UPenn Institutional Review Board ( IRB ) approved all study protocols . LIPG regulatory variants were identified in a discovery cohort of subjects selected from the extremes of the HDL-C phenotypic distribution in the following cohorts: University of Pennsylvania ( UPenn ) High HDL Cholesterol Study ( HHDL ) , UPenn Catheterization cohort ( PennCATH ) , Study of Inherited Risk of Coronary Atherosclerosis ( SIRCA ) , and Philadelphia Area Metabolic Syndrome Network ( PAMSyN ) . HHDL is a cross-sectional study of genetic factors contributing to elevated HDL-C levels . Individuals with elevated HDL-C ( >90th percentile for age and gender ) were identified by physician referrals or through the Hospital of the UPenn clinical laboratory . PennCATH is composed of consecutive subjects undergoing coronary angiography at UPenn Health System hospitals and has been previously described [60] . SIRCA is a cross-sectional study of factors associated with coronary artery calcification in asymptomatic subjects recruited on the basis of a family history of premature coronary artery disease . Study design and initial findings have been previously published [61] . PAMSyN is a cross-sectional study of individuals with varying numbers of metabolic syndrome criteria , from none to all 5 . High HDL participants and low HDL participants were chosen from these cohorts . HHDL Sequencing Cohort participants are subjects with elevated HDL-C ( ≥95th percentile ) for age and sex ( females , range 87–174 mg/dL; males , range 85–166 mg/dL ) . LHDL Sequencing Cohort participants are subjects with low HDL-C ( ≤25th percentile ) , excluding individuals with HDL-C <20 mg/dL to eliminate participants with likely monogenic disorders of lipoprotein metabolism , leading to reduced HDL-C concentration ( females , range 22–61 mg/dL; males , range 23–44 mg/dL ) . Approximately 92% of participants were Caucasian , while the remaining 8% were of African descent; 42% of the participants were males , which was representative of the overall demographics of the parent studies . In total , 195 high HDL participants and 193 low HDL participants were chosen for deep resequencing analysis of the LIPG promoter . The Framingham Heart Study ( FHS ) Offspring Cohort , consisting of 5124 participants who were offspring of the original cohort recruited in 1948 and the spouses of the offspring , was initiated in 1971 . Participants have been examined every 4 to 8 years . The examined genotypes were from a panel of 1778 unrelated individuals who provided blood samples for DNA extraction during the sixth examination cycle ( 1995–1998 ) . HDL measurements were available at up to 7 time points for each individual . The HDL mean from the available measures for each individual was used . HDL2 , HDL3 , HDL size , HDL subfractions , and apoA-I , measured at exam 4 , were determined as described previously [62] , [63] , [64] . The Institutional Review Board at Boston Medical Center approved the study , and all participants gave written informed consent . A 1755-bp region of the promoter region ( directly upstream of the transcription start site ) of LIPG was amplified using a polymerase chain reaction ( PCR ) -based strategy . Genomic DNA was isolated from peripheral blood leukocytes using Nucleon extraction and purification protocols ( Amersham ) . PCR reactions containing 200 ng of DNA template using Ready-to-Go PCR Beads ( Amersham ) were amplified in a final volume of 25 µL . The PCR program included denaturation at 95°C for 5 min , followed by 35 cycles ( 95°C for 1 min , 61 . 5°C for 30 s , and 72°C for 1 min ) , and extension at 72°C for 2 min . PCR products were purified with ExoSAP-IT ( USB , Cleveland , OH ) . Purified PCR products were analyzed via Sanger sequencing on an ABI sequencer with Big Dye ( Applied Biosystems ) terminator chemistry . Sequences were aligned and chromatograms viewed with Sequencher Version 4 . 8 ( Gene Codes ) software . Allelic variations were verified by inspecting chromatograms . Putative variants identified in the HHDL and LHDL Sequencing Cohorts were searched for in the 1000 Genomes Project database . Rare variants were those with <1% MAF in our sequencing cohorts , and common variants were those with ≥5% MAF in our sequencing cohorts . The −1309 A>G ( rs3829632 ) and −1358 ( T insertion ) variants were genotyped in participants of the FHS for association analysis with HDL-C and other HDL traits by using Taqman custom genotyping assays ( Applied Biosystems ) . For association of common variants with plasma EL concentration in SIRCA participants , genotyping was completed by using either Taqman custom genotyping assays ( for −1309 A>G [rs3829632] , 229 T>G [rs34474737] , and rs4299883 ) or the ITMAT-Broad-CARe ( IBC ) cardiovascular gene genotyping array [65] . DNA was diluted to 50 ng/µL , and genotyping was performed at the Center for Applied Genomics ( Children's Hospital of Pennsylvania ) following manufacturer specifications for amplification and hybridization to the IBC array ( HumanCVD beadchip , Illumina ) , as previously described [66] . A 2007-bp fragment consisting of the human LIPG promoter ( 1755-bp portion flanking the transcription start site ) and the 5′ untranslated region ( 252-bp ) was PCR-amplified from a human LIPG plasmid clone with PCR primers that introduced Kpn I and Xho I restriction sites at the 5′ and 3′ ends of the fragment , respectively . This amplified region was cloned into the pGL3-basic vector ( Promega ) with the Kpn I and Xho I restriction sites to generate a construct with wild-type LIPG promoter driving firefly luciferase expression and was confirmed by PCR . Mutagenesis of the wild-type LIPG promoter ( firefly luciferase ) construct to generate mutant constructs for each of the identified regulatory variants was achieved by using QuikChange Site-Directed Mutagenesis Kit ( Stratagene ) according to the manufacturer's directions with primer sequences available in Table S3 . Plasmids were sequenced after site-directed mutagenesis to confirm the changes and to rule out additional nonspecific changes . Clonetics human umbilical vein endothelial cells ( HUVEC , Lonza ) were cultured in Clonetics Endothelial Growth Medium ( EGM-2 , Lonza ) at 37°C , 5% ( v/v ) CO2 . In preparation for luciferase assays , HUVECs were passaged 3 times and plated ( 10 , 000 cells/well ) overnight in 96-well tissue culture grade black-and-white microplates ( Perkin-Elmer ) in EGM-2 . Cells were transfected by using 2 µg DNA/well ( LIPG promoter construct and pRL-SV40 in a 50∶1 ratio ) and Fugene HD transfection reagent ( Roche ) in a 1∶3 ratio of DNA to Fugene HD following the manufacturer's instructions . Cells were harvested at 36 h after transfection . Luciferase assays were performed with the Dual Luciferase Assay Kit ( Promega ) and a dual-injection microplate luminometer ( Orion Microplate Luminometer , Berthold Detection Systems ) . Each well was normalized by Renilla luciferase luminescence values . Normalized values were compared to wild-type LIPG promoter constructs transfected on the same plate . Each construct was transfected with 6 replicate wells for each experiment . Each construct was evaluated at least three times . The preheparin mass of EL was measured from the plasma of SIRCA study participants genotyped for some of the identified common variants and 2 GWAS-identified noncoding variants . Detailed methods of the EL sandwich ELISA have been reported previously [67] , [68] . Briefly , rabbit anti-human EL antibody was used to capture EL from diluted plasma samples , followed by incubation with biotin-conjugated rabbit anti-human EL antibody and streptavidin-horseradish peroxidase conjugate with O-phenylenediamine for detection . Analysis and comparison of promoter activity between wild-type and variant LIPG promoter constructs from the luciferase assays were conducted by using unpaired Student's t-tests ( P-values<0 . 05 were considered to be statistically significant ) . Numbers of individuals with a rare variant identified in each sequencing cohort were initially compared using 2-tailed Fisher's exact tests . Variants that did not alter promoter activity in vitro were discounted , and individuals harboring these variants were included in their respective sequencing cohort as individuals without a functionally altering variant . Numbers of individuals with variants decreasing promoter activity and with variants decreasing promoter activity in each sequencing cohort were then compared separately using 1-tailed Fisher's exact tests . The FHS association analysis was completed by performing multiple linear regressions of the residuals of lipid phenotypes , separately by gender , after adjustment for means of age , age2 , BMI , alcohol intake , and smoking status . In this analysis , for women , the proportion of exams that a woman was menopausal and on hormone replacement therapy was included as a covariate . For association of variant genotypes with effect on plasma EL in SIRCA , plasma EL concentrations were log-transformed to normalize the distribution and analyzed with linear regression . Linkage disequilibrium ( LD ) calculations and presentation were performed with Haploview software [34] .
|
Genetic association studies have identified genomic regions that affect quantifiable traits such as lipid levels . When a gene and a trait are found to be associated with one another , the gene is often further studied to determine its role in affecting the trait . One approach is to sequence the gene in individuals at the extremes of the trait's distribution with the hope of finding rare mutations that directly contribute to the trait . Until now studies using this approach have focused on genetic variation in the protein coding sequence of these genes and have been largely successful in identifying functionally important mutations . However , other studies have found an abundance of noncoding variation in the genome that may also contribute to the heritability of these traits . Here we seek to determine the contribution of such noncoding mutations to high density lipoprotein cholesterol ( HDL-C ) levels in humans using the HDL-C candidate gene LIPG as an example . Through a sequencing study in individuals with high and low HDL-C levels , we demonstrate that both rare and common noncoding mutations are influential contributors to the allelic spectrum of such traits and should be further characterized after initial association with the trait .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"biology"
] |
2011
|
Mining the LIPG Allelic Spectrum Reveals the Contribution of Rare and Common Regulatory Variants to HDL Cholesterol
|
As sessile organisms , plants have to cope with diverse environmental constraints that may vary through time and space , eventually leading to changes in the phenotype of populations through fixation of adaptive genetic variation . To fully comprehend the mechanisms of evolution and make sense of the extensive genotypic diversity currently revealed by new sequencing technologies , we are challenged with identifying the molecular basis of such adaptive variation . Here , we have identified a new variant of a molybdenum ( Mo ) transporter , MOT1 , which is causal for fitness changes under artificial conditions of both Mo-deficiency and Mo-toxicity and in which allelic variation among West-Asian populations is strictly correlated with the concentration of available Mo in native soils . In addition , this association is accompanied at different scales with patterns of polymorphisms that are not consistent with neutral evolution and show signs of diversifying selection . Resolving such a case of allelic heterogeneity helps explain species-wide phenotypic variation for Mo homeostasis and potentially reveals trade-off effects , a finding still rarely linked to fitness .
Some of the most important constraints that plants have to adapt to are those related to soil properties [1] , [2] . These are also possibly some of the least well studied constraints , because they are spatially heterogeneous , thus not prone to typical geographic clines [3] , [4] , and require analysis at the local/population scale [5] . In this context , quantitative genetics approaches hold great promise to reveal the genetic basis of adaptation by enabling the identification of the molecular origin of phenotypic differences between populations or even between species [6] , [7] . One of the benefits of identifying the causative polymorphism ( s ) and/or gene ( s ) explaining natural phenotypic variation is that it allows direct testing for correlations between environmental factors populations may be responding to and the occurrence of the target genetic polymorphism . This contrasts with working indirectly through populations phenotype , which may reflect contradictory patterns and trade-offs , if not genetic drift [5] , [8] . Moreover , this approach also enables direct testing for fitness advantages or potential cost of adaptation ( i . e . antagonistic pleiotropy , an expected argument for local adaptation ) , that may be masked by linkage to deleterious mutations or genetic drift in reciprocal transplant experiments [9] , [10] . Molecularly identified examples of potentially-adaptive variation are still largely lacking and the debate is open as to the scale and rate of adaptive evolution [11] , [12] . In this work , we aimed at identifying the molecular bases of natural variation in accumulation of an essential micronutrient and understanding the ecological significance of this diversity . We describe both the fitness trade-offs of this variation and its potential adaptive advantage in the environment , revealing a system that is unlikely to have remained neutral .
Bay-0 and Shahdara , two strains ( accessions ) derived from wild populations of Arabidopsis thaliana , show contrasted growth behavior when grown on acidic peatmoss substrate ( Figure 1 ) . Using a segregating population derived from the cross of these two accessions , we determined the Shahdara growth defect to be segregating from a major-effect recessive locus on chromosome 2 , as confirmed by a near-isogenic line derived from a residual heterozygous interval in one of the recombinant inbred lines ( HIF084; Figure 1 ) . Additional recombinant lines were phenotyped and genotyped to pinpoint the causative interval to 80 kb ( Figure S1 ) , covering 19 annotated genes . Among these , one gene appeared as a good functional candidate: MOT1 was previously linked to the transport and homeostasis of the essential micronutrient molybdenum ( Mo ) in the plant [13] , [14] , an element which availability is known to vary with soil pH [15] . Indeed , a T-DNA insertion mutant in the MOT1 gene ( mot1 . 1; Figure S1 ) shows a phenotype similar to Shahdara on peatmoss and –contrary to the Bay and Col alleles– the Sha allele is not able to restore wild-type growth when combined with the mutant allele in F1 hybrids ( Figure 2 ) . Moreover , we show that defective growth is complemented by either increasing soil pH with additional CaCO3 mixed to the peatmoss ( Figure S2 ) or increasing Mo availability ( without altering soil pH ) by adding Mo in the watering solution ( Figure 3 ) . Hence , genetic and chemical complementation shows that acidic soil pH is responsible for reducing Mo-bioavailability and that , combined with a defective allele at MOT1 , this results in the typical Mo-deficiency syndromes of reduced leaf Mo contents , strongly altered growth and development , necrosis [16] . These observations of a significant phenotypic consequence of variation at MOT1 provide a model for the potential adaptive significance of this variation that goes beyond the simple variation in Mo content revealed previously [13] , [14] . Although we find that Landsberg erecta ( Ler ) has a similar behaviour than Shahdara in our conditions ( Figure 3 ) , this defective allele ( MOT1Ler ) used initially to reveal the gene's activity [13] , [14] is functionally different from the MOT1Sha defective allele . MOT1Sha doesn't bear the promoter 53 bp-deletion as in Ler ( Figure S1 ) and in fact is not showing MOT1Ler-like transcriptional down-regulation compared to MOT1Bay or MOT1Col ( Figure S3 ) . Instead , MOT1Sha seems defined by a single amino-acid change in the protein relative to Bay-0 and Col-0 ( Figure S1 ) , strongly suggesting that MOT1Sha is hypofunctional . However , the MOT1 protein produced from the Sha allele is still able to increase Mo accumulation when heterologously expressed in yeast ( Figure S4 ) . We then genotyped a random worldwide sample of ∼300 accessions for the Sha-like amino-acid change and the Ler-like 53-bp deletion and find that these alleles are both present at intermediate frequencies ( 15–20% ) among the populations . Sequencing 102 of these accessions for the whole gene and promoter region revealed that the very conserved MOT1Sha haplotype is indeed clearly defined solely by the D104Y amino-acid change , while the MOT1Ler genotype is more complex and diverse ( Table S1 ) . All Sha-like and Ler-like accessions that have been phenotyped show that both MOT1Sha and MOT1Ler haplotypes are perfectly associated with defective growth under acidic soil conditions ( Table S1 ) and complementation crosses with five additional Sha-like accessions confirm allelism to mot1 . 1 ( Figure S5 ) . Taking into account this allelic heterogeneity now explains most of the species variation toward low-Mo contents revealed in previous work [13] ( http://www . ionomicshub . org/arabidopsis/ ) . This form of complexity –in addition to genetic heterogeneity– is probably more frequent than previously thought in many organisms and is likely to help explain part of the missing heritability [17] , [18] . Regarding MOT1 defective haplotypes , MOT1Sha is confined to ‘West-Asia’ ( including Russia ) with a high frequency among these populations ( Figure S6 ) and displays a very low polymorphism level ( πSha = 0 , 00016 ) in comparison to other haplotype clusters , including the worldwide-distributed MOT1Ler allele ( πLer = 0 , 0017; Table S1 ) . This may translate a recent and rapid expansion of the Sha allele through ‘West-Asia’ , which could be due to neutral processes such as gene surfing associated with post-glaciation recolonization events from Central Asia [19] . This may also witness local positive selection events in favour of the Sha allele . Indeed , patterns of nucleotide polymorphisms at MOT1 in the sample of 102 accessions strongly deviate from the expectation under the strict neutrality model , contrarily to two control loci , PI and COI1 ( Table S2 ) . Negative values of Tajima's D reveal an excess of rare alleles at MOT1 , suggesting the possible occurrence of at least one past selective sweep that has targeted this locus . Other well documented evolutionary processes such as population expansion after the last glaciation event [20] and population genetic structure [21] could also have contributed to the excess of rare alleles observed at the genome-wide level [22] , as well as at the MOT1 locus in A . thaliana . Nevertheless , the HKA and McDonald-Kreitman tests , which do not rely on the frequency spectrum , support the hypothesis of diversifying selection at the species level ( Tables S2 and S3 ) . The excess of within-species polymorphisms relatively to inter-specific divergence and the excess of non-synonymous polymorphisms observed at MOT1 may result from the selection of different haplotypes at the worldwide scale . Interestingly , this trend is also clear when considering only accessions from ‘West Asia’ , suggesting that the selection process could happen at different geographical scales . Our own documented collection of wild populations from diverse regions in ‘West-Asia’ allowed us to investigate potential relationships between MOT1 alleles and environmental parameters described precisely at the population site , especially soil properties . We saw no relationship with soil pH ( indeed , none of the described populations were facing acidic soil conditions ) , but there was an obvious trend for populations with the defective MOT1Sha allele to grow on soils with high water-extractable Mo content ( Figure 4 ) . This may indicate that the defective Sha allele is a protective response to Mo accumulation in environments with excess Mo . Indeed , under such conditions in the laboratory , we observe a strong decrease in fitness ( through the total number of seeds produced per plant ) in all genotypes ( Figure S7 ) , indicating that plants have to find the right balance between Mo deficiency and Mo toxicity , a trade-off that could be resolved partly through variation in function of the Mo transporter MOT1 . Moreover , we show that a defective MOT1 allele ( either mot1 . 1 or MOT1Sha ) is accompanied by a slightly increased average seed mass ( another component of fitness ) specifically under Mo-toxic conditions ( Figure S7 ) , the outcome of which is difficult to estimate in nature [23] . It is however worth noting that previous studies in A . thaliana have shown positive effects of increased seed size for example on subsequent root and shoot growth [24] or seedling survival under limiting conditions [25] . In summary , we have identified a new functional variant at MOT1 that contributes to explain most of the species' diversity in Mo homeostasis , and associated phenotypes that provide likely explanations for its non neutral evolution and its correlation to native soil . It is still a rare finding to be able to relate functional genetic variants to fitness or trade-off effects [26]–[28] , and even more to associate this variation to the environment [3] , [7] . Our work indicates that environmental parameters of importance , such as soil properties , may be heterogeneously distributed and therefore require local description [5] and study of local adaptation [9] , [11] , which is greatly facilitated by the identification of the causative locus .
All accessions used and the Bay-0×Shahdara RIL set [29] were obtained from Versailles Arabidopsis Stock Centre ( http://dbsgap . versailles . inra . fr/vnat/ ) . Heterogeneous Inbred Family ‘HIF084’ was derived from RIL084 ( segregating for the region of interest ) as previously described [30] . New collections of A . thaliana accessions were partly described previously [31] and are shown at http://www . inra . fr/vast/collections . htm . T-DNA insertion mutant mot1 . 1 corresponds to line SALK_118311 as described [13] , [14] . Genetic complementation tests were performed on F1 plants issued from the cross of diverse accessions to mot1 . 1 or its wild-type background . Acidic soil assays were performed on ‘Floratorf’ peatmoss ( Floragard , Germany ) mixed with CaCO3 ( 4 g per liter of dry peatmoss ) to maintain a soil pH∼5 , watered with classical nutrient solution and grown under typical long-day conditions at 20°C . Chemical complementations were achieved in the same condition but , either with 8 g CaCO3 per liter of peatmoss to reach a pH∼6 , or using a watering solution supplemented with 1 mM Na2MoO4 . Mo toxicity was tested on regular fertilised soil mix ( pH = 6 ) watered with nutrient solution supplemented with 7 mM Na2MoO4 , or not ( control ) . MOT1 sequencing , qPCR analysis of expression ( normalised against GAPDH and PP2A ) , functional characterisation in yeast were performed as previously described [13] . Extractable Mo was determined in soils by the method of Soltanpour and Schwab [32] using ICP-MS as the detector . A total of 102 A . thaliana accessions ( including 48 accessions known to maximize A . thaliana diversity [33] and 44 accessions from ‘West-Asia’ ) and 5 A . halleri accessions ( I-14 , I-16 , F-1 , PL-22 and TZC; obtained from H . Frérot at Univ . Lille [34] ) were sequenced at MOT1 ( including 1 kb upstream and 0 . 3 kb downstream for A . thaliana accessions ) and at two reference loci , COI1 ( At2g39940; 2 , 600 bp coding sequence ) and PI ( At5g20240; 2 , 150 bp coding sequence ) . Those genes were either used previously as reference or shown to have a neutral pattern of polymorphisms in A . thaliana [35] , [36] . Sequences were aligned using Codoncode Aligner v3 . 7 . 1 . and subsequent alignments were improved visually . Intraspecific analyses i . e . nucleotide diversity estimated by π [37] and θw [38] , and Tajima's D statistics [39] were calculated using DNAsp v5 . 10 . 01 on the whole region sequenced . Ten thousands coalescent simulations under the strict Wright-Fisher neutral model assuming no recombination and conditioning on S were performed to estimate statistical significance of Tajima's D . For interspecific analyses , the orthologous of MOT1 and of the two control loci in A . halleri were used . The McDonald-Kreitman test [40] was performed by using DNAsp v5 . 10 . 01 in order to test for possible excess or deficiency in replacement substitutions at MOT1 . Singletons were discarded for this analysis in order to reduce the contribution of slightly deleterious mutations ( expected at very low frequencies and unlikely to become fixed ) . Neutral index was calculated as previously described [41] . Divergence between A . thaliana and A . halleri , defined as the average number of nucleotide differences between populations per gene , was calculated using DNAsp v5 . 10 . 01 and used to perform HKA tests with the multilocus HKA program available from J . Hey laboratory ( http://genfaculty . rutgers . edu/hey/software ) .
|
Plants are studied for their ability to adapt to their environment and especially to the physical constraints to which they are subjected . It is expected that they evolve in promoting genetic variants favorable under their native conditions , which could lead to negative consequences in other conditions . One approach to study the mechanisms and dynamics of these adaptations is to discover genetic variants that control potentially adaptive traits , and to study directly these variants in wild populations to try to reveal their evolutionary trajectory . We have identified a new polymorphism in a gene coding for a transporter of molybdenum ( an essential micronutrient for the plant ) in Arabidopsis; we show that this variant has strong phenotypic consequences at the level of plant growth and reproductive value in specific conditions , and that it explains a lot of the species diversity for these traits . Especially , the variant is associated with a clear negative effect under molybdenum-deficient conditions ( caused by soil acidity ) and with a subtle positive effect under molybdenum-plethoric conditions . Interestingly , the landscape distribution of the variant is not random among Asian populations and correlates well with the availability of molybdenum in the soil at the precise location where the plants are growing in the wild .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetic",
"polymorphism",
"natural",
"selection",
"genetics",
"plant",
"genetics",
"population",
"genetics",
"biology",
"evolutionary",
"biology",
"molecular",
"genetics",
"evolutionary",
"genetics",
"genetics",
"and",
"genomics"
] |
2012
|
Allelic Heterogeneity and Trade-Off Shape Natural Variation for Response to Soil Micronutrient
|
There has been an explosion of research on host-associated microbial communities ( i . e . , microbiomes ) . Much of this research has focused on surveys of microbial diversities across a variety of host species , including humans , with a view to understanding how these microbiomes are distributed across space and time , and how they correlate with host health , disease , phenotype , physiology and ecology . Fewer studies have focused on how these microbiomes may have evolved . In this paper , we develop an agent-based framework to study the dynamics of microbiome evolution . Our framework incorporates neutral models of how hosts acquire their microbiomes , and how the environmental microbial community that is available to the hosts is assembled . Most importantly , our framework also incorporates a Wright-Fisher genealogical model of hosts , so that the dynamics of microbiome evolution is studied on an evolutionary timescale . Our results indicate that the extent of parental contribution to microbial availability from one generation to the next significantly impacts the diversity of microbiomes: the greater the parental contribution , the less diverse the microbiomes . In contrast , even when there is only a very small contribution from a constant environmental pool , microbial communities can remain highly diverse . Finally , we show that our models may be used to construct hypotheses about the types of processes that operate to assemble microbiomes over evolutionary time .
Our framework applies to a population of hosts and an available pool of microbial colonists . As a first step , we assume that hosts do not exert any preferences on the microbial taxa they acquire . Similarly , we assume that microbes do not interfere with host reproductive capacity , or the survivorship and reproductive success of other microbes in the community . As will become clear , the only indirect effects that influence microbial recruitment and persistence from one generation of hosts to the next are competition for space within hosts and the relative abundance of microbial taxa . Simply put , we assume that the ecological and evolutionary processes that operate on hosts and their microbiomes are neutral; in this regard , our framework is analogous to neutral theories in evolutionary biology [36 , 37] , ecology and biodiversity [34] . We expand on this analogy later , but for now , we note that neutral theories provide parsimonious accounts of the types of patterns that can emerge in complex systems , they serve as null models for statistical hypothesis tests , and they provide platforms upon which we may construct more elaborate representations of these same systems [38] . In this framework , hosts reproduce asexually in discrete generations , following a neutral Wright-Fisher process [39 , 40] , where each individual in a succeeding generation chooses a parent randomly from the preceding generation . Hence , with a population of hosts of constant size N , all asexual individuals will share a common ancestor after 2N generations , on average . In our models , asexual reproduction is a computational convenience , and can be replaced with sexual reproduction without changing the essential patterns that we observe . We model how hosts acquire their microbiomes in three ways ( Fig 1 ) . First , under a strict “parental-acquisition” ( PA ) process , all hosts acquire their microbial communities directly from their parents . Second , with strict “environmental-acquisition” ( EA ) , hosts acquire their microbiomes solely from the environment . Between these two extremes , we also allow a third “mixed-acquisition” ( MAx ) process , whereby hosts acquire some percentage , x% , of their microbiomes from their parents and ( 100-x ) % from the environment . MA0 is exactly equivalent to EA , and MA100 to PA; as such , EA and PA designate boundary conditions of the ecological processes that mediate microbial acquisition in hosts . It is worth pausing at this point to clarify what we mean when we say that hosts acquire their microbiomes from their “parents” or their “environments” . Our models do not explicitly take account of the life events–illness , infections , changes in environments or diets–of each host within a generation , nor does it consider microbial fluxes within the lifespan of each host . Instead , the microbial composition of each host is essentially measured as an aggregate over the single generation that the host exists . Consequently , when we quantify the percentage of microbes from parents and environment using , say , MA10 , we mean that over the life of the host , 90% of its microbes come from the environment and 10% from its parent . In our models , it is possible that the parental contribution happened in the first 10% of the host’s life , or it may be that over the entire lifespan of the host , there was an ongoing contribution by the parent that amounted to 10% of the microbial composition . Since we allow hosts to recruit microbes from an “environment” , we need to define how the microbial content of this environment is constituted . In simulations , we characterize microbial composition using a distribution of taxa’ relative abundances . We propose three processes that determine the composition of the pool of microbes available for recruitment . First , we assume that the environment has a microbial composition that remains fixed over time . For the “fixed environment” ( FE ) , all taxa are present in the environment throughout the simulation , and are available to every generation of hosts . The second process we propose involves a changing environmental microbial profile , whereby the relative abundance of each microbial taxon available to the hosts in a given generation , is an aggregate of their abundances from all hosts of the preceding generation . Under this “pooled-environment” ( PE ) , microbial composition is reflection of what was present in the parents of the current generation of hosts . A third , intermediate , process is a combination of the previous two “environments”: the environmental microbial pool available for recruitment contains a percentage , y% , from the parental pool of microbes , and ( 100-y ) % of microbes from the fixed environment . Under this “mixed environment” ( MEy ) , the proportion of contribution from host microbiomes is given by y . As with our acquisition models , ME0 and ME100 are equivalent to the boundaries FE and PE , respectively . Our framework allows us to combine different host-acquisition processes with different ways of constructing the pool of available microbes in the environment . Conceptually , each of these combinations is a particular neutral model , capturing some of the elements previously discussed in the literature . For instance , PA or MAx incorporate the phylogenetic dependencies that Yeoman et al [31] discuss , and EA x PE is equivalent to what Costello et al [30] call dispersal limitation , whereby the local host community influences microbial composition . It is worth noting that the combinations PA x ( FE , MEy , PE ) –read as “PA in combination with FE , with MEy or with PE”–will give identical results . This is because , in all cases , the environment contributes nothing to host microbial content ( see the first row in Fig 1 ) . We model the construction of the microbial community in each host by competitive random sampling with replacement . Under this process , each host allows only a fixed and limited number of microbes to populate its microbiome . If microbial acquisition occurs under EA , each host samples randomly from the available pool of taxa according to the relative abundance of each taxon in the environment . In the case of MAx , x% of microbes are selected from the parent and ( 100-x ) % from the environment . If hosts acquire their microbial taxa under PA , then all microbes are inherited from the hosts’ parents , although the relative abundance of each taxon fluctuates multinomially . By constructing microbial communities in this way , we allow stochastic factors and indirect competition to modify taxon composition within and between hosts , as proposed by Costello et al [30] . By simulating combinations of PA , MAx and EA against FE , MEy and PE forward in time over many host generations and over a range of conditions , we are able to recover data on the behavior of individual microbial taxa , as well as a variety of summary statistics , including the expected time it takes individual taxa to invade all hosts or go extinct in the host population , and the trajectories of microbial taxonomic richness ( measured simply as the total number of microbial taxa ) and microbial taxonomic evenness ( measuring the similarity in the frequency of each taxon ) , microbial diversity within hosts ( α-diversity ) , inter-host variation in microbial composition ( β-diversity ) and the aggregate microbial diversity from all hosts in the population ( γ-diversity ) . Here , we report only on the latter three measures of diversity .
Microbial diversity within the host population is a function of the proportion of microbes that parents contribute directly to offspring and the proportion they contribute to the environment . Fig 2 illustrates how population-level taxon abundances change under various combinations of these proportions . In our simulations , the distributions of taxon abundances under high levels of parental contributions are skewed , and may be approximated by commonly-applied distributions , including the log-normal distribution and the Dirichlet multinomial ( DM ) distribution [41] ( Fig 3; the DM distribution has the advantage of allowing α- , β- and γ-diversities to be simulated–see S2 Fig ) . The ability to recover skewed abundance distributions is interesting , because we begin our simulations with a uniform distribution of microbial taxa , and we retain this uniform distribution in the fixed environment throughout the evolutionary history of the host population . Consequently , the emergence of dominant and rare taxa is a consequence of repeated parental contributions either directly to the next generation of hosts or indirectly to the environment . In fact , all simulations in which there was complete parental acquisition of microbes ( i . e . , PA ) resulted in the loss of all but one microbial taxon in the host population . Similarly , when the environment was reconstituted each generation exclusively with microbes from the parents ( i . e . , PE ) , the same pattern was observed with only a single microbial taxon remaining . These result are consistent with predictions made under neutral models of community ecology [42] , and highlight the strong depressive effect of parental transmission , either directly from parent to offspring or via parental contributions to a local pool of microbes , on population-level microbiome diversity . With EA x FE , microbes are obtained randomly from a fixed environment that persists over the evolutionary history of the hosts; unsurprisingly , the host population retains all microbes found in the environment . Interestingly , when microbes are obtained both from parents and a fixed environment ( MA x FE ) , we still see the persistence of all or almost all microbes in the host population ( see Fig 2A and 2B , first column of each bar chart; ME ( 0 ) is equivalent to a fixed environment with no microbial contributions from parents ) . This is true even when the proportion of microbial taxa that an individual host acquires from the fixed environment at each generation is very small , on the order of 0 . 001 . Therefore , a very small contribution from a constant environmental source of microbes is sufficient to retain high levels of microbial diversity in the host population . Microbial diversities are frequently measured in three ways: α-diversity , β-diversity , and γ-diversity . Our simulations indicate that all three measures depend on the percentage of parental contribution to offspring microbiomes and the composition of the environmental microbial pool ( see S1–S3 Tables for simulation means and standard deviations ) . Under our neutral model , in which the absence of host sub-population structure means that all hosts sample their microbes from the same environment , α- and γ-diversities remain high , and β-diversity remains low , for a large part of the range of direct or indirect parental contributions ( i . e . , to offspring or to the environment , respectively ) . Nonetheless , at high values of parental contributions , there are discernible differences in diversities , and we have also focused our simulations in these areas ( Fig 4; see S4–S6 Tables for simulation means and standard deviations ) . In general , α-diversity ( average diversity within hosts ) and γ-diversity ( overall diversity within the entire population of hosts ) increase as we increase the fixed environmental contribution because a fixed environment helps maintain a uniform distribution of taxon abundances and delays the loss of microbial taxa during evolution . Conversely , when hosts acquire increasing proportions of their microbiomes from their parents directly , or indirectly from a pooled environment , the variation of taxon abundance increases and taxon richness tends to decrease , thus lowering both α- and γ-diversities ( Fig 4A and 4B; S1 , S2 , S4 and S5 Tables ) . Inter-host variation in microbial composition , or β-diversity , also depends on the degree of parental inheritance , and the ratio of fixed-to-pooled environmental components ( Fig 4C; S3 and S6 Tables ) . Under the combination of PA x ( FE , ME or PE ) , β-diversity tends to zero , because all hosts descend from a single common ancestor and , as noted above , only a single microbial taxon remains in all hosts . When we have the parental microbiome as the only source of microbiomes in the next generation , ultimately , all lineages will have acquired their microbiomes from the most recent common ancestor ( MRCA ) of the population of hosts . Additionally , from one generation to the next , stochastic sampling of microbes over evolutionary time will result in the loss of all but one microbial taxon . Interestingly , with a high percentage of environmental acquisition , β-diversity is also relatively low , because all hosts acquire a large proportion of their microbial taxa from the same environmental pool , and consequently , will tend to acquire the same set of taxa . As noted above , the highest β-diversity occurs in a relatively narrow range of values of pure parental acquisition ( between 87–99% of direct parental transmission; S6 Table ) . If we focus on the relationship between β-diversity and the environmental pool , we see that its behavior is similar to that of α- and γ-diversities: it decreases as we increase the pooled environmental contribution to offspring microbiome . This is because a pooled environment , with contributions from the parental generation , tends to give rise to a non-uniform distribution of microbial taxa . As the degree of parental contribution increases , the environmental community will be dominated by few highly abundant species which are likely shared by most or all hosts within the population , accounting for high between-host similiarity in microbial composition ( Fig 4C; S3 and S6 Tables ) . It is important to note that our simulations have not been performed with inference or prediction in mind: the number of hosts , the number of microbes , and the number of taxa in our simulations are not necessarily equivalent to those of real-world microbial communities and their hosts , nor have we necessarily chosen the appropriate diversity indices or taxonomic resolution to optimize prediction/inference . Nonetheless , it is helpful to examine how the simulated values of diversity compare to empirical observations , and what these comparisons might tell us about the evolutionary processes that are acting on microbiomes . As an example , we used genus-level taxonomic data from the NIH Human Microbiome Project ( HMP ) [43] , specifically , a table of relative abundance found in different compartments of the human body ( http://www . hmpdacc . org/HMSMCP/; see S1 Dataset ) . Several large samples from the anterior nares , vaginal posterior fornix , stool , buccal mucosa , tongue dorsum and supragingival plaque were chosen to calculate α- , β- and γ-diversities on genus level ( Table 1 ) . Values of α- and γ-diversity obtained from all sampled sites of the human microbiome are low , in comparison to most of the values we obtained in our simulations . In fact , human microbiome diversities are generally lower than those of other non-human primates [44 , 45] . If we compare the empirical diversities to those obtained in our simulations ( S1–S6 Tables ) , we would have to posit very high parental contributions , both direct and indirect ( >90% ) , to account for the α- and γ-diversities across all human body sites . In contrast , values of β-diversity appear to provide a little more discrimination amongst body sites: the site with the lowest β-diversity is the vaginal posterior fornix , and its value is consistent with a very low degree of direct parental contribution in our simulations ( approximately between 0–15% ) . The β-diversities at other sites appear to suggest higher levels of parental contribution ( again , >90% ) . In the next section , we discuss the implications of these results , as they relate to human microbiome evolution and how the neutral model may be used to construct hypotheses about relevant evolutionary and ecological processes .
In this paper , we introduce a simple and flexible framework to model the evolution of microbiomes within a population of hosts , which takes account of different modes of microbiome acquisition and environmental microbial composition . Under our neutral model , microbiome composition is affected by sampling effects . Stochastic changes in microbial abundances may affect the persistence of microbial taxa in the microbiome over one or a few generations ( i . e . , ecological drift ) , or over many generations . The latter may occur because host lineages die out; when this happens , changes in microbial abundance across the whole population of hosts are essentially equivalent to changes in allele frequencies ( i . e . , genetic drift ) . The constitution of the microbial community in the environment also plays a considerable role in determining the ultimate fate of microbial taxa within host microbiomes . With a fixed environment , when there is a constant pool of the same microbial taxa from one generation of hosts to the next , microbial taxa never go extinct from the host population as long as hosts obtain some fraction of their microbiome from the environment . This is true , even when that fraction that the environment contributes to each host’s microbiome is very small ( e . g . , 0 . 1% ) . In contrast , when the environmental composition of microbes reflects the microbial content of the hosts in previous generations ( i . e . , PE , the “pooled” environment in our model ) , microbial diversity of the environment shrinks , as does the diversity of host microbiomes . Therefore , the extent to which parents contribute to the microbiomes of their offspring ( either directly or through their contributions to the pooled environment ) plays a crucial role in shaping microbiome diversity and constitution . In our simulations , values of α- and γ- diversities are at their lowest when parental contribution to the microbiome is high . Inevitably , microbial taxa are lost from the population as host lineages are lost . Thus , under our neutral model , it is possible to recover skewed microbial abundance distributions reminiscent of those obtained with real data , despite a fixed environmental component that remains uniform and constant throughout our simulations . Increasing skewness–essentially , decreasing eveness–is obtained as we increase the degree of parental inheritance . Of course , we don’t claim any deep insight here: no one should be surprised that we are able to recover skewed abundance distributions with our models , because there is a large body of literature on the mechanisms–both neutral or otherwise–that may lead to the emergence of skewed abundance distributions ( see [46] for an excellent synthesis ) . Our results reinforce what others [34 , 47 , 48] have found , by adding yet another neutral mechanism to account for the emergence of skewed abundance distributions . Our framework includes sampling effects on an undivided host population , which evolves under a Wright-Fisher process . Consequently , our models have some points of similarity with those that have been developed in population genetics . For instance , Orive et al [49] analyze the evolutionary dynamics of endosymbionts using a discrete-time Moran population genetic model . In their model , endosymbionts are acquired either vertically , passed on from parent to offspring , or horizontally from the environment . This corresponds to our MA x FE model and , in agreement with our results , Orive et al find that increasing the environmental contribution of endosymbionts to host cells results in greater diversity within cells and less diversity between cells . Our models do not include any mutational process or speciation acting on the microbes , as time moves forward . In reality , of course , microbes acquire mutations in their genomes at a rapid rate , but the measures of diversity we use in our analyses capture differences in taxonomic composition , not genetic diversity . In our models , it is implied that no cladogenetic events have occurred over the course of the simulations . The models presented here provide an opportunity to construct hypotheses , and make qualitative predictions , about the patterns of diversity we can expect to find in different biological situations . For example , the effects of “pure” pooled versus fixed environments on microbiomes can be found in a comparison of social and solitary bees . Social bees exhibit behaviors that are likely to result in the transmission of microbes from a microbial pool within the colony [50] . In contrast , solitary bees acquire their microbiomes from the environment , through feeding or burrowing . Our model would predict that social bees would have lower α-diversity and lower taxonomic richness than solitary bees . This is consistent with the results obtained by Martinson et al [51] who surveyed the microbiomes of eusocial bee species Apis spp . and Bombus spp . and non-social bees ( 11 species ) and wasps ( 3 species ) : they found depauperate microbiomes in social bees compared to non-social bees . Whereas it is reassuring to obtain empirical corroboration for our models , arguably neutral models are most useful when real-world observations run counter to predicted outcomes . Falsification of neutral models provides a justification for augmenting these models to include additional processes that account for the phenomena under study . In this regard , our analysis of the Human Microbiome Project data is instructive . As we have noted above , our simulations should not be used for inference , and we should be cautious about reading too much into the comparisons between empirical and simulated patterns of diversity . Nonetheless , at least for some sites , i . e . , the stool , the tongue dorsum , the supragingival plaque , the anterior nares and the buccal mucosa , the low empirical values of α- and γ- diversities appear to point consistently to a high level of parental inheritance when compared against values obtained in our simulations . There is evidence that the human microbiomes at various sites are seeded at birth by the mother , particularly if this birth is through the vaginal tract [52] . There is also reasonably strong evidence that families share microbes to a greater extent than unrelated individuals in a population [53] , and at least in some human populations , mothers share more microbes in common with their offspring than with unrelated children [54] . It is not clear , based on the studies that have been done to date [55] , whether the values of direct or indirect parental contribution we obtain when we compare empirical and simulated diversities are significantly higher than would be obtained in real populations , but we expect that the intuition of mosts microbial ecologists is that percentages of direct and pooled parental contributions > 90% are likely to be too high . Putting to one side the caveats about inference , we accept that while this intuition does not constitute evidence against the neutral model , it is likely to engender scepticism about the model’s correctness . If it is , in fact , true that direct or indirect parental contributions to the next generation’s microbiomes are not as high as our simulations suggest , how do we account for the apparent depression in α- and γ-diversities , and elevation of β-diversities at these sites ? One hypothesis that explains low α- and γ-diversities , and high β-diversities , and does not require the action of non-neutral processes , is the existence of local host subpopulations . The existence of subpopulations of hosts , with limited immigration and sharing of microbes between subpopulations , is likely to give the appearance of high parental contribution from one generation to the next . Certainly , this is a plausible explanation for patterns of microbiome diversity in the oral cavity ( i . e . , the buccal mucosa , tongue dorsum and supragingival plaque ) and stool samples , because of the likely influence of familial [53] or cultural dietary preferences/practices [56] or lifestyles [57] on these microbiomes . A similarly explanation may account for patterns of microbiome diversity of the anterior nares . The vaginal posterior fornix presents an interesting contrast to the other body sites because the α- and γ-diversities suggest high parental contributions ( although they cannot distinguish between direct or indirect contributions ) , whereas β-diversity suggests a low direct parental contribution . This inconsistency may again cause us to reject the neutral model in favor of an alternative explanation , but in this case , subpopulation structure may play a minor role relative to selection for a vaginal microbial community that is common amongst hosts . Such a selective filter is likely a consequence of a complex suite of factors including host immune defences , hormonal cycling , pregnancy , and the presence of apparently beneficial microbial species ( e . g . , Lactobacillus spp . ) [58] . This hypothesis explains both the high level of α- and γ-diversity ( i . e . , a few abundant species with many rare species ) , and the low β-diversity . For the human microbiome , neutral models have the potential to help identify additional processes that may account for patterns of diversity . As noted , of the two processes identified above–host subpopulations and selective filters–the former still remains part of an underlying neutral process , and a plausible extension to the neutral framework presented here . Rejection of a simple neutral model therefore allows us to identify incremental additions that may increase explanatory power . Another example of empirical data that appears to contradict the expectations of our models is the comparison of microbiome diversities in high microbial abundance ( HMA ) and low microbial abundance ( LMA ) sponges [59] . HMA sponges have large numbers of associated microbes , in contrast to LMA sponges . Additionally , researchers have shown that microbial diversity in LMA sponges is lower than that of HMA sponges [60 , 61] . Based on our results , we would predict that there is a greater degree of vertical transmission in LMA sponges , but it turns out that this is not the case: Schmitt et al [62] have found that “vertical transmission , as a mechanism to obtain bacteria , seems to occur mainly in HMA sponges” . Giles et al [60] propose two possible reasons to account for the low diversity in LMA sponges . First , there may be selective filters that permit only certain microbial taxa to colonize the sponges; second , the initial colonization event is stochastic , but serves to constrain or exclude successive colonizations . As with the human microbiome data , the sponge example is important because it does not rely a priori on non-neutral processes to account for the low diversity in LMA sponges; instead , selection ( or other ecological and/or evolutionary processes ) is invoked only after it is shown that vertical transmission in LMA sponges is unlikely , thus indicating that our neutral models are an inadequate explanation for the observed data . Riffing on the theme that “Essentially , all models are wrong , but some models are useful” [63] , Hubbell , writing about models in community ecology , says “Probably no ecologist in the world with even a modicum of field experience would seriously question the existence of niche differences among competing species on the same trophic level” [64] . But , he continues , “[Neutral theory] begins with the simplest possible hypothesis one can think of … and then adds complexity back into the theory only as absolutely required to obtain satisfactory agreement with the data” . We agree with Hubbell: to paraphrase , given what we know about the interplay between hosts , their microbial communities , and the environment , we would hesitate to put money on the table and bet that many microbiomes have evolved under the simplest neutral models that we have constructed here . But we would be equally hesitant betting in favor of the null hypotheses evaluated in statistical tests of significance . The value of these hypotheses resides not in their rightness or wrongness but in their ability to protect against overconfidence in our favorite , more complex model . Whereas it is true that biological processes are frequently complex , Occam’s Razor dictates that we construct as simple explanations ( or models ) as possible . In this way , we remain vigilant against the addition of unnecessary and unjustifiable complexity . Much as we do with statistical hypothesis tests , we accept stronger alternative explanations only when we are sufficiently confident that our neutral hypotheses are unlikely . This is not to say that neutral models only serve as strawmen; in molecular evolution , for instance , neutral models are frequently effective at explaining molecular variation [65] . And even in cases when the assumption of neutrality is questionable , the use of neutral models of substitution applied in molecular phylogenetics does not appear to jeopardize the accuracy of tree reconstruction [66] . Consequently , without taking account of the evolutionary processes of mutation , speciation , selection or recombination , or the ecological processes that operate in the context of spatial , environmental , and temporal heterogeneity , what we have developed is a framework on which we can begin to evaluate empirical patterns of diversity , and where necessary , add more elaborate ecological and evolutionary scenarios . We believe that even this simple framework , devoid as it is of all the embellishments afforded by evolution and ecology , can serve a useful purpose: it is a suitable staging ground on which we can construct null models of microbiome diversity in populations of hosts and it allows us to make strong , testable predictions .
Simulated host populations consisted of a fixed number of virtual host individuals ( N = 500 ) . Each host was allocated a virtual microbiome with a limited capacity or "slots" of microbes ( n = 1000 ) . The environmental pool consisted of 150 microbial taxa . Large number of hosts ( N = 2000 ) , microbes ( n = 100000 ) per host and microbial taxa ( m = 500 ) were also simulated with our neutral model , and similar patterns of diversity were observed ( S1 Fig ) . The microbiomes of the initial generation of host individuals were seeded randomly , with bacteria sampled from a uniform distribution of taxon abundances . We used an initial uniform distribution of taxa because we wanted to ascertain whether the equilibrium distribution of abundances obtained at the conclusion of our simulations would recover patterns seen in natural microbiomes . For each subsequent generation , the microbiome of each individual host was simulated by populating each of the available "slots" in the individual's microbiome by sampling microbial taxa with replacement ( multinomial choice ) from either the environment ( with probability given ( 1-x ) ) or from the microbiome of a "parent" host individual ( selected with uniform random probability from the population of the previous generation ) . When sampling from a parental/environmental microbial community , the probability that the new host microbiome will acquire a particular microbial taxon is given by the relative abundance of that taxon within the community ( see below for details on how environmental microbial taxon abundances were calculated ) . The probability , x , that a particular "slot" in a new individual host's microbiome was occupied by a microbial taxon sampled from a randomly selected parent was varied across simulations . Two sets of simulations were performed: ( 1 ) x and y varied linearly , between 0 and 1 , with increments of 0 . 1 ( see S1–S3 Tables for means and standard deviations of diversities ) ; and ( 2 ) with values of x , y ∈ ( 0 . 0 , 0 . 5[0 , 1 , 2…10] ) ( see S4–S6 Tables for diversities ) . When x = 0 . 0 , a host’s microbiome was sampled directly from the environment , i . e . , the probability of a microbial taxon being selected was equal to the relative frequency of the microbial taxon in the environment . When x = 0 . 50 = 1 , a host's microbiome was sampled multinomially from the microbiome of its "parent" who was selected with uniform random probability from the previous generation . Similarly , when y = 0 . 0 , there is no contribution from the previous host generation to the environmental microbial resource , whereas when y = 0 . 50 , all microbes are replaced each generation by the pool of microbes resident in the hosts of the previous generation . Three different models were used to model the relative abundance of taxa in the environment . First , for FE ( "fixed environment" ) , the abundances of the microbial taxa in the environment were fixed to the initial uniform distribution and did not vary over the course of the simulation . Second , under PE ( "pooled environment" ) the abundances of the microbial taxa in the environment were composed of the pooled microbiomes of all the hosts of the previous generation , i . e . , by summing over the abundances of the respective microbial taxa in hosts’ microbiomes , and renormalizing to relative abundances . Third , under MEy ( “mixed environment” ) , the abundances of the microbial taxa in the environment were calculated by combining the fixed environment and pooled environment with y% derived from the pooled environmental component . A particular simulation regime consisted of a distinct combination of pooled/fixed environmental ratios and environmental factors . A total of ten replicates were run under each simulation regime . The plots of γ-diversity were inspected , and simulations were halted when these stabilized . The number of generations for each simulation varied between 104 and 106 generations . After each generation was simulated , the diversities of microbiomes are measured by scaled Shannon-Wiener index ( α-diversity and γ-diversity ) or Bray-Curtis dissimilarity index ( β-diversity ) . The scaled Shannon-Wiener index is calculated as −∑i=1Rpiln ( pi ) lnR , where R represents the total number of taxa and pi represents the relative abundance of ith taxon within the community . The calculation of the Bray-Curtis index is given by the formula: 2 ( n−1 ) n*∑i=2n∑j=1i−1∑k=1m|pik−pjk|2 , where n represents the total number of hosts in the population , m represents the total number of microbial taxa within the host population , and pik and pjk represents the relative abundance of kth taxon within the community of host i and host j . Empirical data from the human microbiome were obtained from the website of the NIH Human Microbiome Project ( http://www . hmpdacc . org/HMSMCP/ ) . The original community profiling data is a table of relative abundances for each of 690 samples and 718 taxa of bacteria and archaea ( from 2 kingdoms to 397 species ) . As is described on the HMP website , all the samples were collected from 16 body sites from 103 healthy humans and processed with Whole Genome Shotgun sequencing . Specific information of each sample is available on the website ( http://www . hmpdacc . org/HMIWGS/all/ ) . We selected data of microbial communities associated with anterior nares , buccal mucosa , supragingival plaque , stool , tongue dorsum and vaginal posterior fornix because of their large numbers of samples , and removed the replicated samples from the same human subject for each body site ( see S1 Dataset ) . The HMP data provides relative abundances for different taxonomic levels . We calculated diversities for all taxonomic levels ranging from species to kingdom , and found that the values of diversities for genus , family and order were similar . Consequently , we chose to use genus-level diversities . The relative abundances of genera for each site and all samples is given in S1 Dataset . Fitting simulated results into log-normal and Dirichlet-multinomial distributions was performed in R with methods “fitdistr” of package MASS and “dirmult” of package dirmult . All simulations were carried out using Python scripts and Java programs , available from https://github . com/qz28/microbiosima . git Java code: https://github . com/qz28/microbiosima/tree/master/java Python scripts: https://github . com/qz28/microbiosima/tree/master/python
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Microbial communities associated with animals and plants ( i . e . , microbiomes ) are implicated in the day-to-day functioning of their hosts . However , we do not yet know how these host-microbiome associations evolve . In this paper , we develop a computational framework for modelling the evolution of microbiomes . The models we use are neutral , and assume that microbes have no effect on the reproductive success of the hosts . Therefore , the patterns of microbiome diversity that we obtain in our simulations require a minimal set of assumptions relating to how microbes are acquired and how they are assembled in the environment . Despite the simplicity of our models , they help us understand the patterns seen in empirical data , and they allow us to build more complex hypotheses of host-microbe dynamics .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
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Neutral Models of Microbiome Evolution
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Cortical topography can be remapped as a consequence of sensory deprivation , suggesting that cortical circuits are continually modified by experience . To see the effect of altered sensory experience on specific components of cortical circuits , we imaged neurons , labeled with a genetically modified adeno-associated virus , in the intact mouse somatosensory cortex before and after whisker plucking . Following whisker plucking we observed massive and rapid reorganization of the axons of both excitatory and inhibitory neurons , accompanied by a transient increase in bouton density . For horizontally projecting axons of excitatory neurons there was a net increase in axonal projections from the non-deprived whisker barrel columns into the deprived barrel columns . The axon collaterals of inhibitory neurons located in the deprived whisker barrel columns retracted in the vicinity of their somata and sprouted long-range projections beyond their normal reach towards the non-deprived whisker barrel columns . These results suggest that alterations in the balance of excitation and inhibition in deprived and non-deprived barrel columns underlie the topographic remapping associated with sensory deprivation .
The adult cortex adapts to alterations in sensory experience . This experience-dependent plasticity is evidenced by the functional reorganization of the primary sensory maps of the brain [1]–[12] , synaptogenesis in the adult brain [13]–[16] , and reorganization of dendrites [17] , [18] . Detailed knowledge of the structural rewiring of cortical circuitry following sensory loss provides insight into the operation of cortical circuits: which circuits are involved in specific functions , how they can be altered by sensory deprivation and learning , and how they reorganize following nervous system damage ( e . g . , retinal lesions , stroke , neurodegenerative disease or amputation ) . With the availability of genetically engineered viruses and two-photon microscopy , we are able to examine , in the living animal , how cortical circuits are modified by sensory experience . Cortical reorganization may be mediated through both excitatory and inhibitory connections . Pyramidal neurons in the superficial cortical layers form long-range horizontally projecting axons , which undergo a process of sprouting and synaptogenesis that parallels the functional reorganization of the cortex following altered sensory experience [6] , [13] , [14] , [19] , [20] . While the role of these excitatory cells in cortical reorganization has received the most attention , inhibition is also known to regulate plasticity during the critical period in early postnatal development [21] . Locally projecting inhibitory interneurons , also present in layer 2/3 , comprise 20%–25% of all cortical neurons [22] , [23] . The presence of inhibitory responses establishes the beginning of the critical period [24] , [25] , while the end of the critical period is dependent on inhibitory interneuron maturation [26] . Given its role in regulating plasticity during development , inhibition may also play a critical part in experience-dependent plasticity in the adult . A reduction in inhibition could unmask a network of normally subthreshold horizontal connections , driving their influence above threshold as a consequence of sensory deprivation . The rodent whisker-barrel system is a classic model system to study the effects of experience and sensory loss on neural circuitry . During early development , whisker deprivation disrupts the barrel structure in primary somatosensory cortex ( S1 ) [1] . Whisker ablation as early as P7 induces an expansion of the representation of non-deprived whiskers into the deprived barrel columns [27] . Although gross morphological changes in layer 4 barrel structure may have a critical period , the ability to induce changes in the cortical topography persists in the adult barrel cortex [3] , [28] , [29] . Notably , layers 2/3 and 5 retain the ability to undergo plasticity throughout life . In the current study , we investigated the nature and time course of alteration of axonal arbors of different neuronal classes following whisker plucking in adult animals . We compared the patterns of remodeling of excitatory connections , formed by intrinsic long range horizontal projection of pyramidal neurons , with those of locally projecting inhibitory interneurons . We labeled subsets of neurons with one of two adeno-associated viruses ( AAVs ) carrying the enhanced green fluorescent protein ( eGFP ) or enhanced yellow fluorescent protein ( eYFP ) gene under the control of different promoters . We then imaged the labeled axons once before , and multiple times following , whisker plucking . By combining receptive field mapping , viral injections , and two-photon imaging , we were able to determine the location of the labeled somata and axons in reference to the somatosensory map . We could then directly observe the structural changes of the two main populations of layer 2/3 neurons within the deprived and non-deprived barrel columns over time and determine the extent of structural plasticity following sensory loss . Our studies show reciprocal changes in the long-range horizontal excitatory connections and inhibitory connections within layer 2/3 that parallel experience-dependent reorganization of the somatotopic map .
First we confirmed that adult functional topography was stable over time in the absence of whisker plucking ( unpublished data ) and that our procedure for whisker plucking did induce remapping of cortical topography ( Figure 1D ) . We mapped the cortex of 14 mice using the cortical vasculature as a fiducial reference for the recording sites . In animals with the whiskers left intact , we found no reorganization of the cortical topography 1 mo after the initial mapping ( unpublished data ) . Following whisker plucking , however , and in agreement with previous studies , the cortical representation of non-deprived rows expanded into the deprived rows representing the plucked whiskers ( Figure 1D ) . The cortical topography was remapped between 2 and 30 d of whisker plucking . The locations of cortical sites in the whisker barrel map were compared over the different time points . Consistently , the non-deprived C row expanded into the adjacent , deprived rows ( Figure 1D ) . To study the anatomical correlates of this functional reorganization , we first examined the dynamics of pyramidal cell axons during normal experience . The baseline reconstruction of the axonal plexus was done by imaging in and around the injection site ( C3 barrel column ) . The same area , encompassing the extent of the axonal arbors of labeled neurons , was imaged at subsequent time points . Horizontal axons of layer 2/3 pyramidal neurons were identified by their characteristically long range axons , which run parallel to the cortical surface . We imaged mice under normal sensory experience over time periods extending for up to 1 mo . The plexus of long-range horizontal connections was reconstructed both for axons innervating rows A and B as well as rows D and E . There was no net sprouting or retraction of axon collaterals ( Figure 2 ) . The axonal boutons , on the other hand , showed significant change ( Figure 3A ) , having rates of addition and disappearance of , respectively , 6±2 . 4% and 6±1 . 1% per week ( mean ± S . E . M . ) , which closely correspond to rates observed across species and sensory systems [30] , [31] . We imaged the pattern of arborization of axons extending from normal to deprived cortex after variable periods of whisker plucking . Long-range axons underwent a massive reorganization , which included strong sprouting ( yellow collaterals , Figure 4B ) and weaker retraction ( red collaterals , Figure 4B ) , resulting in a large net increase in density ( Figures 4 and 5 ) . The change in axonal density after 2 d of plucking was small ( Figure 5A ) , but after 14 d , the axonal growth was exuberant , extending deeply into the deprived barrel columns ( Figures 4A and 5B ) . At this time , putative growth cones were observed ( Figure 4A , asterisk ) . Some of the added axonal arbor increased the range of the horizontal projections beyond their normal extent . After 14 d of plucking , the axons originating from non-deprived cortex had increased their density in the deprived barrel columns by a factor of 3 . 59 ( Figures 4A and 5B ) . At every time point the sprouting of axons was accompanied by pruning , to a smaller degree , of a portion of the preexisting collaterals ( 30 d: 3 . 21±0 . 65 and 60 d: 3 . 0±0 . 37; mean ± S . E . M . ; Figure 5C and 5D ) . The net effect of plucking , by the end of the imaging period , was a >3-fold net increase of density of horizontal axon collaterals in the deprived rows . In order to examine bouton dynamics , we divided the boutons into two main groups based on whether they were located on axons that were stable over consecutive imaging sessions or if they were on dynamic axonal branches ( e . g . , axons that were added or lost following whisker plucking ) . During a period of 30 d of whisker plucking , boutons were dynamically recycled even on axons that were stable during both imaging sessions . After 30 d of plucking , 33±5% of the original boutons on stable axons were retracted , while 82±2 . 4% of the boutons were newly formed ( Figure 3C ) . For the stable axons , therefore , bouton density increased and their rate of turnover also increased relative to that observed during normal experience . Axons that projected into non-deprived barrel columns , on the side of the injected barrel columns opposite to that of the deprived barrel columns , were also examined . There was no change in the topography of unplucked barrel column rows A and B , even though they , like the deprived rows D and E , receive inputs from row C . We investigated the axonal dynamics of inputs to non-deprived barrel columns and compared them to animals that underwent normal experience . There was no net change in the density of axons projecting towards non-deprived barrel columns ( rows A and B ) ( Figure 6 ) . They did , however , undergo minor additions to and pruning of their axonal arbors ( Figure 6B–6D ) . Additionally , bouton turnover rate was higher than observed for control non-deprived animals . Boutons were added and eliminated at rates of 43±18 . 2% and 29±6 . 7% per month , respectively ( Figure 3B ) . The number of boutons per micrometer increased from 0 . 09 ( baseline ) to 0 . 14 ( following deprivation of D and E rows ) . The horizontal axons of excitatory neurons projecting into the deprived cortical region , which underwent massive restructuring and synaptogenesis following whisker deprivation , originate from pyramidal cells . We next turn to the role of inhibitory neurons in the reorganization . We imaged the axons of inhibitory interneurons before , and 2 to 30 d after whisker deprivation in either deprived or non-deprived barrel columns . In one animal , we imaged axons on an hourly basis following whisker plucking . In order to observe the structural dynamics of inhibitory interneurons , we genetically modified an AAV vector to label inhibitory interneurons exclusively by driving eGFP expression under the GAD65 promoter . We confirmed the selectivity of GAD65 . eGFP . AAV by using antibodies against a combination of three calcium-binding proteins ( calbindin , parvalbumin , and calretinin ) that are expressed in 90% of almost completely non-overlapping groups of inhibitory interneurons [32]–[34] . All GFP expressing neurons were labeled with the antibody cocktail ( Figure 7A–7B ) . The GAD65 . eGFP . AAV therefore allows us to observe the dynamics specifically for inhibitory cells . While the CMV promoter that was used to label long range excitatory connections labeled both interneurons and excitatory neurons , the long range connections that were the focus of the study were likely to originate primarily from excitatory neurons , since the axons of excitatory neurons are known to project much farther than the axons of inhibitory neurons . Since the longest range axons originate from excitatory cells , and since most axons imaged in the deprived barrel columns were far from the injection site , the experiments examining axonal dynamics with cells labeled with the CMV promoter were predominately excitatory axon collaterals . Additionally , as will be shown below , the inhibitory interneurons located in non-deprived cortex did not stretch their collaterals beyond their normal reach following deprivation , minimizing the potential amount of contamination of deprived barrel columns by inhibitory axons in the CMV . GFP injections . The longest range projections into the deprived barrel columns would therefore originate from excitatory neurons . We injected the GAD65 . eGFP . AAV virus in either rows B and C ( non-deprived ) or rows D and E ( deprived ) after the S1 barrel field was mapped electrophysiologically . We were able to directly observe the structural dynamics of inhibitory interneurons in both deprived and non-deprived cortex . As with the previous set of experiments , we plucked rows D and E completely at the end of the initial imaging session . To establish the axonal dynamics of inhibitory interneurons in mice under normal experience , we imaged mice several times without whisker plucking . These animals did not exhibit significant changes in axonal density ( Figure 7C ) . Boutons were added at 10±2 . 8% and retracted and 8±2% per week ( Figure 3D ) . These rates were slightly higher than those observed for excitatory neurons . The inhibitory interneurons located within the non-deprived barrel columns underwent reorganization in the first days following whisker plucking ( Figure 8 ) . The process of structural reorganization was dynamic , with both axonal retraction and growth occurring throughout the axonal plexus , including the portions located within the deprived and non-deprived barrel columns . Unlike the excitatory axons originating from the non-deprived barrel columns , the overall envelope of the axonal field from non-deprived inhibitory neurons did not change significantly over a period of 30 d of plucking . We also examined the boutons of these neurons . Boutons appeared and disappeared at rates of 31±8 . 5% and 23±5 . 7% over a period of 2 d , respectively ( Figure 3E ) . The axonal arbors of inhibitory interneurons residing within the deprived barrel columns underwent massive reorganization ( Figure 9 ) . A large number of axons close to the cell somata retracted following 2 d of plucking . However , the most marked change was the increase in the lengths of axons extending into the cortical area surrounding the deprived barrel column after 2 d of plucking ( Figure 9A–9B ) . At this time point total axonal length increased by a factor of 2 . 5 . These axons , which typically sent axonal arbors approximately 450 µm from the injection site in normal cortex , now reached as far as 1 , 100 µm . Over the subsequent weeks there was a slight retraction in the overall extent of the inhibitory axon collaterals , but the new axons still extended well beyond their original reach . At 14 d and 30 d these axons reached all rows of whisker barrel columns and were stable ( Figure 9C and 9D , right column ) . Interestingly , the new axons formed boutons almost exclusively around the outer perimeter of the barrel columns ( Figure 10A ) . This distribution suggests that the newly formed inhibitory axons target either neurons located within the septal region of the barrel columns or the apical dendrites of layer V pyramids . These dendrites cluster along the barrel column walls as they reach towards the cortical surface [35] . In order to examine inhibitory interneuron bouton dynamics , we divided the axons into three main groups based on whether they were stable , added , or lost following whisker plucking . Turnover rates were determined for all of these categories . For the stable axons , bouton turnover was elevated following 2 d of whisker plucking , with bouton disappearance dominating over appearance ( appearance: 26±5 . 3% and disappearance: 59±6 . 7% over the 2 d period; Figure 3F ) . This led to a net decrease in the density of boutons on stable axons ( from 0 . 09 boutons/µm to 0 . 07 boutons/µm ) , which was the opposite of what we found for excitatory neurons . Inhibitory interneuron bouton density was 0 . 06 boutons/µm for axons destined for retraction and 0 . 05 boutons/µm for axons added following whisker plucking . We also examined the axonal dynamics over shorter periods of whisker plucking by repeatedly imaging a short stack of axons located within the non-deprived cortex and originating from the deprived D and E rows . We imaged both before plucking and every hour following whisker plucking from 2 . 5 to 5 . 5 h ( Figure 10B ) . At the earliest time point ( 2 . 5 h ) axonal length increased ( 14 . 9%; Figure 10C ) and bouton density decreased ( −18 . 75%; Figure 10D ) . The axonal length and bouton numbers continued to change over the next few hours , involving both addition and retraction . At 5 . 5 h , we observed a net gain in axonal length of 56 . 5% while boutons were reduced by 28 . 1% . We plotted the ratio of axonal length , for different durations of deprivation , over baseline before whisker plucking for axonal arbors of inhibitory interneurons residing within the deprived barrel columns and for axonal arbors of excitatory neurons residing in non-deprived cortex ( Figure 11 ) . Interestingly , the growth of inhibitory interneurons axons projecting to the non-deprived cortex preceded and increased more quickly than those of the excitatory neurons projecting into the deprived cortex . By 14 d of whisker plucking , however , excitatory axons surpassed the amount of growth seen for the inhibitory interneurons .
The combined techniques of labeling neurons with genes encoding fluorescent proteins , delivered with viral vectors , and longitudinal in vivo two-photon imaging enabled us to determine the dynamics of cortical circuitry following alterations in sensory experience . Moreover , the use of cell specific promoters allowed us to dissect out the contributions of different components of cortical circuits to the reorganization . Several novel features of the reorganization were revealed in these experiments . The axonal remodeling consisted of a parallel process of sprouting and pruning . While there was steady state turnover of both excitatory and inhibitory synapses in the absence of plucking , this turnover was dramatically elevated following sensory deprivation . Previous studies have focused primarily on the contribution of excitatory connections in the remodeling [16]–[18] . Here we observed striking changes in the axonal arbors of inhibitory neurons . The changes consisted not only of alterations in the density of axon collaterals in deprived and non-deprived barrel columns , but an extension of the envelope of axonal arborization far beyond its normal extent , both for excitatory neurons in non-deprived barrel columns and inhibitory neurons in deprived barrel columns . This contrasts with earlier findings that the axonal changes principally consisted of changes in the density of existing clusters of axon collaterals [13] , [14] . Moreover , the changes observed in inhibitory interneurons located in the deprived barrel columns were extremely rapid and preceded the axonal growth of excitatory neurons from non-deprived cortex . Finally , while postmortem studies examined late time points after the onset of sensory deprivation , here we observed changes occurring very rapidly , in some instances within hours after the initial whisker plucking for inhibitory interneurons . The reorganization of adult cortical topography has been detailed in many systems . Remapping occurs almost immediately and continues to progress in the weeks and months following peripheral lesions [36] , [37] . Almost immediately following sensory loss , large regions within the deprived area become unresponsive [7] , [9] . This is accompanied by an immediate expansion of the cortical representation of non-deprived sensory input into the deprived regions . The newly remapped sites have neurons with receptive fields that are larger than normal and that respond more sluggishly . Over time , remapping progresses , with the region of unresponsive cortex getting smaller and the new receptive fields shrinking back to their normal size [9] . In the model system we have used here , cortical reorganization following whisker plucking occurred across barrel columns . Previous studies have demonstrated topographic changes in the whisker barrel system within three and a half days of whisker trimming [28] . We observed the process of axonal remodeling of inhibitory interneurons to begin even earlier , within hours of whisker plucking . By carrying information across adjacent whisker barrel columns , the horizontally projecting axons are likely to represent the circuit mechanism of topographic remapping . Although one may have expected that short term functional changes would be attributed to alteration in the strength of existing synapses rather than axonal sprouting and synaptogenesis , the rapidity and massive onset of the morphological changes we observed opens the possibility that axonal remodeling may underlie even the earliest phases of cortical remapping . Following whisker plucking , inhibitory interneurons from within the deprived cortex extended their axons into the non-deprived rows with extreme rapidity . Axons from excitatory neurons located in the non-deprived barrel columns invaded the deprived row on a slightly slower time course ( Figure 11 ) . Previous studies have demonstrated that cortical areas undergoing sensory deprivation show a transient decrease in GAD expression [38]–[40] , while over-stimulated cortical areas show a very early yet transient upregulation of GAD , GABA , and/or GABAAR in the hours and days following the manipulation [41]–[43] . The retraction of inhibitory axons within the deprived barrel columns parallels the decrease in GAD expression . The observed decrease in bouton numbers following plucking may account for the decreased levels of GAD expression . In the current study , the axonal arbors from inhibitory interneurons were as dynamic as excitatory axons following sensory deprivation . The extension of inhibitory axons from deprived to non-deprived cortex may represent a compensatory mechanism required to maintain the balance between excitation and inhibition that exists in normal cortex . We hypothesize that this regulation may require contacting the somata of the excitatory neurons that sprout into the deprived barrel columns , which would explain the extension of inhibitory axon collaterals into the non-deprived barrel columns . The functional match between inhibitory and excitatory tuning has been seen in orientation tuning in visual cortex [44] , [45] and in frequency tuning in auditory cortex [46]–[48] . In order to maintain this balance when excitatory neurons extend their arbors into new territory , there appears to be a compensatory attraction of inhibitory axons towards these excitatory neurons . Increasing evidence points towards an ongoing process of synaptogenesis and synapse elimination in the adult brain , even in the absence of deprivation [16] , [30] , [31] , [49] . Our current results support these findings , with excitatory axons showing a constant rate of bouton turnover of 6% per week . Here we showed that inhibitory interneurons have a similarly high rate of turnover ( 8% to 10% per week ) . With altered sensory experience , the rate of bouton turnover accelerated several-fold . The largest rates of turnover were seen for the excitatory axons projecting into deprived cortex and for inhibitory axons projecting from deprived to non-deprived cortex . Our results demonstrate that rapid alteration of cortical circuits accompanies the functional changes associated with sensory deprivation in the adult . The changes of inhibitory connections were as substantial as those seen for excitatory connections , and these results show that inhibition plays a key role in adult cortical plasticity , mirroring its role in early postnatal development . Although the changes we observed in this study are associated with sensory deprivation , similar mechanisms may apply to normal processes of experience-dependent change , such as those associated with perceptual learning .
We genetically engineered two AAVs to label subsets of neurons . One AAV contained the CMV promoter and the eYFP gene . The eYFP gene ( derived from pEYFP-N1 , Clontech ) was PCR-amplified and then cloned into the pCMV-MCS Vector ( Stratagene ) . The transgene was flanked by the two inverted terminal repeats . The vector was verified by sequencing . The AAV was prepared by packaging the vector plasmid with the AAV serotype 2/1 using a calcium phosphate transfection . The virus was purified using heparin affinity chromatography [50] and concentrated ( Millipore Biomax 100K filter ) . Titer was determined by quantitative PCR using CMV-specific primers . The titer was determined to be 1011 viral particles per milliliter . The other AAV labeled inhibitory interneurons ( GAD65 . eGFP . AAV ) . The GAD65 promoter [51] was isolated from a plasmid that included 5 . 5 kilobases upstream from the start codon of GAD65 ( provided by G . Szabo , Institute of Experimental Medicine of the Hungarian Academy of Sciences ) . From that 5 . 5 kb section , a 2 . 7 kb fragment directly upstream from the start codon of the GAD65 gene was amplified out via PCR; KpnI and AgeI restriction sites were added to the 5′ and 3′ sites , respectively . The KpnI primer sequence was 5′ CGAGGTACCAAGTAAGCAGAGGGGCAGTG; the AgeI sequence was 5′ CGAACCGGTGCAGAGCCATCTTCAGATCC . The GAD65 promoter sequence was inserted into a custom AAV2 plasmid ( provided by J . Pena , The Rockefeller University ) . The AAV2 vector and the GAD65 promoter sequence were digested with Kpn I and Age I restriction enzymes . The GAD65 promoter was then cloned into the AAV2 vector , which contains the eGFP sequence and woodchuck hepatitis post-translational regulatory element ( WPRE ) . The total resulting size of the AAV genome in the plasmid , including the region flanked by the ITR , is 4 . 7 kb . The titer was determined to be 2×1013 particles per millimeter by quantitative PCR using GFP-specific primers . Calbindin , calretenin , and parvalbumin are calcium-binding proteins that are expressed in almost non-overlapping populations of inhibitory interneurons . 90% of all inhibitory interneurons express one or another of these three proteins [32]–[34] . Antibodies for all three of these proteins were used to confirm the specificity of the virus . Animals were perfused with 2% paraformaldehyde in PBS and cryoprotected in 30% sucrose . The region of cortex including the injection site was sectioned at 30 µm . Sections were rinsed three times in PBS and incubated in a blocking media that contained 10% normal goat serum in Tris buffer solution ( pH 7 . 4 ) , 0 . 2% Triton X-100 , for 1 h at room temperature . Primary antibodies were incubated for 48 h at 4°C . Primary antibodies: rabbit anti-calbindin ( 1∶5000 , Swant ) , rabbit anti-calretinin ( 1∶2000 , Swant ) , and rabbit anti-parvalbumin ( 1∶5000 , Swant ) were used . Sections were rinsed three times in tris buffer solution . Sections were then incubated with a secondary antibody: Cy3 Goat Anti-Rabbit ( 1∶500 , Jackson ImmunoResearch laboratories ) at room temperature for 2 h . Sections were rinsed three times before being mounted and coverslipped with 4% n-propyl gallate ( Sigma-Aldrich ) in 90% glycerol to prevent photo-bleaching . All procedures were done according to institutional and federal guidelines . All mice ( N = 42 ) used for these experiments were adult , 2 mo or older , at the time of the virus injection . Imaging began 3 to 4 wk later . They were anesthetized with ketamine ( 80 mg/kg ) and xylazine ( 6 mg/ml ) . Dexamethasone ( 0 . 2 mg/Kg ) was administered . A craniotomy was performed over the barrel cortex , but the dura was left intact . The receptive fields of the whiskers were mapped electrophysiologically . Recordings were taken between 300 and 500 µm below the cortical surface , in response to stimulation by whisker reflection with a rod . Receptive fields were mapped by using insulated tungsten microelectrodes ( impedance 1–2 MΩ , Alpha Omega , Israel ) . Cortical spikes were acquired with an acquisition program ( Plexon Inc , Dallas , TX ) , amplified 10 , 000 times , and fed into an audio monitor ( Grass Medical Instruments , West Warwick , RI ) . An image of the exposed cortex was taken . Blood vessels were used to determine the location of the electrode in the cortex . Responses to electrode penetrations were recorded and labeled on the image using Photoshop CS ( Adobe Systems Inc , San Jose , CA ) . The cortical topography was determined for the entire whisker barrel cortex . The functional and anatomical maps were brought into register by aligning the blood vessels in the cortical topography photomicrograph and the two photon images . Recording positions were established relative to the cortical surface vasculature , and the resulting map was used as the basis for the viral injections . The injections consisted of 10 nl of CMV . eYFP . AAV ( 1×1011 particles/ml ) placed in the C3 barrel column , or 20 nl of GAD65 . eGFP . AAV ( 2×1013 particles/ml ) , placed into either the deprived columns or non-deprived barrel columns . One of the two high-titer preparations of AAV was pressure injected into the cortex using borosilicate glass micropipettes ( World Precision Instruments , Inc , Sarasota , FL ) and Picospritzer III ( Parker Hannifin Corp , Cleveland , OH ) . Several pulses at 0 . 1 bar were given at a depth of 350 micrometers over 1 min , with a 5 min resting period afterwards . Dura was left intact throughout the procedure . Agar and a 5 mm circular glass coverslip were placed over the craniotomy and sealed with dental acrylic ( Lang Dental Manufacturing Co Inc , Wheeling , IL ) . Imaging began at least 3 wk following the viral injection to ensure full expression of the virus . Animals were anesthetized with isoflurane ( 3% induction; 1 . 5%–2% maintenance ) . The cranial window was cleaned but the dura was left intact and the area was imaged with the 2-photon microscope . The labeled neurons and their axons were first imaged 3 to 4 wk following the viral injection to ensure full expression of the fluorescent protein . Following the baseline imaging session ( s ) , whiskers from rows D and E were plucked every other day to prevent new whisker growth and thereby to maintain deprivation of the D and E whisker barrel columns . Imaging was done for variable intervals following the onset of the deprivation period . Images were collected on a custom built 2-photon microscope that was modified from a Leica TCS Sp2 confocal microscope ( Mannheim , Germany ) with a custom moveable scanning head , which can be moved in three dimensions using a Sutter ( Novato , CA ) MP-285-3Z micromanipulator . The laser source was provided by a Ti-sapphire laser ( Tsunami/Millenia System , Spectra Physics , Mountain View , CA ) . Images were acquired with Leica Confocal Software . Offline images were viewed with ImageJ ( http://rsbweb . nih . gov/ij/ ) . Images were aligned and corrected for movements created by breathing artifacts using a custom Matlab ( Mathworks ) program written in the laboratory . Images were deconvolved using Huygens deconvolution software ( Scientific Volume Imaging , Hilversum , The Netherlands ) . Finally , axons were traced via the semi-automatic mode in Neuromantic ( v1 . 6 . 3 , http://www . rdg . ac . uk/neuromantic/ ) , and voxel size was corrected with another custom Matlab ( Mathworks ) program . Boutons were then categorized depending on the fate of the axons on which they resided ( stable , added , or retracted ) . Voxel size was corrected by a custom Matlab program .
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The adult brain is capable of learning new tasks and being shaped by new experiences . Evidence for experience-dependent plasticity of the adult cerebral cortex is seen in the functional rearrangement of cortical maps of sensory input and in the formation of new connections following alteration of sensory experience . The barrel cortex of the rodent receives sensory input from the whiskers and is an ideal model for examining the influence of experience on cortical function and circuitry . In the current study , we asked how experience alters cortical circuitry by examining excitatory and inhibitory axons within the adult whisker barrel cortex before and after plucking of a whisker and hence removal of its sensory input . By combining delivery of genes encoding fluorescent proteins , under the control of cell-type specific promoters , with two-photon imaging , we were able to directly examine subpopulations of axons and to determine when and to what extent experience altered specific connections in the adult living brain . Following whisker plucking we observed both the retraction of existing connections and an exuberant amount of growth of new axons . Axonal restructuring occurred rapidly and continued to undergo changes over the following weeks , with reciprocal sprouting of axons of excitatory neurons located in non-deprived cortex and of inhibitory neurons located in deprived cortex . The changes in the inhibitory circuits preceded those seen for excitatory connections .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"neuroscience/neurobiology",
"of",
"disease",
"and",
"regeneration",
"neurological",
"disorders/neurorehabilitation",
"and",
"trauma",
"neuroscience/sensory",
"systems"
] |
2010
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Axonal Dynamics of Excitatory and Inhibitory Neurons in Somatosensory Cortex
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Chagas disease affects millions of people in Latin America . The control of this vector-borne disease focuses on halting transmission by reducing or eliminating insect vector populations . Most transmission of Trypanosoma cruzi , the causative agent of Chagas disease , involves insects living within or very close to households and feeding mostly on domestic animals . As animal hosts can be intermittently present it is important to understand how host availability can modify transmission risk to humans and to characterize the host-seeking dispersal of triatomine vectors on a very fine scale . We used a semi-field system with motion-detection cameras to characterize the dispersal of Triatoma infestans , and compare the behavior of vector populations in the constant presence of hosts ( guinea pigs ) , and after the removal of the hosts . The emigration rate – net insect population decline in original refuge – following host removal was on average 19 . 7% of insects per 10 days compared to 10 . 2% in constant host populations ( p = 0 . 029 ) . However , dispersal of T . infestans occurred in both directions , towards and away from the initial location of the hosts . The majority of insects that moved towards the original location of guinea pigs remained there for 4 weeks . Oviposition and mortality were observed and analyzed in the context of insect dispersal , but only mortality was higher in the group where animal hosts were removed ( p-value <0 . 01 ) . We discuss different survival strategies associated with the observed behavior and its implications for vector control . Removing domestic animals in infested areas increases vector dispersal from the first day of host removal . The implications of these patterns of vector dispersal in a field setting are not yet known but could result in movement towards human rooms .
Chagas disease , a vector-borne disease caused by the parasite Trypanosoma cruzi , affects from 7 to 8 million people in the Americas [1] . The vast majority of people infected are not detected [2] , [3] , and when the disease manifests clinically it cannot be reversed and can be fatal [2]–[4] . In addition to the inability to detect early cases and the lack of treatment in advanced stages , currently there are no vaccines available to prevent infection [5] . Therefore , efforts to halt transmission are crucial to reduce the burden of disease [6] . Most control programs aimed at halting T . cruzi transmission focus on reducing the insect vector populations [6] . In the Southern Cone of South America , transmission of T . cruzi is mostly driven by Triatoma infestans living within or very close to households , at times feeding on humans , but feeding mostly on domestic animals [7] , [8] . The proximity between animal corrals and human bedrooms , especially in urban areas or densely populated rural areas , may facilitate dispersal of vectors from animal enclosures into human houses . Several studies have reported the presence of guinea pigs in houses as a risk factor for triatomine infestation in endemic areas [1] , [9]–[11] . In Arequipa , Peru , an area where Chagas disease is an emerging and re-emerging problem , the presence of domestic guinea pigs increases the odds of triatomine-insect infestation by 1 . 69 times and the triatomine density by 2 . 4 times [2] , [3] , [12] . In this setting , guinea pigs are raised in small numbers in backyards , on rooftops , and inside houses . Guinea pigs are an important source of protein in the region . Many reasons can lead to changes in the distribution and presence of domestic animals of different species . Because guinea pigs are typically fed with alfalfa , the price of which fluctuates widely [2]–[4] , [13] , and because of their small number in a corral , guinea pigs are often withdrawn during certain parts of the year and the corral left empty . The loss of hosts is presumably a catastrophic event for local triatomine populations relying on those animals as a source of blood meals . In other areas where the animal species composition differs , animals could be withdrawn from corrals due to death , migration , trading , or slaughter , and these events might pose the same threat for triatomine populations . When hosts are removed from corrals the triatomine insects that live , reproduce and feed on them might either leave , or stay to wait for a new wave of hosts . If the triatomine vectors disperse from their refuge in search of new hosts , the removal of animal hosts likely implies a sudden and important rise of the risk for the human populations . The dispersal behavior of different triatomine vectors has been studied from various perspectives . T . infestans uses two types of locomotion for dispersal: flying and walking . In Argentina most T . infestans were found walking in infested areas , but a number were captured flying [5] , [14] . To add complexity to the locomotion patterns observed in T . infestans , it has been reported that the initiation time of flight shows wide variability based on climatic and individual factors [6] , [15] and that T . infestans do not fly above 2 , 750 m [6] , [16] , an important feature in the highly populated cities of the Andes with altitude in this range such as Arequipa in Peru and Cochabamba in Bolivia . For some triatomine species that use flight as an important type of locomotion , such as Triatoma dimidiata [7] , [8] , [17] , [18] , light is a physical cue that might attract insect into houses [19] , and streetlights have been associated with increase domestic infestation [20] . Some studies have reported cues for walking dispersal related to host seeking , mainly by isolating the effect of a chemical cue . Taneja and Guerin [21] noted that carbon dioxide is an important cue for host location by triatomine insects; however , its attractant effect was not stronger than host odor . Barrozo and Lazzari [22] found that T . infestans moves towards airstreams enriched with CO2 and the accuracy of the orientation increases with CO2 intensity . They also found that L-lactic acid did not show an effect by itself , but its combination with CO2 had a synergistic effect that increased a positive orientation . Taneja and Guerin also found in 1997 that urine and its component ammonia attract triatomines [23] . The joint attractive effect of the volatile compounds of growing yeast was demonstrated under laboratory conditions by Guerenstein et al . [24] and under field conditions by Lorenzo et al . [25] . Guerenstein and Guerin [26] found that there is an activation effect caused by nonanal and that isobutyric acid increases triatomine upwind displacement . Another source of chemical compounds that causes triatomine aggregation are the triatomines themselves . Schofield and Patterson [27] found that triatomine feces contain an assembly pheromone that attracts unfed larvae and stops the locomotion of fed larvae . Lorenzo et . al . [28] demonstrated that nymphs of T . infestans tend to aggregate around papers impregnated with dry feces , but not papers with fresh feces . Lorenzo and Lazzari [29] found that T . infestans aggregates not only around papers impregnated with its own feces , but also with feces of T . guasayana and T . sordida . Also Lorenzo and Lazzari [30] reported a response of T . infestans to chemical footprints left by walking insects . Experimentally they showed that the cuticle plays a role in this signaling process . Interestingly , Reiseman et al . [31] described a differential response to feces based on color lights . Other studies have reported the role of physical cues on host seeking by triatomines . Lazzari and Nuñez [32] and Flores and Lazzari [33] reported the importance of heat from warm-blooded animals on the displacement of triatomines towards food . Barrozo et al . [34] described the role of water vapor , a common and constant by-product of animal respiration , on the orientation of triatomines . Finally , Catala et al . [35] proposed a hypothesis about the role of infra-red radiation on the dispersal of T . infestans . The current knowledge about triatomine behavior in relation to different attractants has led to the development of traps baited with host-based cues , light traps , and artificial shelters [36] . All these studies , and their applications , analyzed displacement of triatomines towards chemical and physical cues; however , it is not yet known how triatomine insects disperse in the event that their hosts , the sources of all these cues , completely disappear from their surroundings . In order to determine how the vectors behave under such circumstances , we characterized the initial dispersal behavior of triatomine insects in a small-scale area when the only source of blood meals is withdrawn from the environment , and compared it to an identical environment in which the host population remains constant . The main objective of our study was to test the hypothesis that T . infestans migrate at a faster rate when their hosts are removed compared to T . infestans in continuous presence of hosts .
The Institutional Animal Care and Use Committee ( IACUC ) of Universidad Peruana Cayetano Heredia reviewed and approved the animal-handling protocol used for this study ( identification number 60942 ) . The IACUC of Universidad Peruana Cayetano Heredia is registered in the National Institutes of Health at the United States of America with PHS Approved Animal Welfare Assurance Number A5146-01 and adheres to the Animal Welfare Act of 1990 . In order to understand the dispersal of T . infestans after bloodmeal sources are removed , we conducted an experiment in a semi-field system with motion-detection cameras . In two 10-foot long glass tanks with a glass-walled maze in the middle area and floor covered with 2-foot x 3-foot sheets of white paper , we placed a cage with two guinea pigs at one extreme of the tanks . A cardboard box with corrugated paper to increase internal surface ( the primary refuge ) containing 60 triatomine insects was placed in each tank proximal to the guinea pig cage . An identical carboard box ( the secondary refuge ) was placed at the extreme of the tank opposite to the guinea pig cage and the primary refuge ( Fig . 1 ) . After a week of cohabitation , the guinea pigs from one of the tanks were removed ( time of intervention ) ; this tank was designated the intervention tank and the tank with constant presence of guinea pigs was designated the control tank . All triatomine insects were fasted for 2 weeks before cohabitation with guinea pigs started , and insects had free access to the hosts during the time hosts remained in the tanks . We defined six discrete locations across each experimental tank: quadrants 1–4 and the primary and secondary refuges . Quadrant 1 refers to the area occupied by the guinea pig cage at one extreme of the tank; the primary refuge is proximal to this quadrant . Quadrant 2 refers to the area immediately surrounding the primary refuge and the beginning of the maze . Quadrant 3 refers to the middle portion of the maze . Quadrant 4 refers to the final portion of the maze and the area immediately surrounding the secondary refuge , which was placed at the extreme of the tank ( Fig . 1 ) . The insects were free to move throughout all areas of the tank . During the experimental period , we counted the number of T . infestans in these six different areas of the intervention and control tanks , video-recorded their movements , and estimated their level of activity by motion-activated snapshots . The experiment was repeated three times . We used a data logger to record temperature and relative humidity every 10 minutes during the three replicates of the experiment . We used a total of 12 one-month-old female guinea pigs from a local farm free of triatomine infestation and a total of 360 triatomine insects that were raised in a large triatomine colony originated from Arequipa , Peru . In order to form the 60-triatomine groups for each tank and each repetition , we chose 10 triatomine insects from the following six groups: 2nd , 3rd , 4th , 5th instar , female and male . We used a video camera system to observe and record two types of files; snapshots of each quadrant of each tank every 10 seconds and snapshots of a quadrant where any movement was detected to a maximum of two motion-activated snapshots per second . Because of the continuous movement of guinea pigs in quadrant 1 , we only used snapshots from the other 3 quadrants where activation of snapshots was solely related to triatomine movement . Because triatomines are nocturnal , we used red-light bulbs between dusk and dawn to provide illumination for the cameras . To confirm the lack of disturbance of red light in the dispersal of T . infestans we conducted a pilot with three different light colors: white , green , and red . We illuminated a 2-square-foot-area tank containing 20 T . infestans and observed their behavior . Under white and green light the triatomine insects stayed on the edge of the tank and only moved along the borders , a behavior consistent with negative phototaxis . Under red light the triatomine insects moved all over the tank surface without showing any pattern that would suggest light disturbance ( S1 , S2 , and S3 Figs . ) . We also visually examined each tank at around 10 AM every three days and the day before and after removing guinea pigs . We counted the number of triatomine insects in each quadrant and box and registered their sex or developmental stage and their nutritional status . The nutritional status was only recorded during the 7 first days of cohabitation prior to intervention . The nutritional status was measured in a scale from 1 to 4 , and was based on a qualitative determination of blood reserves in the midgut developed by Montenegro [37] . During these in-person observations , we recorded the number of dead insects and eggs laid in each quadrant or refuge . We withdrew the eggs from the tanks upon observation , but left dead insects where they were found .
Across the three repetitions , we observed a similar pattern of emigration of insects; the number of triatomine insects in the primary refuge decreased faster in the intervention tank than in the control tank ( Figs . 2 and 3 ) . We also observed that triatomine insects in the intervention tank leaving their primary refuge dispersed in both directions , towards and away from the empty guinea pig cage ( Fig . 2 ) . In the control tank the number of triatomine insects in the primary refuge reduced slightly over time and those triatomines that emigrated consistently did so towards the secondary refuge . Fig . 2 illustrates the dispersal data over time from one of the repetitions , although we observed the same pattern in all three repetitions . In this figure , the number of triatomine insects that were found in their primary refuge is represented by green dots , and insect emigration from the primary refuge is represented by the reduction of those dots over time . This reduction is more marked in the intervention tank . Emigration from the primary refuge to the secondary refuge was prevalent in both tanks and is represented by the red dots . However , in the intervention tank alone , some insects left their primary refuge and dispersed towards the empty guinea pig cage; this emigration is represented by the black dots . When we evaluated dispersal by developmental stage and sex , we observed more complex patterns . The most remarkable finding is that females , independently of the tank , started dispersal immediately after they were placed in the experimental area . A proportion of the females quickly found the secondary refuges and stayed there . On average , 32% of females were observed in the secondary refuge the day before intervention . A related observation was the presence of eggs in the primary and secondary refuges and across the quadrants . We did not observe any pattern in the distribution of eggs over the study area , but the number of laid eggs increased exponentially over time in the three repetitions in both tanks as shown in Fig . 4 . The three Poisson regression models we used to quantify the rate of emigration of T . infestans from their primary refuge in our system estimated similar rates , with an average of 10 . 2% and 19 . 7% reduction of insects in the primary refuge per 10 days lapsed in the control group and the intervention group , respectively . This difference was statistically significant , with p-values between 0 . 029 and 0 . 036 depending on the model ( Table 1 ) . Overall , the AIC favored the hierarchical Poisson model with a random intercept ( Table 1 ) , suggesting that the counts of insects in the refuge at the time of intervention might be different between repetitions but the net emigration rates after the intervention are consistent across repetitions . Nevertheless , the heterogeneity in the number of triatomine insects in the primary insect refuge at the time of intervention was not statistically significant ( chi-squared = 0 . 3748; df = 2; p-value = 0 . 83 ) . The dispersion of the data around the values predicted by the best model is presented in Fig . 3 . The nutritional status of triatomines found in the secondary refuge the day before intervention was not statistically different when we compared the intervention versus the control tank ( Fisher's exact test p-value = 0 . 39 ) . We observed high variability of daily median relative humidity and low variability of daily median temperature as observed in Fig . 5 . These weather variables had neither an important effect size nor a statistically significant influence on the emigration rate . In terms of observed level of insect activity , there were complex patterns in the intervention tank after guinea pigs were removed . The night after the guinea pigs were removed , we observed an elevated number of movements recorded by our system in the intervention tank compared to the control tank . S1 Video shows quadrant 1 and primary refuge of the intervention and control tanks between 11 PM and 2 AM the night after guinea pigs were removed . The video was created with the snapshots taken automatically every 10 seconds and clearly shows the high level of activity in the intervention tank . Over the observation period , the level of activity was higher in the intervention tank , and can be observed by comparing the spikes in Fig . 6 . The average number of motion-activated snapshots per day was higher in the intervention tank compared to the control tank by 11 , 186 snapshots across all three repetitions ( 95% CI: 4 , 653–17 , 720; p = 0 . 001 ) . A one Celsius degree increase in the median daily temperature was associated with an increase of 5 , 068 motion-activated snapshots per day ( 95% CI: 1 , 317 , 8 , 818; p-value = 0 . 01 ) , and an increment of one percentage point of relative humidity was associated with an increase of 302 motion-activated snapshots per day ( 95% CI: −49 , 655; p-value = 0 . 10 ) . An observation related to the frequency of movements recorded by our system is that insect activity started on average 1 hour and 12 minutes earlier in the intervention tank compared to the control tank the night after hosts removal . The probability of triatomine insects dying in the intervention group was 1 . 46 times higher than in the control group and this increase was statistically significant ( 95% CI: 1 . 10 , 1 . 95; p-value = 0 . 0098 ) . Overall , most dead triatomine insects were found in the primary refuge . In total , in the three repetitions , 24 out of 33 dead insects were found in the primary refuge of the control tank , and 34 out of 52 dead insects were found in the primary refuge of the intervention tank .
Here we characterize the ex-situ dispersal of T . infestans after removal of sources of blood . The observed reduction of insect count in their primary refuge was , on average , 19 . 7% over 10 days , and triatomine insects did not distribute randomly over the available area . Some of the insects remained in the primary refuge ( close to the guinea pig cage ) , some migrated to the secondary refuge ( far from the guinea pig cage ) , and some dispersed towards the empty guinea pig cage . The empty guinea pig cage was the only source of olfactory cues associated with blood sources , and also offered shelter to bugs . In r/K selection theory some species develop an r strategy that favors population growth rate ( r ) with low intra-species competition , and many offspring with low probabilities of survival , while others develop a K strategy , where population growth is limited by carrying capacity of their environment ( K ) , few offspring are produced , all with a high probability of survival , and intra-species competition is high [39] . These two strategies also correspond to different dispersal patterns: a pure r-strategy would involve a continuous high level of dispersal that would minimize the impact of perturbations , and conversely , a pure k-strategy would minimize dispersal under constant conditions , but may display significant dispersal in the face of perturbation [40] . Active dispersal of triatomine insects has been mainly associated with seeking food and mating [41]; the r-strategy may also serve to find and colonize new areas , maximizing the overall survival of the descendants in the case of unpredictable environments [42] . Rabinovich proposed that T . infestans should be considered a K-strategist based on population growth , average longevity , starvation resistance , and dispersal capacity [43] . However , he also recognized that little was known about the dispersal capacity of T . infestans . The significant dispersal we observed to the refuge at the other side of the tank , even with blood sources at immediate proximity suggests that part of the dispersal of T . infestans is linked to an r-strategy type dispersal , while the significant impact of the removal of the hosts on the dispersal rate confirms that there is a K-strategy type dispersal component for a mixed strategy in T . infestans dispersal . We observed two opposite responses after hosts were withdrawn; a proportion of insects stayed close to the host cage , while others migrated away . One explanation for the prolonged presence of triatomines close to the empty host cage is the continued presence of chemical attractants for triatomines . Specifically , urine [21] , [23] , humid feces , or even humid scraps of pasture greens may attract triatomines [34] . Remaining near a previously present food source may also be an advantageous strategy in areas where foraging for new food sources carries a high cost . Triatomine insects have a number of nocturnal predators such as geckos [44] , rodents [45] , and spiders [46] , and diurnal predators such as chickens [47] , dogs [48] , and cats [48] . In addition to the risks of predation , seeking new food sources might expose triatomine insects to desiccation , as occurs with other hemiptera insects [49] , and possibly deplete the insects' energy stores , as suggested by the observations of Abraham et al . [50] who found that flying T . infestans had a poorer nutritional status than those captured close to animal corrals . Interestingly , this potential strategy associated with a passive food-seeking behavior ( waiting in a high-probability-of-food zone ) could explain some previous field observations by our team . In 4 rural communities of Arequipa , Peru , we conducted entomological surveys for triatomines in each animal corral in the area . Among 1762 animal corrals we found T . infestans in 294 , and 104 of these infested corrals did not contain any animals . Those empty corrals might represent high-probability-of-food zones that are exploited by triatomine insects and should be targeted in vector-control strategies . The opposite response observed in our experiment , a large proportion of insects migrating away from the empty cages , may be associated with an active-food-seeking strategy , useful when the risks outside of the refuge are lower than the risk of starving by remaining in the refuge . The increased frequency of movements and the earlier start of insect activity observed in the intervention tank versus the control tank may be explained by active search for a host . This active search most likely was performed by unfed triatomines , while engorged triatomines could stay or avoid being in the proximity of hosts . Surprisingly , across the three repetitions this differential behavior started the same night that guinea pigs were withdrawn . Based on our pilot observations and the biting rate range ( 0 . 29 to 0 . 59 ) reported by Lopez et al . [51] , we left the insects from both tanks to cohabitate with the guinea pig for one week prior to withdrawing the hosts from the intervention tank , and it is possible that most insects had fed on the host early during that week , and would have needed to feed again the same night of host removal . Hosts might also be sources of heat for proper enzymatic activity and be sought by engorged or partially full triatomine insects to facilitate digestion [52] . Previous studies have examined a number of determinants of triatomine dispersal . The presence of hosts for blood meals has been linked directly to the nutritional status of triatomine vectors and to their population density [53] , [54] . Nutritional status can affect female triatomine fecundity , but its main impact on population density is by modifying the duration of the egg-to-adult development period [53]; therefore , the effects of nutritional status on triatomine population density and dispersal would only be seen over several months . In our 5-week-long repetitions , we examined the immediate and short-term effects on triatomine dispersal caused by sudden host removal , a common event in infested areas of Arequipa , Peru . Several studies have reported the association between nutritional status of triatomine vectors and flight dispersal or flight potential [55]–[58] and Ramsey and Schofield even discussed the risk associated with passive transportation of passive triatomines [59] . The role of walking triatomines and its relationship with triatomine nutritional status has been suggested [50] , [60] . In Rhodnius prolixus , a model of triatomine physiology , nutritional status has been found to have an effect on sensory response to host cues , and this is likely to partially explain the host-seeking behavior observed in our system . In the intervention tanks the only host-related cues remained in the empty corral , which may have drawn triatomines to them as their nutritional status decreased over the duration of the trial . However , triatomines dispersing beyond the empty corral might be explained by exploration of the unknown when blood meals are required and not found upon following host-related cues . We observed that in both tanks , control and intervention , triatomines were usually found in the extremes of the tanks during in-person observations ( ∼10 AM ) . Ideal free distribution theory [61] proposes that animals know the quality of the patches ( distribution of resources ) where they move and will choose patches with higher quality . In our intervention tank , after removing guinea pigs , the quality of the quadrants in terms of food became the same; however , the presence of the refuges as well as the presence of the empty guinea-pig cage with feces , urine and scrapes of alfalfa provided cues as well as safe harbor for insects , making some quadrants more attractive than others . Thus , ideal free distribution might explain the similar distribution of T . infestans in areas far from and close to the location of blood-meal sources after removal of hosts . We found T . infestans eggs scattered across the experimental tanks , without any clear pattern . The absence of spatial pattern in the distribution of eggs might suggest a strategy to disperse eggs around the original colony , and could support the null hypothesis of the ideal free distribution of triatomine insect females over the experimental area . It is also possible that triatomine insects did not find an appropriate substrate to lay their eggs [62] . The importance of walking pregnant triatomine females was reported by Abrahan et al . in 2001 [50] , who suggested that this type of locomotion in females is an adaptive strategy that allows for dispersal of many eggs . Dispersing eggs around the original colony increases the chances of at least one egg surviving and it is a preferred strategy as the intensity of predation increases [63] . Egg dispersal might also increase colonization success and help to avoid reaching carrying capacity [64] if the colony only grows with a limited supply of blood meals or nesting space . We did our best to maintain the intervention and control groups under identical conditions throughout the experiments . Despite our efforts , there were some insects that initiated dispersal in the intervention tank before guinea pigs were withdrawn in two of the three repetitions . The slight difference in the number of insects in the original refuge before the guinea pigs were withdrawn was not statistically significant for any of the three repetitions . For all repetitions the control and intervention tanks were switched , and in all cases the insects started the repetitions in a completely clean tank without residues from previous experiments that could have leaved traces of olfactory cues . One potential explanation we propose is that triatomine insects display a clear r-strategy , dispersing considerably even when they have reliable food sources and refuge [65] , [66] . We faced some limitations that should be taken into account when making inferences from our results to in-situ triatomine dispersal . In our system the substrate of the tanks' floor was white paper and within the refuges was corrugated cardboard . In the wild , triatomine insects are exposed to a wide variety of materials which can alter their physiology and behavior [67] and their fecundity [62] . We had a fixed number of triatomines by developmental stage and sex which did not reflect the stable population distribution of T . infestans [43] . Patterns of dispersal , however , might be influenced by density and stage structure of vector colonies [68] . We kept a fixed number of guinea pigs across all repetitions , but the ratio of hosts to vectors might also influence dispersal patterns [69] . Also , the number of triatomine insects per tank reduced slightly during the experiment due to mortality . In field conditions the population might recover or increase through reproduction , especially in corrals with a constant source of blood meals for the production of eggs . Changes in population size might affect dispersal , especially when the number of insects exceeds the carrying capacity of the environment [70] , [71] . In addition , we observed our ex-situ system for only 28 days after the removal of the hosts . The dispersal of triatomines would certainly have continued past our period of observation , and different patterns could have emerged over longer time scales . We observed a smooth decrease of triatomines in the primary refuge over 4 weeks . If it were the case that chemical cues associated with the empty guinea pig cage did not attract triatomines following that period , a sudden dispersal of triatomines would be observed . This effect could be accentuated by the increasing presence of feces associated to dispersing triatomines in the secondary refuge , since triatomine feces and triatomine aggregation are strongly associated [27]–[29] . In our system there was significant variation in relative humidity and small variation in temperature over time . A greater variability of temperature and relative humidity would be expected in field conditions across different ecotopes and in peridomestic areas , probably influencing the activity and the dispersal of T . infestans , in keeping with our observed increased activity associated with higher temperature . Also , we purposely did not place attractants in the other side of the tanks such as secondary sources of bloodmeals in an effort to mimic situations in which migration would entail seeking an entirely new food source . In areas where animal corrals are close to one another or to rooms where humans sleep most migration might be directed to such an area . Cues from proximal animals or humans would be detected soon after food seeking starts and then traces of those cues would lead the insects directly to the hosts [72] . It is likely then that the observed propensity to disperse when hosts are removed would be even higher in a field environment . Finally , our experimental design included three replicates of the experimental units to avoid the effect of stochasticity , as suggested by Hurlbert [73] . Hurlbert also discusses other study design tools to avoid the problem of pseudoreplication . He emphasizes the need of controls , randomization , independence and interspersion [73] . We applied all those tools in the design and statistical analyses , but there is still potential for dependence of the studied emigration pattern in each tank . As described by Lorenzo and Lazzari[30] , walking triatomines leave traces on the floor , orienting other triatomines in their dispersal . Even though we could not prevent this effect , and the observation over time in each tank might be not be independent , we think our observations “still contain useful information…” , as Hulbert states about some imperfect studies [73] . Our results represent triatomine dispersal in areas of low-animal-corral density , where chemical and physical cues associated with the presence of animals are sparse and diluted , and might , as well , represent initial dispersal of triatomine insects in areas of high-animal-corral density . While dispersal is important even in the constant presence of hosts , there is an important change in the dispersal pattern when hosts are removed . We observed two types of dispersal: close spatial association with original location of removed hosts , and dispersal , seemingly at random , far from the primary refuges and host locations . Our study was not set up to conclude if any ecological or evolutionary theory explains the observed patterns of dispersal; further studies would help to determine the ecological and evolutionary meaning of these dispersal strategies , and could answer if behavioral patterns are the result of bet hedging [74] and/or random individual variation [75] . Also , we focused our study in the interaction between host and vector . The presence of the parasite T . cruzi might change the host-vector interaction; more complex experiments considering the parasite , host and vector are needed to assess this possibility . We used guinea pigs for our experiments , but our results may extend beyond that species . In other areas goats [76] , dogs [8] , and poultry [77] , [78] have been described as host highly associated with the presence of triatomines . Our observed effect of host removal on triatomine dispersal may change depending on the population dynamics of domestic species . Depending on the rate of removal and replacing of hosts , a higher proportion of continuous triatomine dispersal could be observed without sudden increases in insect activity , or on the contrary , questing in corrals that would shortly be repopulated could be more common . The removal of these animal hosts may lead to sudden infestations of surrounding areas by insects looking for other sources of blood meals , increasing the risk of T . cruzi transmission for humans in proximal areas . Further studies are needed to discern adequate strategies to limit T . infestans dispersal in these settings and the associated increase of transmission risk . Additionally , empty animal corrals may remain attractive to the vectors or be used by triatomines as hiding places and should be carefully considered in vector-control activities such as monitoring , insecticide treatment , and housing improvement [12] , [79]–[81] .
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Chagas disease is transmitted by triatomine bugs that actively disperse by walking and flying . The control of this vector-borne disease focuses on reducing or eliminating the insect vector populations . Most transmission of Trypanosoma cruzi , the causative agent of Chagas disease , involves insects living within or very close to households and feeding mostly on domestic animals . As animal hosts can be removed due to migration , slaughter , or death , it is important to understand how host availability can modify transmission risk to humans and to characterize the dispersal of triatomine vectors on a very fine scale . We used a semi-field system to characterize the dispersal of Triatoma infestans , and compare the behavior of vector populations in the constant presence of hosts and after the removal of the hosts . The emigration rate – net insect population decline in original refuges – following host removal was on average 19 . 7% of insects per 10 days compared to 10 . 2% in constant host populations . Activity of insects was significantly increased when hosts were removed . The removal of domestic animals in infested areas increases vector dispersal , possibly towards nearby human sleeping spaces .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"ecology",
"entomology",
"epidemiology",
"vector",
"biology",
"arthropod",
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"life",
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"insect",
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"zoology",
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2015
|
Host-Seeking Behavior and Dispersal of Triatoma infestans, a Vector of Chagas Disease, under Semi-field Conditions
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In most species , and particularly in vertebrates , the percentage of genes absolutely required for survival , the essential genes , has not been estimated . To obtain this estimation , we used the mouse as an experimental model to carry out high-efficiency N-ethyl-N-nitrosourea ( ENU ) mutagenesis screens in two balancer chromosome regions , and compared our results to a third previously published screen . The number of essential genes in each region was predicted based on allele frequencies . We determined that the density of essential genes differs by up to an order of magnitude among genomic regions . This indicates that extrapolating from regional estimates to genome-wide estimates of essential genes has a huge variance . A particularly high density of essential genes on mouse Chromosome 11 coincides with a high degree of regional linkage conservation , providing a possible causal explanation for the density variation . This is the first demonstration of regional variation in essential gene density in the mouse genome .
In the era of complete genomes , the total number of genes in a sequenced organism can now be predicted , but the function and selective importance of a substantial fraction of genes remains unknown . Some gene functions may be of central importance to the organism , whereas other gene functions may be useful , but not critical , or may have functions that are partially redundant . Genes are classified as essential if an organism cannot develop to maturity without them . Here , employing balancer chromosome mutagenesis studies on specific regions of the mouse genome , we evaluate the distribution of essential genes in these regions . Our data also show that in mammals , similar to worms [1] , essential gene clusters are located in genomic regions with high linkage conservation .
Essential genes in two genomic regions were targeted using balancer chromosome screens: a 35-Mb region of mouse Chromosome 11 between the Trp53 and Wnt3 loci [2] and a 20-Mb region of mouse Chromosome 4 between markers D4Mit281 and D4Mit51 [3] . For comparison , we also analyzed results from an earlier mutagenesis study that identified nine essential loci in a 20-Mb deletion region on mouse Chromosome 7 [4] . In our study , we considered essential genes to be those that when mutated cause lethality at or before birth . To improve the accuracy of the analysis , we performed pair-wise complementation tests of fully penetrant mutant lines from each screen to identify alleles at each locus . From 785 pedigrees bred in the Chromosome 11 balancer screen , we isolated 45 mutant lines that die at or before birth ( Table 1 ) . These 45 lines formed 40 complementation groups , and thus only five loci were detected more than once ( Table 1 ) . From 551 pedigrees bred in the Chromosome 4 balancer screen , we isolated 16 mutant lines that die at or before birth ( Table 1 ) . These mutants formed 12 complementation groups ( Table 1 ) . In comparison , the deletion screen on Chromosome 7 bred 4 , 557 pedigrees to generate 24 fully penetrant lethal mutant lines that fell into nine complementation groups [4] . Notably , only a third of the number of pedigree groups were screened on Chromosome 11 as compared to Chromosome 7 . However , we obtained about two and a half times as many mouse lines carrying essential genes , and almost six times as many complementation groups . To predict the number of essential genes in each chromosomal region , we employed a Bayesian approach that incorporates variation in the degree of mutability among loci to provide a credible range of values rather than a point estimate [5] . This analysis requires knowledge of the number of complementation groups in each region , and cannot be applied to studies that fail to consider allelism . Evidential support for gamma and mixture models that incorporate variation in mutability among loci was minimal based on the datasets alone , although previous analyses show that variation in mutability is the norm [5] . When mutabilities vary , genes with low mutabilities tended to be under-counted if a model with a single mutability rate ( Poisson ) is assumed; the numbers of lethal mutations predicted from a Poisson distribution are therefore probably an underestimate [6 , 7] . To obtain an accurate measurement , we considered gamma-distributed mutabilities with the shape parameter constrained to reasonable values ( a = 0 . 2–5 . 0 ) based on previous observations [5] . There were 222 essential genes ( between 98 and 943 based on a very conservative 99% credible region ) predicted in the Chromosome 11 balancer region ( Figure S1A; Table S1 ) . Similarly , 31 essential genes ( 16 to 124 ) were predicted in the Chromosome 4 balancer region ( Figure S1B ) . The Chromosome 7 mutagenesis experiment was more highly saturated , with 12 essential genes estimated ( 10 to 25 , Figure S1C ) . These three regions clearly vary considerably in their density as well as their number of essential genes . The predicted mean density of essential genes per Mb in the Chromosome 11 balancer region is four times greater than the density on Chromosome 4 , and 11 times greater than the density on Chromosome 7 . All density differences between chromosomes are significant , and the chromosome 11/4 density ratio is at least 2 . 26 ( p < 0 . 05 ) , while the 11/7 ratio is at least 7 . 0 ( p < 0 . 05 ) . The number of essential genes predicted in each region is also significantly different ( p < 0 . 05 ) as a proportion of the total number of predicted genes ( 739 , 373 , and 237 , respectively ) . The Chromosome 11 balancer region has unusually high synteny in addition to its high essential gene density: human Chromosome 17 is entirely conserved with this region of mouse Chromosome 11 , making it the most conserved mouse–human autosomal linkage group ( Figure S2 ) . Chromosomes 4 and 7 have less synteny conservation with human chromosomes ( data not shown ) . Although gene density ( as well as essential gene density ) is high on Chromosome 11 , we found that on other mouse chromosomes the relationship between gene density and synteny conservation was weak ( Figure S3 ) . The number of essential genes appears to be predictive of microsynteny and sequence conservation as well as large-scale synteny . We examined homologs among mouse , rat , human , dog , and cow to determine which genes had the same neighbors in all five species , and found that 26% of the genes on mouse Chromosome 11 had conserved microsynteny . In contrast , only 22% of the genes on Chromosome 4 and 13% of the genes on Chromosome 7 had conserved microsynteny in all five species ( Table 1 ) . These frequency differences are significant ( Table 1 ) . At the sequence level , a previous comparison between the C57BL/6J and 129S5 mouse strains demonstrated that Chromosome 11 has much higher sequence conservation than Chromosomes 4 or 7 [8] . Overall , Chromosome 11 is the third most-conserved chromosome between these two strains [8] .
In this first comparative study of essential gene densities in a mammalian genome , we have identified surprising differences as large as an order of magnitude . Our region-specific mutagenesis screens combined with complementation testing were laborious but necessary for these calculations . Our statistical accommodation of variation in mutability , although more complex than most previous studies , allowed a more accurate assessment of the variability in essential gene density . Sequence conservation of regions dense in essential genes is perhaps not surprising , but synteny conservation is more so . A weak correlation between essential gene density estimates and synteny was previously observed in roundworms based on RNAi [1] , but our observations in mammals use a more precise assessment of essential function and a more definitive assessment of large-scale synteny among more species , as well as an assessment of microsynteny . Thus , it is reasonable to consider a general causal relationship between essential genes and reduced rates of chromosomal translocation and rearrangement . If adjacent essential genes generally reduce the probability of productive chromosomal translocations between them , essential gene-dense regions would be expected to expand over time as essential genes randomly join a cluster , but then have a reduced probability of departing . Thus , it appears that the large number of densely packed essential genes on the balancer region of mouse Chromosome 11 may have forced it to remain as a unit in spite of millions of years of divergence and speciation . This also predicts that syntenically conserved regions should be especially attractive targets for future essential gene detection . It is traditional to use regional estimates of essential gene density to estimate the total number of essential genes in the genome . If we extrapolate the number of essential genes as a proportion of predicted genes in each region , there would be 5 , 749 essential genes overall ( 20% of the genome ) . If we extrapolate based on the density of essential genes per Mb , we predict about twice as many ( 10 , 849 ) . The results of our own research , however , indicate that the variability on this extrapolation is huge . If the variability of the regional estimates , as well as the variability among the regional estimates ( up to 11-fold ) , is taken into account , the estimate ranges from ∼1 , 100 essential genes up to more genes than the total predicted number of genes in the genome ( 28 , 594 ) . It is a near certainty that such variability is not specific to our study , but applies to all previous estimates of essential genes that utilized one or a few genomic regions . If the relationship between essential genes and synteny , particularly microsynteny , is consistently upheld in a variety of organisms , more accurate and believable estimates could be obtained by using microsynteny and conservation in essential gene predictions .
The fraction of lethal mutations remaining to be isolated from each screen was calculated using Saturate [5] . We considered gamma-distributed mutabilities with the shape parameter constrained to reasonable values ( a = 0 . 2–5 . 0 ) based on previous observations . For the gamma model , alpha was constrained to be less than 5 . 0 . Genomic sequences of mouse , human , chimp , rat , cow and dog were downloaded from Ensembl v . 38 ( http://www . ensembl . org/info/data/download . html ) . Each region of mouse sequences was divided into 150-kb fragments , which were then blasted using Megablast ( http://www . ncbi . nlm . nih . gov/BLAST/download . shtml ) . The sequence comparison was carried out on a Sun cluster with SunFire 280R ( http://www . sun . com ) . Mouse genomic annotation was downloaded from Ensembl BioMart v . 38 ( http://www . ensembl . org/Multi/martview ) . To visualize the blast results , we developed in-house software written in Microsoft Visual Basic ( http://www . microsoft . com ) . All blast results were uploaded in a MS SQL server database , and the results displayed on a PC . Microsynteny comparisons were performed using gene annotation from Ensembl Biomart v . 38 . A list of genes with conserved microsynteny will be provided upon request . An explanation of calculations is found in Table S2 . All predictions are based on protein-coding known genes found in Ensembl Biomart v . 39 . The extremes of two distributions such that they were as similar as possible but the joint probability was no less than 5% was taken to obtain the minimal ratio of the two essential gene predictions . In no case did the density distributions overlap with greater than 5% probability .
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The genome sequences of many organisms are now complete . However , speculation remains regarding the function of many newly discovered genes . There is also debate about the percentage of genes that are required to build an organism . These genes , which are necessary for the development of the organism , are essential genes . We have performed mutagenesis screens that allow the identification of mutations in essential genes from specific regions of the mouse genome . From these data we have predicted the number of essential genes in three regions of the mouse genome . When we compared these predictions , we found that the density of essential genes varies in different regions of the mouse genome . We then analyzed these regions of the genome to identify similar regions in other mammals . We found that regions of the mouse genome with a high density of essential genes are more similar to other species than those regions with fewer essential genes , suggesting that throughout evolution genomic regions with many essential genes remain intact .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"mus",
"(mouse)",
"genetics",
"and",
"genomics"
] |
2007
|
Regional Variation in the Density of Essential Genes in Mice
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Alu elements are trans-mobilized by the autonomous non-LTR retroelement , LINE-1 ( L1 ) . Alu-induced insertion mutagenesis contributes to about 0 . 1% human genetic disease and is responsible for the majority of the documented instances of human retroelement insertion-induced disease . Here we introduce a SINE recovery method that provides a complementary approach for comprehensive analysis of the impact and biological mechanisms of Alu retrotransposition . Using this approach , we recovered 226 de novo tagged Alu inserts in HeLa cells . Our analysis reveals that in human cells marked Alu inserts driven by either exogenously supplied full length L1 or ORF2 protein are indistinguishable . Four percent of de novo Alu inserts were associated with genomic deletions and rearrangements and lacked the hallmarks of retrotransposition . In contrast to L1 inserts , 5′ truncations of Alu inserts are rare , as most of the recovered inserts ( 96 . 5% ) are full length . De novo Alus show a random pattern of insertion across chromosomes , but further characterization revealed an Alu insertion bias exists favoring insertion near other SINEs , highly conserved elements , with almost 60% landing within genes . De novo Alu inserts show no evidence of RNA editing . Priming for reverse transcription rarely occurred within the first 20 bp ( most 5′ ) of the A-tail . The A-tails of recovered inserts show significant expansion , with many at least doubling in length . Sequence manipulation of the construct led to the demonstration that the A-tail expansion likely occurs during insertion due to slippage by the L1 ORF2 protein . We postulate that the A-tail expansion directly impacts Alu evolution by reintroducing new active source elements to counteract the natural loss of active Alus and minimizing Alu extinction .
Long INterspersed Element-1 ( LINE-1 or L1 ) and the Short INterspersed Element ( SINE ) Alu are non-long-terminal-repeat ( non-LTR ) retroelements that are responsible for approximately one third of the human genome [1] . Due to their ability to randomly insert throughout the genome [2] , both L1 and Alu are capable of disrupting critical genes and causing a large diversity of genetic diseases [3]–[6] . The creation of an engineered L1 assay system specifically designed to rescue de novo L1 inserts in a culture system demonstrated that L1 insertion contributes significantly to genetic instability through retrotransposition-mediated deletions and rearrangements [7]–[10] . This assay has the added advantage of providing a valuable tool for analyzing aspects of the L1 insertional mechanism under controlled experimental conditions [11]–[13] . Computational analyses further corroborated that both Alu and L1 insertions are associated with genomic loss , rearrangements and structural variation in humans [14]–[16] . Prior to our development of a similar assay system for SINES , there are very few published details of recovered de novo SINE insertions in culture . Two previous reports account for a total of 12 fully characterized de novo Alu insertion events in culture [17] , [18] . One of these approaches utilized an untagged AluSx to transfect cells and the Alu inserts were then detected by “panhandle” PCR amplification using an anchor that is attached to the restriction digested cellular DNA . The researchers evaluated a total of 101 PCR products and found that seven were bona fide Alu insertion events [18] . The other five Alu insertion events were recovered using a tagged Alu and inverse PCR approach [17] , [18] . An additional published report describes eight inserts from two tagged rodent SINEs [19] . Thus , only 20 de novo SINE inserts from cell culture have been characterized prior to the work reported here . Because these data arose from different approaches , using different SINEs , and different cell lines , generalizations from the data become difficult . New high-throughput approaches have yielded large amounts of data on mobile element insertion , including somatic events observed in cancer samples [20] and brain [21] . However , these approaches are limited by short sequence reads , the inability to sequence through homopolymeric A-tails , and high difficulty of recovery and validation of “singleton” events ( very rare events , i . e . , somatic insertions ) due to the inability to refer back to a reference clone . Although in silico and high throughput sequencing analyses provide valuable insights into retroelement activity , a tissue culture assay system provides a controlled genetic environment during retrotransposition that confers the ability to distinguish between retrotransposition-mediated events and those that occur post-insertionally with the added advantage of being able to manipulate SINE sequences for experimental evaluation . Here , we present the adaptation and development of an engineered recovery-construct that allows for the rescue of inserted tagged SINE elements in a tissue culture assay system and provide detailed data from over 200 rescued de novo Alu inserts .
Because SINEs are transcribed by RNA polymerase III ( pol III ) , several obstacles introduced by the RNA pol III transcriptional requirements must be overcome to develop experimental methods to investigate the mechanistic aspects of Alu retrotransposition . Due to these constraints , prior methods for the recovery of SINE inserts in culture have been mostly limited to inverse PCR [17] , [18] . As an alternate approach , we have developed an Alu recovery system by redesigning the existing Alu-neoTET vector [17] , following the strategy used to create the L1 recovery vector [7] , [8] . The principle of the method is shown in Figure 1A . We performed extensive modifications and adaptations of the Alu construct [17] ( Figure 1B ) . First , a bacterial promoter ( EM7 ) was inserted upstream of the neoTET cassette to obtain kanamycin resistance in bacterial cells . We then introduced a minimal γ origin of replication ( 305 bp ) of plasmid R6K [22] , [23] , which was sequence modified to allow RNA polymerase III ( pol III ) transcription . The R6KγORI was selected due to its smaller size . Specific sections of the R6KγORI were changed by site directed mutagenesis to eliminate runs of four or more thymidine residues that could function as internal RNA pol III terminators ( details in Materials and Methods ) . Finally , in order to analyze A-tail expansion , we substituted the original homopolymeric A-tail with a dA-rich sequence containing non-A disruptions ( Figure 1B ) . As expected , the added sequence length ( 439 bp ) and the variation in A-tail length and composition [24] reduced the retrotransposition efficiency of the Alu rescue construct to close to 50% when directly compared to the parental construct ( Figure 1C ) . The retrotransposition efficiency of the Alu rescue construct increases when using a highly efficient driver vector expressing only L1 ORF2p ( Figure 1C ) . However , the added length to the tagged Alu RNA did not appear to contribute to 5′ truncation of the Alu inserts , as fewer than five percent were truncated ( see details below ) . We recovered a total of 226 Alu inserts from transfected HeLa cells ( complete sequence details of the insertions are available in Text S1 and Table S1a ) . Because transfection of the L1 ORF2 protein alone supports Alu retrotransposition in HeLa [17] , [25] , we wanted to determine if ORF2-driven Alu inserts differed from those driven by a full length L1 . We analyzed de novo Alu inserts driven by ORF2 alone ( N = 178 ) or driven by full-length L1 ( N = 48 ) for comparison between the sets . Overall , we found no significant differences between Alu inserts driven by full-length L1 vs . ORF2 alone ( Tables 1 and 2 and Figure 2 ) . Therefore , we primarily report the combined observations of all Alu inserts . We obtained sequences from both 5′ and 3′ genomic flanking sequence of the inserts ( Text S1 and Table S1a ) . Of the fully characterized de novo Alu inserts , the vast majority ( ∼96% ) exhibited the hallmark characteristics of retrotransposition: direct repeats flanking the insert , a 3′ oligo dA rich tail and a target site resembling the L1 endonuclease consensus sequence [7] , [9] , [26]–[28] . Atypical insertions ( lacking the retrotransposition hallmarks ) were associated with genomic deletions or rearrangements ( details below ) . The observed target consensus site for the inserts ( 5′-TTTT/AA-3′ ) is identical to the known preferred L1 endonuclease cleavage site [8] ( Figure 2A ) , suggesting that most Alu inserts in our culture system initiated by the conventional endonuclease-dependent target primed reverse transcription ( TPRT ) mechanism . The direct repeats ranged from 5–27 bp , with a 14 . 0±3 . 0 bp average ( Table 1 ) . Eight of the recovered events ( 3 . 5% ) resulted in an Alu insert with a 5′ truncation . This is less than half of what is observed in the genome ( ∼10% of Alu elements are 5′ truncated ) [1] , [29] . As proof of the versatility of the method , we modified our construct to the study of other SINE elements . We recovered seven inserts from two rodent SINEs by substituting the BC1 or B2 sequences for the Alu sequence in the rescue vector [30]– . Sequence analysis revealed that the fully characterized de novo inserts ( five B2 and one BC1 ) also contained the endonuclease target site and insertion characteristics of typical L1-mediated retrotransposition ( Text S2 and Table S1c ) Our analyses of the recovered Alu inserts determined that less than four percent of the inserts ( 8 of 226; 3 . 5% ) lack the typical characteristics of TPRT-mediated Alu insertions . Six of these insertions ( 2 . 7% ) contain two features indicating that the insertion likely completed through recombination with an existing Alu present at the genomic site ( Text S1 and Table S1a ) . First , the recovered sequences of these clones contain a chimeric sequence between the genomic and the tagged Alu . Secondly , they lack the characteristic flanking direct repeat . In several cases , the recombination caused a loss or a rearrangement of the genomic sequence ( Figure S1 ) . This type of retrotransposition mediated deletion has been previously described for L1 [7]–[9] and Alu [14] , [34] . For one particular example , clone 57 , the immediate 3′ and 5′ genomic sequences flanking the insert are 99 kb apart in the reference genome assembly . PCR analysis of the transfected and untransfected HeLa DNA confirmed that this genomic rearrangement was not pre-existing in the HeLa cell line , but instead is likely associated with the Alu insertion ( Figure S2 ) . Interestingly , clone 57 is the only insert in our data set with no identifiable A-tail . An additional two inserts of the fully characterized Alus ( 0 . 8% ) also lacked the canonical endonuclease cleavage sites and direct repeats of TPRT insertion ( clones 108 and 203 ) , suggesting an endonuclease independent mechanism of insertion [11] , [35] . These clones were also associated with potential genomic rearrangements ( details in Text S1 ) . We used the 5′ flanking genomic sequence from the 226 rescued inserts to determine their genomic location . Alu insertions were recovered from all chromosomes ( Figure 2B ) . The distribution of Alu inserts across chromosomes appears largely random ( Figure 2C ) , in agreement with previous reports of L1 insertions from tissue culture [9] . A previous study showed an enrichment of L1 inserts associated with the c-myc gene on chromosome 8 [36] . However , we did not observe Alu insertions associated with the c-myc gene . We analyzed the G+C and repeat element sequence content of the pre-insertion loci in 20 kb intervals of all 226 Alu inserts ( Table 2 ) . Relative to the genomic average and modified HeLa karyotype , we find that the overall pattern of Alu pre-insertion sites is consistent with a previous analysis of de novo tagged L1 inserts [36] . Pre-insertion sites were Alu rich and L1 poor , although the tagged L1s inserted into comparatively more L1 poor regions ( 13 . 3% for L1 inserts from Gasior et al . 2006 compared to 15 . 5% for Alu inserts in the present study ) . Alu insertions that were driven by ORF2 alone landed in genomic regions that were more L1 poor than insertions driven by full-length L1 ( 13 . 9% compared to 17 . 0% ) . However , this observed difference is not statistically significant ( two sample , two-tailed t-test , p = 0 . 172 ) . We next assessed the distribution of recovered inserts relative to annotated genes in the human reference genome . We find that 57 . 7% of all combined inserts landed in genic sequence compared to 42 . 3% that were intergenic ( Table 3 ) . As previously indicated , there is no significant difference between the genic/intergenic distribution of L1 and ORF2 driven Alu inserts ( Pearson X2 = 3 . 41; p = 0 . 065 ) . Six of the Alu inserts landed within exons , but only two caused disruption of coding sequences , while the other four landed in the 5′ or 3′ untranslated regions ( UTRs ) of coding exons ( Table 3 ) . Just over a third ( 36 . 2% ) of genic de novo Alus inserted in the sense strand , compared to ( 63 . 8% ) on the opposite strand . This observation is slightly more skewed than the 55% antisense to 45% sense strand intronic distribution of the sequenced human genome [37] , [38] . Overall , these data are consistent with an antisense bias ( binomial probability , p = 0 . 002 ) . To further analyze Alu insertion preferences , we assessed the de novo Alu inserts relative to features that were found to associate with the genome-wide distribution of Alus in a previous evolutionary analysis [39] . In this approach , the 226 de novo Alu inserts observed here are localized within a system of 2765 non-overlapping human genome 1 Mb windows as employed in [39] and statistically evaluated for association with previously described genomic features ( details in Materials and Methods ) . Nine genomic features were selected to evaluate genome landscape , recombination and natural selection ( details in Table 4 and Table S2 ) . For each feature , we contrasted 203 insert-containing windows and the 2562 insert-free windows , using the Mann-Whitney-Wilcoxon test ( see Materials and Methods ) . After Bonferroni correction for multiple testing , our results ( Table 4 ) indicate that the de novo Alus integrated in genomic regions that: ( a ) are rich in genes and highly conserved elements ( suggesting function ) , ( b ) have high GC-content , ( c ) contain a 13-mer associated with recombination hotspots and genome instability ( Myers et al . 2008 ) and ( d ) are enriched with SINEs , confirming that our observations of the 2-kb flanking regions ( Table 2 ) may extend up to 1 Mb . We repeated the analysis using random subsets of the de novo inserts and the results remained consistent ( data not shown ) . Some transcripts containing Alu sequences have been reported to be subjected to RNA editing [40]–[43] . However , these examples refer to Alu sequences within RNA pol II generated transcripts . Thus , we evaluated our data for evidence of editing of RNA pol III transcripts . A total of 52 , 039 bp of de novo Alu inserts were analyzed , which excluded the middle A-rich region of the Alu sequence from the analysis . We only found six point mutations ( ∼0 . 01% ) , three clustering within a 20 bp sequence of a single Alu insert . None of the changes reflected the expected sequence changes due to RNA editing and may reflect errors introduced during reverse transcription by the L1 ORF2 or random mutations . Our observations are consistent with previously published data showing no evidence of editing by three APOBECs ( 3A , 3B or 3G ) on the neomycin cassette sequence from inserts of a tagged Alu [44] , [45] . An intriguing observation associated with SINE insertion events is the reported increase in A-tail length of new inserts relative to their source element [17]–[19] . We used constructs with non-A disruptions within the A-tail to further investigate the underlying mechanisms of A-tail expansion in recovered de novo Alus . We used two constructs containing different A-tails ( Figure 1B ) to determine if differences in A-tail disruptions or length might differentially affect new insert A-tail sequence . The shorter A-tail construct ( A30D ) contains three polyA segments of 10 adenosines , separated by two different disruptions ( CT and TAC , Figure 1B ) . The longer A-tail construct ( A70D ) is more than twice as long as the A30CT tail ( 82 bp compared to 35 bp ) and contains four polyA segments of 17 or 18 adenosines separated by three different disruptions ( CATTAC , G , and CACAC , Figure 1B ) . We fully analyzed A-tail sequence data from 14 Alu inserts generated from the construct with the short A30D A-tail and 91 inserts from the longer A70D construct ( Figure 3 ) . Overall , the de novo Alu inserts showed extensive A-tail expansion relative to the parental Alu . A-tail and insert characteristics for the individual inserts are detailed in Table S1b . Because the length of the A-tail at the 3′ end of the recovered inserts can vary depending on where priming occurs within the RNA molecule during TPRT ( see Figure 3A ) , we grouped inserts based on the priming location . Internal priming has previously been observed for L1 inserts [2] . Priming location was inferred by the absence/presence of the non-adenosine disruptions . We define polyA segments of new inserts as “terminal” when the segment is used as the priming location for TPRT . Note that the “terminal” polyA segment of a new insert can be any one of the polyA segments from the reference parental element ( shaded orange in Figure 3C ) and that internal priming events can generate inserts with shorter individual polyA segments as well as shorter A-tails in general . Figure 3C shows examples of the four types of A-tails generated by construct A70D . Although the A30D data set is much smaller , many of the observed characteristics were shared between both data sets . Figure 3B shows the A-tail length results for the A30D data set ( data for the larger A70D set is provided in the Table S1b ) . Surprisingly , when the construct with this shorter A-tail was used , all but two Alu inserts ( #123 and #125 ) primed at the most 3′ end polyA segment ( Figure 3B ) . These two Alu30D inserts with A-tails lacking one or both of the non-adenosine nucleotide disruptions were likely the result of internal priming during TPRT ( as illustrated in Figure 3A ) . In contrast , the majority of the priming occurred internally in the A70D dataset , but very few primed in the first or most “internal” polyA or segment #1 ( 8 out of 91 inserts , Figure 3D ) . The A30D and A70D data sets are significantly different with respect to having “complete” A-tails ( all disruptions and polyA segments present ) ( Pearson X2 , p<0 . 001 ) . It is possible that the added length of the A70D A-tail led to an increased frequency of internal priming by expanding the available area for priming to occur . In both sets , priming seldom occurred at a distance of less than 20–25 bp downstream from where the polyA segment initiates . The A70D data set also has significantly fewer than expected priming events within the most 3′ polyA segment , under the null hypothesis that priming locations are randomly distributed across the A-tail ( Chi-square goodness-of-fit , p<0 . 0001 ) . We observed significant extension of the polyA segment length in both data sets . Closer inspection of the individual segment sizes revealed that the terminal segments are considerably longer than internal segments . The median terminal segment length ( 41 . 5 bp ) for the A30D set is about 4 times longer than the median for internal segments ( 11 bp ) ( Mann-Whitney U test , p<0 . 0001 ) . Similar to the A30D data , the 91 A-tails from the A70D data set showed a bias to 3′ end elongation of the inserts when the length of the internal polyA segments is compared to terminal segments ( Figures 3D and E ) . The histogram ( Figure 3E ) shows the overall size distribution of all four polyA segments , separated into internal ( white bars ) vs . terminal ( black bars ) segments . Although both internal and terminal polyA segments increased in length , terminal segments are significantly longer ( medians of 23 . 0 and 42 . 0 , respectively; Mann-Whitney U test , p<0 . 0001 ) . Almost all of the A70D terminal polyA segments ( 95 . 9% ) show expansion of four adenosines or more , while just over half of the internal segments exceed this level of expansion ( 55 . 6% ) . Although there is a bias toward larger expansions occurring at terminal segments ( gray bars , Figure 3D , Table S1b ) , all of the internal polyA sections showed at least a minor increase in length relative to parental segments ( indicated by the dashed horizontal line , Figure 3D ) with medians of 22 or more adenosines per polyA segment . In contrast , shortening only occurs in terminal segments , as we observed 17 inserts with shorter terminal polyA segments than the parental construct ( Figure 3E , black bars left of the vertical dashed line and Table S1b ) . This suggests that the shorter terminal A-stretches may be a result of internal priming within the terminal polyA segments during the initial step of reverse transcription by ORF2p ( Figure 3A ) . To determine if the observed A-tail expansion may have occurred at the RNA level , we generated cDNA clones using 3′ RACE ( RT-PCR ) from isolated RNA of transiently transfected cells using either construct specific primers or a generic anchored oligo-polydT primer ( details in Materials and Methods ) . Sequence analysis of these clones clearly showed that the insert A-tail elongation could not be explained by RNA transcript variation . We observed only slight transcript sequence differences of 1–3 adenosine losses or gains per polyA segment ( Figure S3 ) . Moreover , we observed more than twice as many adenosine losses than gains in the cloned cDNA sequence derived from the transcripts , suggesting that the A-tail variation introduced by transcription or by our recovery and cloning methodology is more likely to lead to shorter A-tails . Analysis of clones recovered from PCR amplification of a DNA template also revealed a similar change in adenosine numbers ( Figure S4 ) , possibly indicating that these sequence differences in the cDNA are introduced during the bacterial growth or amplification steps during the RT-PCR steps of the 3′ RACE and are not reflective of the actual RNA sequence . It is noteworthy that we did not observe the large adenosine amplifications in our analysis of RNA transcripts , making it unlikely that changes in the Alu RNA template are a significant mechanism for the A-tail expansion observed in our recovered clones . During the Alu rescue process , many of the loci containing the Alu inserts were independently recovered multiple times . If expansion of polyA segments is an artifact of the cloning process , we would expect to see segment length variation between independently recovered samples . Instead , we observed minimal sequence variation between the recovered samples derived from the same Alu insert . In eight randomly chosen A-tail examples with a combined 2444 bp , only one sample with a single adenosine insertion was observed ( Figure S5 ) . This observation is in stark contrast to the consistent and large A-tail length expansion of the Alu inserts . Our data strongly indicate that the recovery assay is unlikely to contribute to the large A-tail expansions observed .
Our SINE recovery method provides a complementary approach for comprehensive analysis of the impact of Alu on the human genome that can give novel insights into the biological mechanisms governing SINE amplification . In summary , the recovery of de novo tagged Alu inserts in HeLa cells revealed that ( 1 ) L1 and ORF2 driven Alu inserts are indistinguishable in human cells; ( 2 ) Alu insertion mediated deletions and rearrangements lack the hallmarks of retrotransposition , likely due to an alternate mechanism to resolve insertion intermediates; ( 3 ) inserts show an apparently random distribution across chromosomes , although a bias exists favoring insertion near other SINEs , highly conserved elements and genes; ( 4 ) de novo Alu inserts show no evidence of RNA editing; ( 5 ) TPRT priming was not observed within the first 20 bp ( most 5′ ) of the A-tail , suggesting the possible interference of bound proteins to the transcript or an unknown spacing requirement needed to engage the RT , RNA and priming sequence; ( 6 ) L1 ORF2 protein may show slippage during reverse transcription , leading to the expansion of de novo Alu element A-tails; and ( 7 ) expansion occurs across the entire length of the A-tail , but with a bias toward the 3′ end . A major advantage of our approach is the ability to study inserts that have experienced little or no selection and the ability to compare de novo inserts to the known reference source element . By comparing inserts from our tissue culture system to genomic Alu inserts , we can better understand how selection has shaped the current distribution of human Alu sequences . Our results indicate that different genomic features might be important for initial Alu integration , as studied here , vs . long-term evolutionary survival of Alu insertions in the genome [39] . In particular , here we show that Alus integrate in genomic regions rich in genes and in sequences categorized as “most conserved” [46] , suggesting an integration preference into or near functional elements . The association of Alu integrations with gene-dense regions is intriguing and is consistent with the previously reported enrichment of Alus near housekeeping genes [47] , [48] . Although speculative , this integration preference suggests Alu is a highly efficient mutagen of human genes . In addition , targeting gene rich regions may provide fertile ground for added damage due to genomic rearrangements generated during insertion [49] . Interestingly , among these significant features , only enrichment of the genome instability 13-mer [50] was also a significant positive predictor of the distribution of human-specific AluY elements , as identified in an evolutionary analysis [39] . This suggests that , except for this one common predictive feature , there are substantial differences between Alu integration and fixation preferences; while the present analysis largely captures integration , the published Alu distribution properties [39] reflect both integration and fixation . Increased Alu insertion near other SINEs provides a mechanism explaining the clustering of Alus in the human genome [51] and the common occurrence of tandem Alu inserts [52] . Having a higher density of Alu elements may facilitate non-allelic homologous recombination ( NAHR ) , leading to the uneven genetic exchange between alleles that cause both deletions and duplications [49] . The importance of the genome instability 13-mer motif correlating with both Alu distribution and integration is that it highlights a convergence of recombination hotspots and high Alu density regions potentially contributing to Alu-mediated NAHR [49] , [53]–[55] . Consistent with the observations obtained from genomic data mining [56] , we have found that Alu retrotransposition is associated with genomic deletions and rearrangements . However , the lack of the structural retrotransposition hallmarks suggests that alternate means of resolving retrotransposition intermediates , such as recombination [8] , [9] , [14] , [57] or non-homologous end joining [7] , [8] , [58] is likely contributing to the Alu-mediated genomic rearrangements/deletions . Overall , our findings validate the tissue culture system as a robust method to study SINE biology . An important feature of our Alu rescue system is that we were able to directly compare de novo Alu insert A-tails to the parental source A-tails with engineered disruptions . This approach allowed us to determine that A-tail elongation occurs during reverse transcription by ORF2p , leading to expansion across the length of the A-tail , but with disproportionate expansion closer to the 3′ end . The portion of the A-tail used for base pairing during TPRT priming was likewise not random , with the majority of priming locations at least 25 or more bases away from the 5′ end of the A-tail . This priming location preference may reflect a physical constraint such as bound proteins that limit where annealing for reverse transcription can occur . Although speculative , a potential protein candidate could be polyA binding protein ( PABP ) , which is known to associate with SINE RNPs [59] , [60] . We present a model of slippage by ORF2p during TPRT ( Figure 4A ) favoring A-tail expansion . We propose that the beginning of TPRT only provides a weak interaction between the Alu transcript and the cleaved DNA strand through limited hydrogen bonding between base pairs . At this early stage , the complex may become dissociated , pausing reverse transcription until the interaction is re-established in a manner somewhat reminiscent of the reiterative synthesis of telomerase during reverse transcription . This is similar to the model proposed for the I factor , a non-LTR element in Drosophila [61] . In addition , telomerase slippage has been reported in Saccharomyces [62]–[64] , T . thermophila [65] and Candida albicans [66] . Previous in vitro data also highlighted similarities between the L1 protein and telomerase by demonstrating that L1 ORF2 can initiate reverse transcription on oligonucleotide adapters simulating telomere ends [67] . Our model depicts two non-mutually exclusive mechanisms by which slippage can occur: either ( 1 ) complete dissociation occurs followed by re-annealing , or ( 2 ) partial dissociation occurs , causing the cDNA strand to “loop out” before base pairing can once again secure the complex . Interestingly , previous observations on the reverse transcription activity of the Bombyx mori R2 element demonstrate the incorporation of additional nucleotides that appear to arise from multiple rounds of the reverse transcriptase engaging the 3′ end of full length RNA templates [68] . However , potentially untemplated residues can be incorporated depending on the length and composition of the extreme 3′ end of the RNA . As cDNA length increases , the additional hydrogen bonding between the molecules stabilizes the process and reduces or eliminates slippage . This increased stability with cDNA extension provides a simple explanation for our observation of preferential 3′ A-tail expansion , as the probability of dissociation and expansion diminishes as the nascent cDNA strand grows in length . In order for our model to favor A-tail expansion over shortening , re-annealing and/or “looping out” must preferentially occur as depicted in Figure 4A to duplicate A-tail nucleotides rather than delete them . Specifically , re-annealing of the cDNA strand must be further 3′ on the Alu RNA strand , with the cDNA strand looping out . We propose that the presence of proteins bound to the Alu RNA could affect re-annealing dynamics . For example , a potential candidate is poly ( A ) binding protein ( PABP , as shown in Figure 4A ) , which may play an important role in favoring A-tail sequence duplication over deletion , serving as a physical barrier that promotes 3′ re-annealing and/or prevents the Alu RNA from looping out . Because our construct contains non-adenosine residues , sequence duplications can be easily identified . We recovered five Alu inserts with duplicated non-A disruptions in the A-tail sequence ( Figure 4A ) . Duplication of 3′ sequences was previously observed for a recovered L1 sequence [9] , indicating that this type of event also occurs during L1 insertion . Several data support our proposed model . First , no expansion of the polyA segments is observed at the RNA level . Second , A-tail expansion occurs across all polyA segments . These observations are not consistent with RNA polyadenylation or template switching , as these processes would lead exclusively to expansion of the terminal polyA segment . Finally , duplications of the non-A disruptions are a strong indicator of slippage . Although polyadenylation of Alu transcripts and template switching may occur , our data indicate that these types of events are not the main processes contributing to the A-tail expansion of de novo Alu inserts in this assay system . In contrast to L1 , A-tail expansion of new Alu inserts has a significant biological impact on the perpetuation of active Alu elements in the human genome ( Figure 4B ) . Although there are over one million Alu elements in the genome , the vast majority are inactive and unable to generate new copies . Several factors , including intrinsic nucleotide composition and adjacent genomic sequences , determine Alu retrotransposition capability [24] , [69] . One such requirement for efficient Alu retrotransposition is the presence of an A-tail [70] . Because RNA polymerase III transcribed Alu RNA does not undergo enzymatic polyadenylation like mRNAs , Alu depends on the 3′ encoded polyA sequence to generate A-tail containing Alu transcripts . Previous work has shown that A-tails of individual Alu elements mutate rapidly leading to smaller and more heterogeneous tails [71] , [72] and limiting retrotransposition capability [24] . As time progresses , the A-tails of active Alu source elements shrink and degrade , decreasing their ability to support retrotransposition . Therefore , without the reintroduction of new Alu copies with expanded A-tail sequence to counteract the rapid evolutionary loss of homogeneity and length , active Alu copies would be lost , leading to the eventual extinction of Alu . There are precedents for SINE extinction such as in the sigmodontine rodents , where SINE extinction may have preceded LINE extinction [73] . The acquisition of a longer A-tail by new inserts serves an important function in maintaining Alu activity through time and preventing the extinction of Alu or other A-tail dependent SINEs . Additionally , A-tail expansion can explain the appearance of “stealth-driver” Alu elements that have contributed to Alu expansion [74] . Thus , the L1 ORF2 protein is not only essential for Alu retrotransposition , but also plays a critical role in Alu perpetuation by expanding the A-tail of new inserts .
pBS-L1PA1CHnotag- contains the fully codon optimized L1RP driven by the CMV promoter and flanked at the 3′end SV40 polyadenylation signal in pBluescript [75] . pBud-ORF2CH- contains the fully codon optimized ORF2 from pBS-L1PA1CHnotag , cloned into the expression vector pBudCE4 . 1 ( Invitrogen ) , under control of the CMV promoter . pBS-Ya5rescue-A70Du is derived from pAluYa5-neoTET [76] by substituting the 3′ region with a commercially synthesized sequence ( Blue Heron biotechnology Inc . ) schematic of the plasmid is shown in Figure 1B . The changes to Alu-neoTET , include the introduction of a bacterial promoter ( EM7 , 134 bp ) upstream of the neoTET cassette to obtain kanamycin resistance in bacterial cells and the introduction of our modified version of the minimal γ origin of replication ( ORI ) of plasmid R6K [22] , [23] . We selected the R6KγORI for two reasons: first , its small size ( 305 bp ) helps minimize transcript length , and second , it has the fewest poly-T runs of all ORIs evaluated . Two sections of the R6KγORI were changed by site directed mutagenesis to eliminate RNA pol III terminators . The 3′end contains a non-homogeneous 80A-tail , the BC1 unique ( “u” ) region and a pol III terminator ( Figure 2C ) . pBS-Ya5rescue-A70D , -the BC1 unique region of the pBS-Ya5rescue was removed by PCR but still contains the A-tail with the three disruptions . pBS-Ya5rescue-A70D-SH , -the Shine-Dalgarno sequence was modified to remove AT richness that could function as a RNA polymerase III terminator [77] from pBS-Ya5rescue-A70D . pBS-Ya5rescue-A30D- the A-tail of pBS-Ya5rescue was replaced by 30 adenine run with two disruptions , ( details in Figure 2C ) . pBS-B2rescue-A70D the 7SL-Alu sequence of pBS-Ya5rescue-AT was replaced by the 7SL-B2 sequence of pB2-neoTET [76] , [78] . pCEP-Ya5rescue-AT , -the complete tagged Alu rescue sequence was introduced into the SalI sites of the pCEP4 ( InVitrogen ) that removes the multicloning site with its promoter and polyadenylation signal , using a PCR approach to add compatible SalI overhangs to the amplicon . Plasmids were purified by alkaline lysis and twice purified by cesium chloride buoyant density centrifugation . DNA quality was also evaluated by the visual assessment of ethidium bromide stained agarose gel electrophoresed aliquots . Site directed mutagenesis of the R6Kγori in the pR6Kan plasmid ( Epicentre Biotechnologies ) was performed using the commercially available Stratagene kit following the manufacturer's recommended protocol . Changes were introduced sequentially using the following primers in independent reactions: 1st site: 5′-AGTTGCTGATTTATATTAATATTATTGTTCAAACATGAGA-3′ and 2nd site: 5′- AAGCCTTATATATTCTTVTTVTTCTTATAAAACTTAAAACC-3′ ( See Figure 2B ) . The final sequence of the construct resulted in the first V = G and the second V = C . The nucleotides targeted for mutagenesis are underlined . These primers were specifically designed to eliminate any four contiguous thymidines that may function as RNA polymerase terminators . Individual clones were grown and sequenced to confirm the introduction of the desired nucleotide changes . Because R6Kγori is the only origin of replication of the plasmid used in the mutagenesis , only functional mutations yield bacterial colonies , eliminating the need to verify functionality of our mutated sequences . The basic transient Alu retrotransposition assay was performed as previously described with some minor modifications [17] . HeLa cells ( ATCC CCL2 ) were seeded in T-75 flasks at a density of 1×106 cells/flask . Transient transfections were performed the next day using the Lipofectamine and Plus reagent ( InVitrogen ) following the manufacturer's protocol using 10 µg of the Alu rescue vector plus 2 µg ORF2 expressing vector or 2 µg of the untagged L1 vector . Following the removal of transfection cocktail , the cells were grown for 24 hr before adding the media containing 400 µg/ml G418 ( Fisher Scientific ) for selection . To determine evaluate retrotransposition , colonies were stained after 14 days of growth in selection media . To recover Alu inserts , the G418 resistant cells were grown under selection for 14–26 days to produce enough replicated cells for DNA isolation and the Alu insert recovery procedure . Fully confluent flasks of expanded G418 resistant cells were trypsinized and centrifuged in a new tube to be used for DNA extraction . DNA extraction was performed using the DNA-Easy kit ( Qiagen ) following the manufacturer's recommended instructions . We used a modification of a previously described protocol [13] . Briefly , 200 µg of extracted DNA was digested for at least 5 hours at 37°C with 200 U of HindIII , EcoRI , SpeI , BsrGI , NheI or NdeI followed by heat inactivation of the enzyme by incubating at 65°C for 20 minutes . The digested DNA was diluted to a final volume of 1000 µl containing 1X T4 DNA ligase buffer and 1200 U T4 DNA ligase and incubated overnight at 16°C . After ligation , the sample was concentrated using a Microcon YM-50 filter ( Amicon ) , washed twice with 500 µl distilled water and concentrated to a final volume of approximately 20 µl . The sample was incubated with 50 µl of electrocompetent E . coli pir-116 [F− mcrA Δ ( mrr-hsdRMS-mcrBC ) φ80dlacZΔM15 ΔlacX74 recA1 endA1 araD139 Δ ( ara , leu ) 7697 galU galK λ- rpsL ( StrR ) nupG pir-116 ( DHFR ) ] TransforMax™ EC110D™ ( Epicentre Biotechnologies ) in a 0 . 4 cm cuvette ( BioRad ) and pulsed using a MicroPulser power source ( BioRad ) at the manufacturer's preset conditions for bacteria and plated on LB plates containing 50 µg/ml kanamycin . Plasmid DNA was obtained from individual bacterial colonies using the Wizard Plus SV miniprep purification system ( Promega ) . Inserts were initially analyzed by restriction site mapping . Samples were sent for sequencing to either the Translational Genomics Research Institute ( TGen ) , Arizona or to Elim Biopharmaceuticals , Inc , Hayward , California . Lasergene 8 , Seqman software was utilized for sequence analysis . Genomic location and details are provided in Text S1 and Table S1 . The genomic position of each rescued Alu insertion was determined by BLAT ( http://genome . ucsc . edu ) search using the human genome reference ( GRCh37hg19 ) . After manual verification of each insertion position , 20 kb flanking regions ( 10 kb 5′ and 3′ of the insertion point ) were extracted via custom PERL scripts for calculation of GC content and RepeatMasker ( V . 3 . 2 . 8 ) analysis . The relative abundance of Alu , L1 , L2 , MIR , and malR elements was tracked for each recovered insertion . To examine how the genomic regions of the recovered inserts compared to that of Alu elements of various age classes , [1000/100] randomly selected Alu elements from AluJo , AluSx , AluSp , AluYa5 , AluYb8 , and AluYb9 subfamilies were analyzed in the same fashion as described above . Simulation of random insertion of L1 sequences into a genome possessing a HeLa karyotype was conducted using custom Perl scripting . For the purpose of the simulation , the sequenced nucleotides of the human genome ( version hg18 ) were mapped to a corresponding set of unique consecutive integers . Using published HeLa karyotypic data [79] , the mapping process accounted for over and under-represented chromosomal regions of the HeLa karyotype by increasing or decreasing the amount of integer space allocated to the corresponding human regions . Insertion locations were chosen by randomly selecting a genomic nucleotide ( via its corresponding integer ) from the total mapped set of sequence space using a uniform distribution . The insertion was recorded as occurring between the selected and subsequent genomic base . The sequence flanking the chosen location was subsequently extracted from the human genome and analyzed for repeat content with a local installation of RepeatMasker ( default settings ) . The non-parametric Mann-Whitney-Wilcoxon test [80] implemented in the coin package version 1 . 0–18 of R [81] was used to assess whether the distributions of each of the nine genomic features ( Table 4 ) were shifted left or right in the insert-containing ( 203 ) versus insert-free ( 2562 ) 1-Mb windows ( windows were from [39] ) . From the original 226 Alu de novo inserts , nine were not assigned to any window ( as some windows were removed due to gaps in the human genome assembly ) , and 14 windows contained two inserts each , resulting in a total of 203 1-Mb insert-containing windows hosting 217 Alu inserts , and 2562 insert-free windows . The null hypothesis of the test assumes that the distributions in both types of windows are the same , and a shift between the distributions will render a significant p-value ( we analyzed all three possible alternative hypotheses; two- , left- and right-sided ) . For each predictor , we ordered all data ranking them independently of the groupings ( insert-containing vs . insert-free ) and computed the observed U statistic for the test . Next , we performed 10 , 000 random permutations of the data; in each , the insert-containing and insert-free labels of the 2765 windows were reshuffled as to produce randomized insert-containing and insert-free groups with the same sizes as the original ( i . e . , 203 and 2562 windows , respectively ) , and the test statistics for each predictor were recomputed . Benchmarking the observed U statistics with the null distributions generated by the 10 , 000 random permutations allowed us to compute the empirical p-values . A Bonferroni correction for multiple testing was then applied to these p-values . Additionally , random subsets of the data ( usually including 100 inserts at a time ) were analyzed by the same procedure; similar results were obtained ( data not shown ) . HeLa cells ( 4×106/T75 flask ) were transiently transfected with 10 µg of pBS-Ya5 rescue-A70Du using the Lipofectamine Plus ( InVitrogen ) following the manufacturer's protocol . Total RNA was harvested between 24 and 48 h post-transfection using the previously described protocol [82] . For the RT-PCR amplification , cDNA was generated by incubating approximately 1 µg of extracted RNA with the following primers: either unique2: 5′-AGGTTGTGTGTGCCAGTTACCTTGTT-3′ , unique4: 5′-GCCAGTTACCTTGTTTTT-3′ ( for cells transfected with pBS-Ya5rescue-A70Du ) or the anchored oligo dT 5′-GCGAGCACAGAATTAATACGACTCACTATAGGTTTTTTTTTTTT-3′ ( for cells transfected with pBS-Ya5rescue-A70D ) . The unique primers anneal to the unique region of the Alu RNA located between the A-tail and the RNA polymerase III terminator . The RNA-oligo mix was incubated with transcriptor reverse transcriptase ( Roche Applied Science ) at 65°C for 10 min following the manufacturer's recommended protocol . PCR amplification was performed for the Alu rescue samples with the same primer during cDNA generation or the primer to the anchor: 5′- GCGAGCACAGAATTAATACGACT-3′ and the FAtail230 primer: 5′- CTTATAAAACTTAAAACCTTAGAGGC-3′ . PCR amplification was performed for 30 cycles of 20 s at 94°C , 30 s at 58°C and 60 s at 72°C , with a final cycle of 20 min at 72°C . PCR products were excised and extracted from 1% agarose gels using QIAquick gel extraction kit ( Qiagen ) and cloned for sequence analysis using the TOPO TA cloning kit ( Invitrogen ) .
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SINEs are mobile elements that are found ubiquitously throughout a large diversity of genomes from plants to mammals . The human SINE , Alu , is among the most successful mobile elements , with more than one million copies in the genome . Due to its high activity and ability to insert throughout the genome , Alu retrotransposition is responsible for the majority of diseases reported to be caused by mobile element activity . To further evaluate the genomic impact of SINEs , we recovered and characterized over 200 de novo Alu inserts under controlled conditions . Our data reinforce observations on the mutagenic potential of Alu , with newly retrotransposed Alu elements favoring insertion into genic and highly conserved elements . Alu-mediated deletions and rearrangements are infrequent and lack the typical hallmarks of TPRT retrotransposition , suggesting the use of an alternate method for resolving retrotransposition intermediates or an atypical insertion mechanism . Our data also provide novel insights into SINE retrotransposition biology . We found that slippage of L1 ORF2 protein during reverse transcription expands the A-tails of de novo insertions . We propose that the L1 ORF2 protein plays a major role in minimizing Alu extinction by reintroducing active Alu elements to counter the natural loss of Alu source elements .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"transposons",
"retrotransposons",
"biology",
"molecular",
"cell",
"biology",
"molecular",
"biology"
] |
2012
|
Rescuing Alu: Recovery of New Inserts Shows LINE-1 Preserves Alu Activity through A-Tail Expansion
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The complex connectivity of the cerebral cortex suggests that inter-regional communication is a primary function . Using computational modeling , we show that anatomical connectivity may be a major determinant for global information flow in brain networks . A macaque brain network was implemented as a communication network in which signal units flowed between grey matter nodes along white matter paths . Compared to degree-matched surrogate networks , information flow on the macaque brain network was characterized by higher loss rates , faster transit times and lower throughput , suggesting that neural connectivity may be optimized for speed rather than fidelity . Much of global communication was mediated by a “rich club” of hub regions: a sub-graph comprised of high-degree nodes that are more densely interconnected with each other than predicted by chance . First , macaque communication patterns most closely resembled those observed for a synthetic rich club network , but were less similar to those seen in a synthetic small world network , suggesting that the former is a more fundamental feature of brain network topology . Second , rich club regions attracted the most signal traffic and likewise , connections between rich club regions carried more traffic than connections between non-rich club regions . Third , a number of rich club regions were significantly under-congested , suggesting that macaque connectivity actively shapes information flow , funneling traffic towards some nodes and away from others . Together , our results indicate a critical role of the rich club of hub nodes in dynamic aspects of global brain communication .
Constrained by finite resources , such as metabolism and physical space , which place severe limits on the number and density of synaptic connections , brain networks are an example of how optimized topology may facilitate information flow . The structural topology of cortical networks can be represented and formally studied using the graph model [1]–[3] , whereby the brain is spatially parcellated into a set of grey matter nodes interconnected by a set of white matter edges [4] , [5] . This approach has revealed several aspects of network organization that theoretically confer an increased capacity for information processing , including small-world connectivity [6]–[8] , the presence of hubs [9] and cores [10] , cost-efficient spatial embedding [11] , [12] and the coexistence of local segregation and global integration [13] . Recent studies have also uncovered a “rich club” of hub nodes that are more densely interconnected with each other than predicted by chance [14] , [15] and that participate in a disproportionately high number of shortest paths in the network [16] , [17] . The rich club is hypothesized to act as a central backbone for signal traffic , allowing for rapid integration and dissemination of signal traffic [16] . While this graph theoretic approach can articulate the diverse properties of static neural connectivity , it does not take into account the dynamics of information flow on that connectivity . If information flow is introduced into the network , how does neural connectivity influence the efficacy and speed of communication ? In other words , how does network topology enable and constrain the capacity of brain networks to globally integrate information ? For instance , while certain areas may bridge distant communities and potentially function as hubs by virtue of their connectivity , other areas may be ill-suited as conduits for information transfer because of their position in the network . Under conditions of elevated network traffic such regions could become bottlenecks , imposing limits on the relay of information [18] . To determine the effect of topology on inter-regional communication , we implemented a macaque anatomical brain network as a communication system in which units of information flow between grey matter nodes along existing anatomical paths ( Fig . 1 ) . This allowed us to estimate several metrics of information flow in the network , including the proportion of time a given brain region is in use ( utilization ) , the load on a given brain region ( node contents ) , the time it takes for a unit of information to travel from its source region to its target region ( transit time ) and the probability of losing information ( blocking ) . The goal of the present study was to use these performance metrics to address the following questions about communication in brain networks . First , does the unique topology of brain networks offer any particular advantage in terms of information processing , and how do brain networks compare to other networks with the same number of nodes and edges , but different topologies ? Second , which features of brain network organization contribute most to its capacity for efficient communication ? Third , which anatomical regions and pathways are most important for communication ?
Information flow on the network was simulated by generating signal units with specific , randomly-selected source and destination nodes . Each signal unit diffused to one of the neighbouring nodes , until it reached its destination . If a signal unit arrived at a node that was occupied , a queue was formed . A maximum buffer size was imposed , such that a signal unit arriving at a full buffer caused the oldest signal unit in the queue to be removed from the system . A signal unit was removed from the network once it reached its destination node . Simulation intensity was controlled parametrically to investigate the effect of increasing load on communication efficiency . Measures of information flow on the macaque network were compared against a spectrum of degree-matched control networks with an equal number of nodes and edges , but systematically altered topology . One set was comprised of randomized networks , while the other set was comprised of latticized networks ( see Materials and Methods for more information on how surrogate networks are generated ) . Under conditions of increasing load ( see SI Fig . S1 ) , all networks experienced increased blocking and utilization , as well as decreased throughput , thus exhibiting signs of congestion . Mean transit times for signal units that reached their destination also decreased with increasing load , but this counterintuitive observation is the result of decreased throughput . At lower network load , more signals reach their destination but some may take a long time to do so , which increases the mean transit time . As the network becomes congested , many such signals may get dropped at over-utilized nodes and never reach their destination , and thus cannot influence the mean transit time . The macaque network was intermediate on all information flow statistics compared to the randomized and latticized networks . This is consistent with the fact that the randomized and latticized networks represent two extreme and diametrically opposite network configurations and suggests that the organization of the macaque network serves to strike a balance between speed , reliability , utilization and total throughput . Compared to its randomized control network , information flow on the macaque network was characterized by significantly higher loss rates , faster transit times and lower throughput ( Fig . 2 top , for all measures , Tables S2 , 3 , 4 ) , suggesting that neural connectivity may be optimized for speed rather than fidelity . In general however , the two networks performed similarly , while the latticized control network performed much differently , with significantly lower utilization and throughput , shorter transit times and near total loss of information ( for all measures ) . We next sought to determine which feature of network topology most contributed to this pattern of results . To investigate the degree to which the existence of a rich club influences information flow , a synthetic network containing a rich club was created , as well as a set of degree-matched randomized and latticized surrogate networks ( Text S1 , Section 7 , Fig . S7 ) . Information flow on these networks was contrasted with a canonical small world network , which is a ubiquitous and well-studied model for many different kinds of information-processing networks , including neural networks [19] . The pattern of results produced by the synthetic small world and rich club networks and their respective randomized and latticized surrogate networks were considerably different ( Fig . 2 , middle and bottom ) . Importantly , the system statistics associated with the rich club network were nearly identical to the macaque network ( see Text S1 , Section 4 , Table S1 ) . The results observed for the macaque network were also similar to the canonical Watts-Strogatz small world network [19] , but to a significantly lesser extent ( Text S1 , Section 4 , Table S1 ) . Overall , this suggests that the rich club is an important topological feature for information flow in the brain , as defined by these four statistics . We now examine regional contributions of the CoCoMac network in detail . To study the individual relevance of nodes for information flow , three complementary node-level metrics of congestion were used: utilization , blocking and mean node contents . Information flow was highly heterogeneous across the network , with some nodes vulnerable to overwhelming influx , while others experienced only occasional traffic ( Fig . 3 ) . To a large extent congestion at a given node was predicted by the number of afferent projections to that node ( in-degree , and for utilization , blocking and node contents , respectively ) and this is expected given the fact that in the present model information flow is implemented as an interactive random walk [2] , [20] , [21] . With the exception of CA1 , all nodes with the largest average contents were previously identified as part of the rich club [17] ( Fig . S2 ) , indicating that membership in this densely inter-connected subgraph entails a heavy workload . Much of the congestion appears to be concentrated at three distinct sites , mainly along the medial surface , roughly corresponding to medial prefrontal cortex , medial/inferior temporal cortex and precuneus/posterior cingulate cortex . To determine the extent to which these congestion metrics depend on topology rather than degree sequence , we statistically compared them to a set of metrics from simulations run on a population of randomized control networks for which the topology had been altered while preserving degree sequence [22] . Fig . 4A shows the “raw” mean differences between the two networks for the contents of each node , while Fig . 4B shows the spatial distribution of these differences . Due to the high level of consistency between the three metrics of congestion ( utilization , blocking and contents ) , only the results for node contents are shown . Nodes with contents that are significantly different ( , controlled for multiple comparisons using false-discovery rate correction ) for the two sets of the networks are labeled . Interestingly , while the majority of these nodes are part of the rich club , nearly half experience greater congestion in the macaque network , while half experience greater congestion in the randomized networks . This suggests that macaque cortical connectivity imposes a characteristic set of traffic patterns , such that signal traffic is directed towards some nodes and away from others , in contrast to what would be expected based only on the degree of these nodes . We next consider information flow with respect to specific edges . Given the relevance of the rich club in the macaque network [17] , we classified edges according to whether they connect rich club nodes [16] . Edges connecting two non-rich club nodes were classified as ( L ) ocal , those connecting a non-rich club and a rich club node as ( F ) eeder and those connecting two rich club nodes as ( R ) ich club . Moreover , these classifications were made with respect to two rich club levels , RC1 and RC2 , which represent a more conservative and a more liberal definition of the rich club [17] . An initial observation is that this stratification of edges closely resembles the patterns of edge throughputs . In particular , projections with greater throughput appear more likely to be those connected to at least one rich club node , i . e . Rich Club or Feeder . Despite the fact that the vast majority of edges in the macaque network are Local , followed by Feeder and then Rich Club [17] , the mean throughput per edge is greatest for Rich Club edges , followed by Feeder and Local ( Fig . 5B ) . In other words , traffic tends to concentrate not just at rich club nodes , but also at the edges around them , effectively encompassing their local neighborhoods . Finally , we investigate information flow with respect to every possible pair of source and target nodes . For each pair , all completed trajectories are compiled in order to compute the total number of deliveries ( throughput ) , as well as their mean transit time or delay . For both the throughput and transit time statistics , taking the mean across sources results in greater variance than taking the mean across targets ( Fig . 6A , B ) . For the target nodes , both statistics showed substantial association with in-degree ( , for transit time and throughput , respectively ) . In other words , the mean throughput and transit time were much more dependent on the destination , rather than the source , indicating that some nodes in the network are intrinsically easy to reach , while others are intrinsically difficult . A non-monotonic relationship emerges when comparing the mean throughput and the mean transit time across target nodes ( Fig . 6C , dark grey ) . When the total throughput is low , any increase in throughput results in slower transit times . However , for a subset of nodes with a high throughput this relationship does not hold and these nodes tend to receive information much faster than would be expected . Most of these nodes belong to the rich club ( RC2 ) , indicating that rich club nodes receive more information than other nodes in the network , and do so with a disproportionately faster latency . A similar relationship is observed for degree-preserving randomized controls ( Fig . 6C , light grey ) , indicating that the effect is largely due to the high degrees of rich club nodes . Rich club connectivity enhanced the effect . As expected from Fig . 2 , the randomized controls generally have slightly higher throughput , but also longer transit times . This was particularly true for rich club nodes , which received significantly fewer signal units when embedded in the macaque than in randomized networks ( for RC1 and RC2 ) , but did so with significantly faster transit times ( for RC1 and RC2 ) .
The modeling paradigm employed in the present study entails a number of features and simplifying assumptions . Therefore , it is important to consider what the biological correlates of these features are and the extent to which they limit the utility of the model . Central to our approach are discrete signal units . At the large spatial scale , it is unlikely that neural communication takes place via discrete signal units . Rather , information flow between large scale neuronal ensembles is likely to be based on spike trains or coordinated volleys of spike trains . It is also possible that information is transferred as an ensemble of signals from multiple neurons . In our model signal units represent the ability of brain regions to influence one another . This simplifying assumption allows us to trace the trajectory of each signal unit as it propagates in the network , and hence to calculate various metrics about the potential for communication that is afforded by the anatomical connectivity . External arrivals represent the assumption that new information is continuously generated and communicated in the network . The source of this information may be either stimulation exogenous to the nervous system , or some endogenous process . Poisson arrivals were chosen because at the level of individual neurons , inter-spike intervals ( ISIs ) are found to be exponentially distributed [32] and likewise , in psychophysics and signal detection , the Poisson process is often used to model stimulus fluctuations and other statistical properties of the sensory environment [33] . Queues and finite buffers are constructs that allow us to model how network topology constrains information flow . Queueing is a mechanism by which signal units are made to interact as they flow through the network , modeling the interplay between multiple information flows on top of the structural network [34] . Finite buffers allow for the possibility of signal loss , modeling the poor fidelity of neural transmission [35] . Note also that the present model does not take into account the intrinsic hierarchy of sub-domains of brain networks . For instance , one may expect primary sensory areas to send more information than they receive . Likewise , one may expect higher order , multimodal areas to receive more information than they send . In our model , source and destination nodes for each signal unit are chosen randomly , irrespective of their function , and we largely ignore this aspect of cortical organization . Future studies should investigate this fundamental feature of cortical networks . The strength of the modeling approach pursued here is that it allows one to generate relative metrics about network communication . The approach is complementary to other , more physiologically realistic paradigms for modeling global system dynamics [36]–[41] , which do not model information transmission directly . These models suggest that three key ingredients are needed to generate realistic brain dynamics: empirically derived patterns of structural connectivity , time-delayed transmission and noise [40] , [41] . Indeed , a queueing network model has the potential to incorporate all three , and the present implementation includes both empirically derived connectivity and stochastic dynamics . A fundamental aspect of networked communication is the switching architecture: the manner in which information is directed and transported across the network . In the present study , we utilized a message-switched architecture , wherein an entire “message” is contained in a single discrete signal unit . Our study represents the first attempt to characterize communication dynamics in brain networks , so this type of switching architecture , together with diffusive , stochastic routing , was particularly advantageous because this type of model does not assume that signal units have any knowledge of the global topology or traffic conditions [42]–[44] . A physiologically plausible alternative would be a packet-switched architecture , wherein a message is broken up into packets , which individually take the most efficient path to the destination , where they are re-assembled [45] . This type of architecture has many potential advantages for systems that rely on temporally sparse “bursts” of communication , including lower transit times [45] . Thus , our results and conclusions are strongly tied to the diffusion-based , non-hierarchical , message-switched architecture we used , and may not hold for other switching architectures . Altogether , our results reveal a dynamic aspect of the global information processing architecture and the critical role played by the so-called “rich club” of hub nodes . Our work lays the foundation for further systematic study of organizational principles for communication in large-scale brain networks , including routing strategies and resource allocation .
The anatomical connectivity data set used in the present study was derived from the online Collation of Connectivity data on the Macaque brain ( CoCoMac ) database , comprised of data from 413 tract tracing studies of the macaque [46] , [47] . The database was originally queried by [48] and further condensed by [17] . To facilitate comparison with previous reports , only cortical nodes were included . The final directed network was comprised of 242 nodes and 4090 edges and was fully connected , such that each node maintained at least one incoming and one outgoing edge . Two populations of surrogate networks - one randomized and one latticized - were generated to explore the extent to which the topology of the macaque connectivity matrix influenced the simulation results . Randomized networks were generated using a Markov switching algorithm that randomly swapped pairs of edges [22] . Latticized networks were generated using a modified version of the same algorithm , whereby the edges were swapped only if they moved closer to the main diagonal as a result [7] . By randomly re-ordering edges and forcing them closer to the diagonal , the topology of the original network is destroyed and replaced by one where neighbouring nodes are more likely to be connected , as in a ring lattice . Both sets of surrogate networks were degree-matched in the sense that the in-degree and out-degree of each node was preserved . Statistical assessment was performed by comparing 100 simulations on the CoCoMac network with 100 simulations on a randomized null network , for 100 null network realizations . Comparisons between node-specific metrics were made using Welch's t-test for samples with unequal variances [49] , and evaluated with respect to degrees of freedom determined using the Satterthwaite approximation [50] . To control the false discovery rate , -values were corrected following the procedure outlined by [51] . A similar procedure was performed for synthetic small-world [19] and rich club [14] , [15] , [52] networks and their respective null models . A network containing a rich-club was created from a random network by endowing a sub-set of the nodes ( the rich club ) with greater connection density than the rest of the network , and an even greater connection density amongst each other . The randomized and latticized controls were then created as described above . For the small world scenario , the starting point was a ring lattice . A small world network was generated by randomly permuting 10% of the edges , while a completely randomized network was generated by further permuting each edge 100 times . Our results have considerable implication for the rich club feature of brain network topology and so for completeness we briefly rehearse the procedure for detecting and defining rich clubs . Fuller descriptions of the rich club phenomenon can be found elsewhere , for brain networks in general [15] , [16] , as well as for this particular network [17] . For a given graph , a rich club is defined as a set of high-degree nodes ( a subgraph ) that are more densely connected amongst each other than would be expected on the basis of degree alone [52] . Rich club classification is made with respect to a range of node degrees . For a given degree , all nodes with degree are stripped from the network . A rich club coefficient is calculated as the ratio of remaining connections to all possible connections . Thus , can be thought of as the density of the subgraph . For the same set of nodes , the ratio is also computed with respect to 10 , 000 degree-matched randomized networks . The normalized rich club coefficient , , measures the density of the subgraph relative to the null model where the global topology has been destroyed . These steps are repeated for a range of , from the lowest to the second-highest degree in the macaque network ( 2 to 121 ) . A consistently greater than 1 for a range of suggests the existence of rich club organization . Therefore , across the range of it is possible to define unique sets of rich club nodes corresponding to different values of . These nodes can then be positioned in a nested hierarchy of rich club “levels” , ranging from those containing nodes with the highest degree to those containing nodes with the lowest degree . In the present study , we follow the classification made by [17] , whereby two rich clubs were singled out . The first , RC1 , was more densely interconnected and comprised of fewer nodes , with greater minimum degree . The second , RC2 , was less densely interconnected and contained more nodes , with smaller minimum degree . RC2 is a subset of RC1 , and by examining these two levels of the rich club , it is possible to identify robust relationships between rich-club organization and information flow as estimated by our model . Once nodes have been classified as either rich club or non-rich club , it becomes possible to classify edges as well . Namely , edges that connect non-rich club nodes to non-rich club nodes are classified as “local” , those connecting non-rich club nodes to rich club nodes as “feeder” and those connecting rich club nodes to other rich club nodes as “rich club” . Signal units were generated and introduced in the network according to a Poisson process with rate , i . e . with exponentially distributed inter-arrival times . For each signal unit , a source node and destination node were randomly selected . To reach its destination node , the signal propagated to one of the neighbouring nodes , with equal probability for each . The time spent at each node ( service time ) was exponentially distributed with rate . If a signal unit arrived at a node that was occupied , a queue was formed . Units entered the node on a last-come-first-served basis , also known as last-in-first-out ( LIFO ) queueing [53]–[55] . A maximum buffer size was imposed ( ) , such that a signal unit arriving at a full buffer caused the oldest signal unit in the queue to be ejected and removed from the system . Upon reaching the destination node , the unit was removed from the network . The purpose of queueing is simply to ensure that information flow is interactive , while a finite buffer size allowed us to model imperfect signal transmission [35] . Buffer capacity is not a critical parameter , in the sense that it cannot induce a phase transition in the system . Changes in buffer capacity will produce quantitative , but not qualitative , changes in system behavior ( SI Section 3 , Fig . S5 ) . This type of model has two characteristic modes of operation . At low intensities ( external arrival rates ) , the total number of signal units in the network fluctuates around some finite value and the system is said to be in a steady-state . As the intensity is increased , there is a qualitative change in the system dynamics , characterized by a monotonic increase in the number of signal units in the network until all buffers are filled , leading to “jamming” [2] , [20] . The key variable is the ratio between the arrival rate and service rate at each node . Therefore , we fixed the service rate ( ) and varied the rate of external arrivals ( ) . The focus of the present study was on the steady-state behavior of the network , and the range of external arrival rates ( ) was chosen to sustain stationary flow , prior to the phase transition . All simulations were run for 2 million dimensionless time units . Due to the presence of stochastic time variables in the simulation ( inter-arrival times and service times ) , the state of the system was updated at non-uniform time points . Upon completion , the time series of system states were linearly interpolated to produce uniformly sampled time series ( Text S1 , Section 6 , Fig . S6 ) . An initial transient of 40 , 000 time units , during which the system state had not yet stabilized ( determined via the ensemble average method [54] ) , was discarded from further analysis to avoid transitory effects . The Mersenne Twister [56] was used to generate a uniform distribution , which was then used to generate exponentially distributed random numbers ( inter-arrival times and service times ) using the standard inverse transform method . All simulations were implemented in Matlab ( Mathworks Inc . , Natick , MA ) and independently verified in Artifex ( RSoft Design Group Inc . , Ossining , NY ) , as well as analytically ( Text S1 Section 2 , Figs . S3 , 4 ) . All signal units were uniquely identified , allowing for their position and complete trajectory in the network to be traced across the simulation . These trajectories were then analyzed to compile a set of node- , edge- and network-level statistics . For each node , we calculated the mean proportion of time the node was busy ( utilization ) , the probability of signal loss ( blocking ) and the mean system contents . For each edge , we calculated the mean throughput of signal units . For each network , we calculated the mean utilization and blocking rates across nodes , as well as the total number of signal units successfully transmitted from source to destination ( throughput ) and the mean latency of those transmissions ( transit time ) . More formally , simulation variables were defined as follows . A node at time has two components: the server contents , which describes the number of signal units currently in service , and the queue length , which describes the number of signal units waiting in the buffer . The node contents were thus defined as ( 1 ) Likewise , the contents at any existing channel from node to node was . The total network load is then the sum of all node and channel contents: ( 2 ) The utilization of node is the proportion of simulation time during which . The blocking probability at node was calculated as the number of signal units ejected from divided by the total number of signal units arriving at . The total time a signal unit spends at a single node , , is the sum of the waiting time in the queue and the service time in the node ( 3 ) Both and are stochastic processes , with determined by the the topology and dynamics on the network , while is drawn from an exponential distribution with rate . For any signal unit , the transit time is the sum of waiting and service times across all nodes traversed from source to destination . Transit time statistics are calculated only for signals that successfully reached their destination .
|
A fundamental question in systems neuroscience is how the structural connectivity of the cerebral cortex shapes global communication . Here , using computational modeling in conjunction with an anatomically realistic structural network , we show that cortico-cortical communication is constrained by high-level features of brain network topology . We find that neural network topology is configured in a way that prioritizes speed of information flow over reliability and total throughput . The defining characteristic of the information processing architecture of the network is a densely interconnected rich club of hub nodes . Namely , rich club nodes and connections between rich club nodes absorb the greatest proportion of total signal traffic . In addition , rich club connectivity appears to actively shape information flow , whereby signal traffic is biased towards some nodes and away from others . Finally , synthetic networks containing a rich club could almost perfectly reproduce the information flow patterns of the real anatomical network . Altogether , our data demonstrate that a central collective of highly interconnected hubs serves to facilitate cortico-cortical communication . By simulating communication on a static structural network we have revealed a dynamic aspect of the global information processing architecture and the critical role played by the rich club of hub nodes .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"connectomics",
"neuroanatomy",
"computational",
"neuroscience",
"biology",
"neuroscience"
] |
2014
|
Communication Efficiency and Congestion of Signal Traffic in Large-Scale Brain Networks
|
Sphingosine 1-phosphate ( S1P ) is a lysophospholipid mediator which activates G protein–coupled sphingosine 1-phosphate receptors and thus evokes a variety of cell and tissue responses including lymphocyte trafficking , endothelial development , integrity , and maturation . We performed five all-atom 700 ns molecular dynamics simulations of the sphingosine 1-phosphate receptor 1 ( S1P1 ) based on recently released crystal structure of that receptor with an antagonist . We found that the initial movements of amino acid residues occurred in the area of highly conserved W2696 . 48 in TM6 which is close to the ligand binding location . Those residues located in the central part of the receptor and adjacent to kinks of TM helices comprise of a transmission switch . Side chains movements of those residues were coupled to the movements of water molecules inside the receptor which helped in the gradual opening of intracellular part of the receptor . The most stable parts of the protein were helices TM1 and TM2 , while the largest movement was observed for TM7 , possibly due to the short intracellular part starting with a helix kink at P7 . 50 , which might be the first helix to move at the intracellular side . We show for the first time the detailed view of the concerted action of the transmission switch and Trp ( W6 . 48 ) rotamer toggle switch leading to redirection of water molecules flow in the central part of the receptor . That event is a prerequisite for subsequent changes in intracellular part of the receptor involving water influx and opening of the receptor structure .
Sphingolipids together with glycerol-based phospholipids are major structural components of cell membranes . In response to various extracellular stimuli , including growth factors , inflammatory cytokines , antigens , and agonists of some GPCRs , the sphingolipids can be metabolized into potent mediators , such as sphingosine-1-phosphate ( S1P ) [1] . This sphingolipid has emerged as an important signaling mediator participating in the regulation of multiple physiological and pathological processes taking place in cancer , cardiovascular diseases , wound healing , atherosclerosis and asthma but also is important in pathological conditions such as inflammation and stress . It can also trigger a range of biological effects such as cell migration , differentiation , apoptosis , immunity , proliferation and angiogenesis [2]–[5] . The functioning of S1P receptors in the maintenance and modulation of the activity of the biological barrier is of the profound biological importance and has many therapeutic implications including treatment of multiple sclerosis , prevention of the transplant rejection and probably the adult respiratory distress syndrome as well [6]–[11] . Within the five known high-affinity S1P receptors the S1P1 receptor subtype is the most commonly expressed in various cell types including cardiac cells , endothelial cells and neurons [11]–[15] . Studies on deletions in the S1P1 gene have revealed its essential endothelial function in the arterial smooth muscle cell migration [16] . The S1P1 knockout mice exhibit embryonic lethality or abnormalities in the development of the immune system [11] , [17] , [18] . The recently published crystal structure of S1P1 with antagonist ML056 by Stevens group [19] ( PDB code: 3V2Y ) showed a detailed ligand binding mode including the precise position of a long hydrophobic tail of a ligand regardless of lack of directional bonds establishing its location in the binding site . The authors also predicted the binding mode of an agonist S1P by docking it to the same binding site as the antagonist . Based on the docking results they concluded that the long hydrophobic tail of the agonist is responsible for the receptor activation as it was not possible to fit it to the antagonist-bound crystal structure with preserved interactions of a zwitterionic head . Only after allowing the receptor structure to adapt to the agonist it was possible to fit the hydrophobic tail and simultaneously preserve the polar interactions of the ligand head . However , the exact mechanism of the S1P1 activation is still not known and it is particularly interesting to learn how these changes are evoking passing of a signal to the cytoplasmic side of the receptor . To address that issue , we conducted five all-atom 700 ns MD simulations for the Apo form of S1P1 , antagonist ML056-bound S1P1 and agonist S1P-bound S1P1 . We studied movements of amino acid residues in centrally located area where the transmission switch operates . We also proposed the pathway of the activation mechanism involving the movement of water molecules as it was recently detected during simulations of the model of the formyl peptide receptor FPR1 [20] .
The S1P agonist coordinates were obtained from the PUBCHEM online database [21] . The ligand preparation utility in MacroModel [22] was used to optimize the geometry of the initial structure . The systematic conformational search was also performed in MacroModel and top five conformers of the lowest potential energy were kept for docking . The docking procedure was performed using Glide [23] , [24] ( Schrödinger 2012 suite ) . The protonated state of primary amine of S1P and ML056 at physiological pH was predicted by Epik [25] , [26] and resulted in zwitterionic head group of both ligands . The S1P molecule was initially placed in the binding pocket with a pose similar to the antagonist molecule in the S1P1 crystal structure ( PDB: 3V2Y ) . Cubic box defining the docking area was centered on the ligand mass center with a box size of 10 Å . Next , the flexible ligand docking was performed . Ten poses out of 10 , 000 were included in the post-docking energy minimization and the best scored pose was chosen for MD simulation . For an antagonist ML056 present in the crystal structure no ligand optimization was performed but only addition of hydrogen atoms according to the calculated protonated state . To obtain the atomic partial charges for S1P and ML056 ligands , the structures obtained from docking were energy-minimized and the electrostatic potentials were obtained . The quantum mechanical calculations were done in GAUSSIAN 09 program [27] with 6-31G* basis set . The obtained potentials were used as input for the RESP ( Restrained-Electrostatic Potential ) fit method [28] performed by the R . E . D . tools [29] . All ligand topology parameters were generated using SwissParam web server [30] . The crystal structure of the S1P1 receptor lacks of two intracellular loops ICL2 ( amino acids 149–155 ) and ICL3 ( amino acids 232–244 ) . The latter one , between helices TM5 and TM6 , was substituted by T4-lysozyme to stabilize the structure . The original missing loops were modeled in Modeller 9v10 [31] and Rosetta loop modeling tools [32] . Initial 5000 loop conformations were generated in Modeller , and conformations with the lowest DOPE score were submitted to the Rosetta loop modeling for an all-atom refinement ( the kinematic closure method ) . The unstructured part of C-terminus , the residues 327–330 after helix H8 , was removed in our model . Pre-equilibration of the lipid bilayer composed of POPE phospholipids ( 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine ) and embedding of the receptor into lipid bilayer was done in Maestro 9 . 2 program [33] and in Desmond [34] program . We used 23 Na+ and 44 Cl− ions to make the system neutral and to set the ionic strength to 0 . 15 M . The total number of atoms in the investigated system was approximately 50 , 000 including about 8 , 300 water molecules and 132 POPE phospholipids . The periodic box dimensions were set to 7 . 0 nm×7 . 0 nm×10 . 4 nm . Equilibration of the system was performed at the constant pressure and temperature ( NPT ensemble; 310 K , 1 bar ) employing Berendsen temperature and pressure coupling scheme [35] under CHARMM36 force field [36] . All bond lengths to hydrogen atoms were constrained using M-SHAKE algorithm [37] . Van der Waals and short-range electrostatic interactions were cut off at 10 Å . Long-range electrostatic interactions were computed by the particle mesh Ewald ( PME ) summation scheme [38] . A RESPA ( time-reversible reference system propagator algorithm ) integrator [39] was used with a time step of 2 fs . Long-range electrostatic interactions were computed every 6 fs . Harmonic positional restraints on the protein backbone were tapered off linearly from 10 to 0 kcal/mol−1A−2 over 20 ns . Additional 20 ns NPT equilibration without restraints was executed afterwards . Finally , 700 ns simulations were performed for Apo receptors , and with agonist and antagonist bound structures . All simulations were performed in Desmond [34] . To facilitate comparison of our structure to other GPCRs the Ballesteros-Weinstein numbering scheme [40] was used ( numbers in superscript ) apart from the sequence numbers of S1P1 residues . The Desmond force field parameters for both ligands , S1P and ML056 , are provided as a supplementary information ( Protocol S1 ) .
After the non-restrained final step of equilibration procedure the backbone of the TM core and loops were matching the crystal structure . Only the loose , unstructured N-terminus ( amino acids 16–21 ) was freely moving during equilibration . The amino acids in the binding site of Apo receptor structure were nearly in the same positions as in the crystal structure with exception of S1052 . 64 at extracellular end of TM2 ( movement of whole residue 1 . 5 Å outside of the receptor ) and a rotamer of M1243 . 32 side chain which was oriented in such a way that it took a position occupied in the crystal structure by the ligand's hydrophobic tail . Contrary , those two residues , S1052 . 64 and M1243 . 32 , in the MD simulation of the antagonist-bound receptor were matching the crystal structure . After the equilibration the antagonist molecule took a slightly shifted position compared to that of the crystal structure as its phosphate group lost a direct contact with R1203 . 28 though preserving the interaction with K34 , located in a short linker between two helices in N-terminus . What is more , the charged amino group of antagonist gained another favorable interaction , apart from E1213 . 29 . This happened due to the N1012 . 60 residue , which flipped and started to interact with the nearby E1213 . 29 and amine group of antagonist . We also observed a solvent-mediated hydrogen bond between the antagonist and R1203 . 28 . The carbonyl group of antagonist formed a hydrogen bond with Y982 . 57 which was not present in the crystal structure ( too large distance 4 . 7 Å ) . The binding modes of investigated ligands are shown in Figure 1 while detailed interactions with adjacent amino acids are shown in Figure S1 . The interactions of ML056 in the binding site of S1P1 receptor were preserved until the end of 700 ns MD simulation , apart from residue Y382 . 57 which rotated away and formed a hydrogen bond with S3047 . 46 located few residues to the highly conserved NPxxY motif on helix TM7 . The same bond was formed during MD simulation of Apo receptor but not during a simulation with agonist ( Figure 2 ) . Water molecules , which were not visible in the crystal structure due to its low resolution were found to fill the empty binding site of Apo receptor after equilibration and during MD simulation . In case of ligand-bound receptor structures a number of water molecules in the binding site was only slightly smaller than that in Apo receptor because both ligands took positions mostly inaccessible to water molecules . Only the polar and charged groups of zwitterionic head had a contact with water ( Figure S1 ) . In case of the structure of agonist bound receptor , at the beginning of MD simulation , the zwitterionic head interacted indirectly with amino acids via water molecules but this changed during the simulation ( Figure S1B and S1D ) . After equilibration of the S1P/S1P1 complex the phosphate group of S1P interacted directly with K34 ( similarly to antagonist ) but also with Y29 ( as in the crystal structure of antagonist-bound complex ) . Those interactions were stable through the whole MD simulation . However , in contrast to the antagonist case , both residues E1213 . 29 and N1012 . 60 did not interact directly with agonist , but only via water molecules . However , during simulation , the phosphate group started to interact with R1203 . 28 and the OH group of S1P formed a hydrogen bond with N1012 . 60 , while S1052 . 64 interacted with both the hydroxyl and the amine group of agonist . The superimposition of both studied ligands , ML056 and S1P , in the receptor binding site is shown in Figure 1B . The hydrophobic tail of both ligands is located mostly in the same area surrounded by helices TM3 and TM5-7 as well as hydrophobic residues from extracellular loop ECL2 . The ends of both ligands are pointing toward the same region of TM5 , however , a tail of S1P is longer and reaches a hydrophobic cluster composed of three phenylalanine residues , F1253 . 33 , F2105 . 47 and F2736 . 52 , centered at TM5 . During the MD simulations we observed several movements of aromatic residues ( Figure 2 ) which can be interpreted as possible rotamer switches . In Apo and antagonist bound receptor complex structure a residue Y982 . 57 changed its conformation , which led to the formation of a hydrogen bond with S3047 . 46 ( Figure S2 ) . Although one cannot exclude that such movement is a result of slightly different binding of antagonist compared to the crystal structure , the analogous rotameric change in Apo receptor structure is striking . Additionally , in case of the antagonist complex the residue W2696 . 48 is fluctuating and its χ2 angle is changing between 0 and 90 degrees , until the rotation of Y982 . 57 occurs ( Figure 2B ) . The changes of W2696 . 48 are much smaller in the second simulation with antagonist ( Figure 2B′ ) . Contrary , in the case of agonist S1P-bound complex , a stable rotamer of Y982 . 57 is a result of a hydrogen bond between Y982 . 57 and a backbone carbonyl group of L2977 . 39 . Such a bond was created during equilibration period and was stable until the end of simulation . In the crystal structure the residue Y982 . 57 is bound neither to the ligand nor to any other receptor residue . Instability of residues W2696 . 48 and F2656 . 44 together with Y982 . 57 rotamer “up” in case of agonist-bound receptor ( Figure 2C and 2C′ ) may be a prerequisite to rearrangement of residues located close to the highly conserved W2696 . 48 . Such rearrangement is called a transmission switch [41] , [42] ( previously called a tryptophan rotamer toggle switch ) and can lead to the movement of cytoplasmic parts of helices TM6 and TM7 outward of the receptor center . Similar scheme of activation was recently described for adenosine A2A receptor based on its 1 . 8 Å high resolution antagonist-bound structure [43] . The structure contains 177 structured water molecules , 57 of which occupy the interior of the 7TM bundle . In the antagonist-bound A2AR ( PDB id: 4EIY ) there is so called water channel ( Figure S3A ) . The channel has two bottlenecks close to residues W2466 . 48 and Y2887 . 53 , respectively , reducing its diameter to slightly less than one water molecule ( 2 . 4 and 2 . 0 Å , respectively ) dividing the channel into three parts . Rearrangement of the receptor backbone and side chains due to agonist binding ( PDB id: 3QAK ) makes the structure more open in bottleneck areas suggesting possibility of formation of continuous hydrogen bond network involving water ( Figure S3B ) . The importance of water molecules for GPCR activation have been also reported in several previous studies [20] , [44] , [45] . In our simulations , we found that the residue Y982 . 57 can redirect the flow of water molecules . Keeping a rotamer in “up” position ( agonist-bound state ) Y982 . 57 prevents water molecules to enter the area between Y982 . 57 and W2466 . 48 , but instead allows more water to come near the highly conserved residue D912 . 50 ( Figure S4 ) . In simulations of Apo S1P1 and ML056/S1P1 the number of water molecules within 4 Å distance to D912 . 50 is much smaller than in agonist-bound complex: there are 3–4 water molecules in Apo state versus about 5–7 molecules in antagonist-bound state and approximately 8–10 molecules in agonist-bound state . Those water molecules form an extensive hydrogen bond network between highly conserved residues N631 . 50 , D912 . 50 and N3077 . 49 which can facilitate receptor activation and opening of the cytoplasmic part of the receptor . During a simulation of agonist-bound receptor the side chain of W2696 . 48 rotated about 90° between vertical and horizontal positions ( Figure 3A–B ) . This movement facilitated conformational change of adjacent residue F2656 . 44 located one helix-turn down towards the receptor center in agonist-bound structure . Only after that movement it was possible for the water to enter into the vicinity of D912 . 50 residue ( Figure S4 ) in ligand-bound state ( agonist and antagonist ) . Final rotamer of W2696 . 48 is the same as in the crystal structure but its movement facilitated rotameric change of F2656 . 44 and flow of water ( Figure 3C ) . Movement of water molecules at inner membrane part of the receptor ( close to the NPxxY motif in TM7 ) can be seen in Figure 4A and 4A′ . Large amounts of water accumulate at this position starting at 150 ns in 1st simulation and at 400 ns in 2nd simulation in agonist-bound receptor . At the same time there is much smaller number of water molecules in case of Apo and antagonist-bound receptor ( Figure 4A and 4A′ ) . The reason for such behavior is the change of shape of TM7 ( Figure 5A ) . During the MD simulation the kink angle of TM7 with a pivot point at P3087 . 50 was changing gradually from 155° to 130° with a temporary restoration of initial value between 100 and 200 ns in one simulation ( Figure 5B ) . Such relatively fast movement of intracellular part of TM7 helix is facilitated by short length of that part which consist of two helix turns only . Because of that , a change of TM7 could be the first movement of the transmembrane helix bundle during the activation . Increased volume of this area can accommodate more water molecules ( Figure 4B ) and make room for the G protein . As it can be seen from RMSD plots of the receptor backbone ( Figure 6A ) there is only a small change ( about 2 Å ) of backbone structure in case of Apo and antagonist-bound receptor . However , in case of the agonist-bound receptor there is a transient and sudden increase of RMSD ( up to 4–5 Å ) at 200 ns and ending at 600 ns . Then , the RMSD for both simulation with agonist stabilizes at 3 Å . Such an increase may be associated with movement of residues W2696 . 48 and F2656 . 44 ( Figure 2C and 2C′ ) being a central part of transmission switch rearranging of the central part of the receptor . Such flexibility of these residues , although finally they assume nearly the same conformations as before , may be necessary for larger movements of cytoplasmic parts of TMs in the next phase of the activation process . Nevertheless , those preliminary movements can be still noticeable in our simulations . We found that conformations of S1P1 receptor during MD simulations can be divided into three major clusters: “inactive” , “intermediate” and “active” ( Figure 6B and S5 ) . Such a division was made based on distances between cytoplasmic ends of TM helices ( TM7-TM3 , TM3-TM6 and TM6-TM7 ) from MD simulation of agonist-bound receptor structure . In Figure 6B the central structures from each cluster are shown . Those clusters are well separated so one can easily distinguish three different stages of activation . The “active” conformation differs from the “inactive” one primarily through shifts and rotations of intracellular ends of helices TM3-7 ( Figure 6B ) . During the transition from “intermediate” to “active” stage , the intracellular part of TM7 also rotates while moving away from TM3 and TM6 and an angle at pivot point of TM7 ( P3087 . 50 ) diminish by 25° i . e . the kink of TM7 increases . Although most likely the full activation of the protein was not achieved in our simulation the obtained directions of TMs movements agree well with activated states of other GPCRs: adenosine receptor A2AR [46] , β1- and β2-adrenergic receptors [47] , [48] , and opsin [49] . Parrill et al . [50] studied effect of S1P1 receptor mutations on binding its natural substrate sphingosine 1-phosphate ( S1P ) . Based on experiments: radioligand binding , ligand-induced [35S]GTPγS binding , and receptor internalization assays , they suggested that three amino acids R1203 . 28 , E1213 . 29 and R2927 . 34 were involved in the ligand binding . They illustrated their findings with a model of the ligand-receptor complex constructed on early rhodopsin model based on distance geometry calculations with hydrogen bonding constraints [51] . Those three residues were also shown as binding S1P in S1P1 binding site in more recent paper of the same group [52] . The crystal structure of S1P1 receptor with antagonist ML056 can verify to some extent those findings . The residues R1203 . 28 and E1213 . 29 are directly interacting with ligand while R2927 . 34 is neither interacting nor even being a part of a binding site since its side chain is located outside of a receptor . In our simulations the residue R2927 . 34 is far from antagonist ML056 but also from agonist S1P . Although not interacting directly with the agonist bound in orthosteric binding site this residue may be required as a selectivity filter on the ligand entry pathway . Loenen et al . [53] determined differences in ligand-induced S1P1 receptor activation using an in silico guided site-directed mutagenesis . They mutated three residues , Y982 . 57 , R1203 . 28 , and F1253 . 33 , and probed mutants with a chemically diverse set of agonists including S1P . Mutation of residue R1203 . 28 resulted in a reduction of the potency of all ligands , measured as an inhibition of forskolin-induced cAMP accumulation . For all compounds the effects observed for the R1203 . 28A mutation were larger than those observed for the R1203 . 28K , however an effect of subtle mutation R1203 . 28K was the biggest in case of reducing potency of the endogenous agonist S1P . Mutation of Y982 . 57F did not significantly affect S1P1 agonist potency for any of the ligands tested , however , a mutation of this bulky residue into alanine affected the potency of S1P by almost 80-fold . Also a mutation F1253 . 33Y did not significantly affect the potency of S1P . The above results are in agreement with our simulations: the agonist S1P formed a tight contact with residue R1203 . 28 while residues Y982 . 57 and F1253 . 33 were located on both sides of the ligand and contributed to hydrophobic interactions so exchanging them into alanine could result in reduced binding . Recently , Satsu et al . [54] described a selective allosteric agonist of S1P2 receptor . Mutation of receptor residues responsible for binding to the zwitterionic head group of natural agonist S1P abolished activation of the receptor by S1P , but not activation by synthetic ligand CYM-5520 . Competitive binding experiments with radiolabeled S1P demonstrated that CYM-5520 was an allosteric agonist which did not displace the native ligand . Computational modeling , based on the crystal structure of S1P1 receptor , suggested that CYM-5520 could bind beneath the orthosteric binding pocket , so that co-binding of S1P could not be affected . Possibly , the similar allosteric agonists can be found for S1P1 receptor . The proposition of activation mechanism of S1P1 receptor based on our simulations is illustrated in Figure 7 . After binding of agonist S1P to the binding site of S1P1 , the movement of acyl tail of S1P leads to the flipping of W2696 . 48 ( step 1 ) . Such rotameric change alters the conformation of side chain of F2656 . 44 which is located next to W2696 . 48 in the same helix TM6 ( step 2 ) . These residues form a core of a transmission switch which involves rearrangement of centrally located residues including N631 . 50 , D912 . 50 , S3047 . 46 and N3077 . 49 . They facilitate a redirected flow of water molecules inside a receptor ( step 3 ) . The influx of water molecules at intracellular part of the receptor leads to limited motions of cytoplasmic ends of TM helices , with the largest movement associated with TM7 ( step 4 ) , which is a prerequisite for larger motions of the cytoplasmic parts of transmembrane helices . These movements lead to opening the protein structure to make room for binding a G protein . The mutations of S1P1 receptor analyzed so far were located close to the orthosteric binding site of native agonist S1P . However , finding of the allosteric agonist not having charged functional groups implicated its different binding mode . Possible binding site of this compound close to residue W6 . 48 in S1P2 receptor may have a direct influence on action of the transmission switch . Investigations of residues close to this region could shed some light on activation processes of S1P1 receptor and maybe discriminate effects of allosteric from orthosteric binding . Studying mutations of R2927 . 34 and nearby residues is required to analyze how ligands can enter the receptor binding sites both orthosteric and allosteric . The residues found to be important in our simulations for the transmission switch , including D912 . 50 , Y982 . 57 , F2656 . 44 , W2696 . 48 , N3037 . 45 and S3047 . 46 are forming a cluster in the central part of S1P1 receptor . Mutagenesis studies of those residues may be important to elucidate the details of transmission switch and also to discover the receptor structures hampered at different stages of activation during action of this complex switch . Additional simulations of wild type and mutated S1P1 receptor complexes with different ligands , including those bound in allosteric sites , will be extremely helpful to visualize or guide the site directed mutagenesis experiments and also to explain the exact role of particular residues in receptor activation .
|
The activation of G-protein-coupled receptors ( GPCRs ) depends on small differences in agonist and antagonist structures resulting in specific forces they impose on the helical bundle of the receptor . Having the crystal structures of GPCRs in different stages of activation it is possible to investigate the successive conformational changes leading to full activation . The long molecular dynamics simulations can fill the gap spanning between those structures and provide an overview of the activation processes . The water molecules are recognized to be crucial in the activation process which link shifting of ligand in the binding site , the actions of molecular switches and finally the movements of fragments of TM helices . Here , we present five 700 ns MD simulations of lipid S1P1 receptor , either in Apo form , or bound to antagonist ML056 or natural agonist S1P . The Apo and antagonist-bound receptor structures exhibited similar behavior , with their TM bundles nearly intact , while in the case of the agonist-bound receptor we observed movements of intracellular ends of some of TM helices .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results/Discussion"
] |
[] |
2013
|
Lipid Receptor S1P1 Activation Scheme Concluded from Microsecond All-Atom Molecular Dynamics Simulations
|
Quantitatively predicting changes in drug sensitivity associated with residue mutations is a major challenge in structural biology . By expanding the limits of free energy calculations , we successfully identified mutations in influenza neuraminidase ( NA ) that confer drug resistance to two antiviral drugs , zanamivir and oseltamivir . We augmented molecular dynamics ( MD ) with Hamiltonian Replica Exchange and calculated binding free energy changes for H274Y , N294S , and Y252H mutants . Based on experimental data , our calculations achieved high accuracy and precision compared with results from established computational methods . Analysis of 15 µs of aggregated MD trajectories provided insights into the molecular mechanisms underlying drug resistance that are at odds with current interpretations of the crystallographic data . Contrary to the notion that resistance is caused by mutant-induced changes in hydrophobicity of the binding pocket , our simulations showed that drug resistance mutations in NA led to subtle rearrangements in the protein structure and its dynamics that together alter the active-site electrostatic environment and modulate inhibitor binding . Importantly , different mutations confer resistance through different conformational changes , suggesting that a generalized mechanism for NA drug resistance is unlikely .
Current plans for managing future influenza pandemics include the use of therapeutic and prophylactic drugs , such as zanamivir [1] and oseltamivir [2] , that target the virus surface glycoprotein neuraminidase ( NA ) [3] . Inhibition of NA reduces the spread of the virus in the respiratory tract by interfering with the release of progeny virions from infected host cells . A handful of drug-resistant strains have recently emerged due to antigenic drift [4] , [5] , [6] . NA in these strains contains a series of mutations that do not significantly alter its function , yet render it resistant to inhibition . These mutations lead to a small ( 1–3 kcal/mol ) decrease in the high-affinity binding of these inhibitors that is sufficient to restore in vivo viral propagation . Understanding how different NA mutations confer drug resistance is a critical step in discovering new drugs to safeguard against future influenza pandemics . NAs from different influenza subtypes exhibit a variety of resistance mutations and these mutations can affect inhibitors differently . For example , the R292K mutation in N2 NAs confers resistance to oseltamivir [7] , but in highly similar N1 NAs such mutation remains drug sensitive [8] . These and other complex patterns of resistance can only be explained by the interactions between the binding site and the inhibitors . Previous biochemical [9] and structural studies [10] have implicated the rearrangement of certain binding-site residues as the mechanism of drug resistance in NA . For example , bulky substitutions at H274 result in a conformational shift of the neighboring E276 , which alters a hydrophobic pocket that specifically disrupts oseltamivir binding . While such structure-based explanations are plausible , a critical evaluation of these hypotheses requires atomic-scale models that accurately reflect the microscopic structural mechanisms guiding NA-inhibitor interactions . X-ray crystallography provides high-resolution structures of NA-inhibitor complexes . Although such structures are vital to our understanding of NA-inhibitor interactions , the atomic coordinates themselves lend little direct insight into the underlying thermodynamics of drug resistance . There are numerous examples of crystal structures of proteins with drug resistance mutations , such as of HIV-1 protease [11] , that show only minor structural differences when compared to the drug-sensitive wild type ( WT ) structure and do not reveal any readily apparent mechanism of resistance . Numerous drug resistance mutations in NA fall outside of the immediate binding pocket , and structures of the drug-resistant H274Y and N294S mutants co-crystallized with oseltamivir and zanamivir reveal binding-site conformations that are virtually identical to WT [10] . Molecular simulations that rigorously model the microscopic structure and thermodynamics [12] , [13] , [14] of NA-inhibitor interactions may provide insight into the mechanisms of drug resistance that elude traditional structure-based approaches . Accurately modeling the thermodynamic consequences of mutations that alter protein function , such as in drug resistance , is a major challenge in structural biology . The change in binding free energy associated with a drug resistance mutation is a result of systemic shifts across the totality of structural conformations that impact which biochemical interactions are accessible in the wild-type and the mutant protein systems . Due to the staggering conformational complexity of a protein-inhibitor complex , direct and exhaustive modeling of this entire system is computationally unfeasible . To overcome such difficulties , two types of approaches for predicting free-energy changes from point mutations have been developed: empirical approaches , which apply highly trained score functions that approximate the free energy of a given structure , and simulation-based approaches , which combine extensive stochastic sampling with statistical mechanics-based calculations to estimate free energies . These approaches have been reviewed extensively elsewhere [15] , [16] , [17] . While empirical approaches have been moderately successful at identifying mutations along interfacial residues that disrupt binding , they fail to identify the numerous mutations outside of the interface where the effects are presumably smaller [18] . Even the most rigorous simulation-based methods currently available , such as Thermodynamic Integration ( TI ) and the closely related Free Energy Perturbation ( FEP ) [12] , [13] , [19] , [20] , [21] , [22] , may lack the accuracy and precision to assess small changes to otherwise large binding free energies . These methods , which , in theory , should capture the thermodynamic effects of protein mutations , have been applied to compute absolute binding free energies of several small molecules to wild type and mutant enzymes , including T4 lysozyme and NA [23] , [24] , [25] , [26] . However , straightforward applications of these techniques to large , complex systems are hampered by significant sampling issues . These issues are particularly severe in systems with hindered conformational transitions associated with ligand binding , which often render the resulting absolute binding free energy calculations unreliable [27] , [28] , [29] , [30] . Conventional methods for calculating relative binding free energies across a series of related compounds avoid many of the sampling issues associated with absolute binding free energy calculations [31] , however , they are typically not directly applicable to assessing the effects of mutations on binding of the same compound . Successful modeling of the thermodynamics of large , complex systems , such as NA , requires careful selection of both the conformational sampling strategy and the appropriate reference states in order to obtain precise and accurate estimates of free energy changes . We recently described a novel implementation of the Hamiltonian Replica Exchange ( HREX ) molecular dynamics ( MD ) method [31] that uses an alchemical thermodynamic pathway to arrive at reliable free energy calculations . Here , we adapted this approach to incorporate residue mutations into the thermodynamic cycle . Instead of estimating changes of binding free energies of different compounds with respect to the same protein , we estimated free energy changes for mutating a residue in the bound and unbound wild type protein . We applied this method to several such pathways to predict the binding free energy changes ( ΔΔG ) of a set of mutations in H5N1 NA that have been experimentally tested for drug resistance . We successfully identified drug resistance mutations in NA using a judiciously chosen thermodynamic path within the HREX framework . For this work , we adapted the criterion introduced by Kortemme et al . [32] to classify a mutation as drug resistant when its calculated ΔΔG exceeded +1 kcal/mol . Based on this criterion , the experimentally observed NA mutations N294S , H274Y , and Y252H reveal different resistance patterns with respect to oseltamivir and zanamivir [10] . We explored the capabilities of our approach and alternate ones , including those from previously published work [33] , [34] , to produce accurate and precise ΔΔG estimates consistent with the experimental data [10] . Analysis of over 15 µs of aggregate MD simulation data revealed that different mutations confer resistance through different conformational changes in the active site . Unexpectedly , we found no evidence supporting the previously reported role of hydrophobic interactions with the oseltamivir tail [10] . Instead , we hypothesize that drug resistance arises from rearrangements of several charged residues that alter the electrostatic environment within the binding site and disrupt inhibitor binding . The complexity of the observed structural perturbations highlights the importance of atomic-level structural details and suggests that identification of a generalized theory of resistance is unlikely .
We computed relative instead of absolute binding free energy changes using Single Reference Thermodynamic Integration ( SRTI ) [31] . Computing relative ΔΔGs requires measuring the free energy change along an alchemical thermodynamic path linking the WT to the mutant protein for the ligand-bound and ligand-free states independently , which requires only a partial ‘decoupling’ of the mutating residues and/or ligand along that alchemical path . In contrast , absolute ΔΔG computations entail measuring the free energy change along an alchemical thermodynamic path connecting the ligand-bound and ligand-free states , which requires a complete decoupling of the ligand from the protein [35] . Previous MD simulations of NA [36] , [37] revealed substantial binding-induced conformational changes along a 150-residue loop . A complete decoupling of the ligand [25] would necessitate extensive sampling of this large conformational transition , making reliable free energy predictions practically impossible . By avoiding the need to explicitly model this binding-induced conformational change , the relative SRTI approach is better suited for ΔΔG calculations for NA . Determining the molecular mechanisms of NA drug resistance involves identifying key protein structural features that underlie the thermodynamic differences in inhibitor binding observed in the simulation data . Such features may include changes in biochemical interactions in the NA-inhibitor complex , systematic shifts in the NA structure , and even subtle differences in the overall dynamics between WT and drug-resistant NA . A visual comparison between the crystal structures of NA in complex with zanamivir and oseltamivir revealed few apparent differences in NA-inhibitor interactions . Therefore , we analyzed the structural data derived from the SRSM/HREX simulations in order to identify reliable structural differences between WT and drug-resistant mutant trajectories . Fig . 2 illustrates representative structures from the WT and drug-resistant mutant trajectories for zanamivir and oseltamivir , confirming the x-ray crystallography findings that the most prominent binding interactions are preserved . The negatively charged carboxyl group of both inhibitors maintained interactions with a basic triad formed by R118 , R292 , and R371 . The positively charged ammonium and guanidinium groups of oseltamivir and zanamivir , respectively , maintained salt-bridges with the acidic E119 , D151 , and E227 residues ( E227 is not displayed in Fig . 2 for purposes of clarity ) . Finally , the polar tail of zanamivir maintained some of the hydrogen bonds with R224 , E276 , and E277 in both WT and mutant forms . The long-range nature of these electrostatic interactions and the highly flexible nature of the binding site suggest that NA-inhibitor binding is highly sensitive to subtle , systematic rearrangements of the electrostatic environment caused by mutations beyond the immediate binding site . Our analysis identified several such rearrangements that may be critical to drug resistance . We used MD simulations and statistical mechanics to quantify the effect of drug resistance mutations in NA on the ΔΔG of oseltamivir and zanamivir binding . We found that implicit solvent-based methods , such as MM-GBSA , and empirical approaches , such as Rosetta , were largely unable to predict drug resistance . However , careful use of thermodynamic-integration-based approaches successfully predicted binding affinities with chemical accuracy . Ultimately , the SRSM/HREX approach yielded the most accurate and precise ΔΔG values compared with those obtained experimentally . The SRSM approach minimized the degree of decoupling between the real states and the unphysical reference states , while HREX significantly enhanced conformational sampling as a result of exchanges between the TI simulation windows . Together , the SRSM/HREX approach successfully sampled a thermodynamic path between WT and mutant NA which circumvented conformational sampling barriers that significantly impeded conventional MD simulations to yield highly reliable free energy calculations . The additional computational cost associated with using HREX was practically negligible compared to SRSM because the time required for both types of runs is roughly equivalent . Finally , we must point out that the computation of ΔΔGs using SRSM ( or SRSM/HREX ) is computationally demanding . To evaluate the six ΔΔG values for NA with their corresponding standard errors , we were required to carry out a minimum of 36 runs , each 4ns-long and involving 31 replicas , for an aggregate simulation time of ∼4 . 5 µs . The whole analysis presented here required over 15 µs of aggregated MD simulations . We analyzed trajectories from the SRSM/HREX simulations in order to identify the structural and energetic mechanisms underlying the computed ΔΔGs . We identified a number of subtle , systematic , rearrangements in the extensive hydrogen bonding and electrostatic interactions in the inhibitor binding site in the drug resistant H274Y and N294S mutations that were largely absent in the drug-sensitive Y252H mutation . Although the exact nature of these electrostatic rearrangements varied for each drug and mutation , we hypothesize that these rearrangements in the binding pocket form the basis of drug resistance in NA . This is in contrast with the previous interpretations of the experimental structures that suggested changes in the size and hydrophobicity of the binding pocked as the primary mechanism for resistance [10] . Our study marks the most extensive use to date of molecular dynamics and thermodynamic integration on a large , pharmaceutically relevant system and demonstrates that a rigorous , computationally intensive approach can be successfully applied to studying the thermodynamic mechanisms underlying protein function that can elude traditional structure-based crystallography approaches .
The SRMM approach ( Fig . 1 ) allows for simultaneous comparison of binding free energy changes between all pairs of proteins and ligands . To implement this approach , we designed a common RS for all proteins and ligands for the bound and unbound state . Portions of all three mutating residues and the ligand were decoupled in these simulations . The details are provided in SI Section 1i and Fig . S2A in Text S1 . The SRSM approach ( Fig . 1 ) computed ΔΔG between WT and a specific mutant for each ligand . To implement this approach we constructed specific reference states for each mutant and ligand in the bound and unbound state . Only a single amino acid was decoupled in these simulations . The details are available in SI Section 1j and Fig . S2B in Text S1 . The MM-PBSA/GBSA method [14] , as implemented in Amber10 , was used to obtain additional estimates of the changes in binding free energy based on SRSM trajectories . Additional details are provided in SI Section 1k in Text S1 . RosettaInterface [32] uses computational mutagenesis to predict the change in binding free energy of a protein-protein interaction associated with point mutations . Details on the implementation of RosettaInterface for protein-ligand interactions are provided in SI Section 1l in Text S1 .
|
The capacity of the influenza virus to rapidly mutate and render resistance to a handful of FDA approved neuraminidase ( NA ) inhibitors represents a significant human health concern . To gain an atomic-level understanding of the mechanisms behind drug resistance , we applied a novel computational approach to characterize resistant NA mutations . These results are comparable in accuracy and precision with the best experimental measurements presently available . To the best of our knowledge , this is the first time that a rigorous computational method has attained the level of certainty needed to predict subtle changes in binding free energies conferred by mutations . Analysis of our simulation data provided a thorough description of the thermodynamics of the binding process for different NA-inhibitor complexes , with findings that in some cases challenge current views based on interpretations of the crystallographic data . While we did not find a generalized mechanism of NA resistance , we identified key differences between oseltamivir and zanamivir that discriminate their responses to the three mutations we considered , namely H274Y , N294S and Y252H . It is worth noting that our approach can be broadly applied to predict resistant mutations to existing and newly developed drugs in other important drug targets .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"computational",
"chemistry",
"molecular",
"dynamics",
"biophysic",
"al",
"simulations",
"chemistry",
"biology",
"computational",
"biology"
] |
2012
|
Quantitative Predictions of Binding Free Energy Changes in Drug-Resistant Influenza Neuraminidase
|
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